JP2000311012A - Process data evaluation method and apparatus, static characteristic data extraction method and apparatus, and storage medium storing program - Google Patents

Abstract

(57) [Summary] (With correction) [Problem] To significantly improve process data evaluation processing efficiency. At least one parameter is designated from a plurality of continuous process data for each parameter representing a state of the plant collected from the plant, and a plurality of process data of the designated parameter are converted into two continuous process data. The process of dividing the process data into a plurality of groups each including the plant data described above, and the divided process data are statistically processed for each group to obtain the maximum data value and the minimum data value for each group. The process of obtaining the difference between the minimum data values for each group, and the difference between the maximum / minimum data value for each group obtained by this process and a threshold arbitrarily set in advance are compared for each group. By this, it is determined whether or not two or more process data constituting each group is in a steady state for each group. That process and a has a rating extraction processor 6 for each.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention evaluates process data representing the state of a plant such as a nuclear power plant and converts the process data of a plant in a steady state (process data during plant settling; hereinafter referred to as static characteristic data). The present invention relates to a method and apparatus for evaluating plant data to be extracted, a method and apparatus for extracting static characteristic data, and a storage medium storing a program.

[0002]

2. Description of the Related Art In order to perform performance evaluation and soundness check of a plant body such as a nuclear power plant and associated equipment, etc., process data representing a state quantity (process quantity) of the plant during settling (steady state). (Static characteristic data) must be collected and evaluated.

Therefore, in the early days, many people stuck to the plant and confirmed that the plant was in a condition for data collection (steady state), and then manually collected process data. In recent years, most of necessary data can be automatically collected by maintenance of a process computer or the like.

[0004]

Conventionally, in the case of automatically collecting process data, a method of mechanically collecting process data at an arbitrary predetermined time interval has been generally adopted.

However, in the above-described method of automatically collecting process data, process data is collected regardless of the state of the plant.

Therefore, depending on the timing of collecting process data, the process amount of a predetermined parameter in each measurement item (parameter) corresponding to each facility equipment of the plant fluctuates, and a process that is inappropriate as static characteristic data. It frequently occurs that data is collected or a large amount of similar process data is collected when the plant is in a substantially same state (steady state) for a long time.

As a conventional measure for solving the above-mentioned problem relating to the process data collection, the collected process data is evaluated manually (manually), and the process data for each parameter fluctuates according to the context. Find the process data at the location (unsteady state).

Then, the process data (static characteristic data) collected from the plant in the steady state is extracted by thinning out the found process data in the unsteady state from the collected process data.

Further, in an area where the steady state process data is continuous, even if all the steady state process data is extracted, an inconvenience such as a reduction in the capacity of a memory for storing the process data occurs. Only one of the process data in the steady state is extracted as a representative, and process data other than the representative data is thinned out.

That is, in the conventional method for automatically collecting process data, after collecting the process data, the collected process data is evaluated based on the manual processing of a skilled person who understands the behavior of the plant, and the plant performance is evaluated. Since process data (static characteristic data) necessary for evaluation and soundness check is extracted by a manual process of a skilled person, the burden on a skilled person performing the manual evaluation process and the extracting process is increased.

In addition, the manual evaluation process and the manual extraction process take a long time, and the process data evaluation process efficiency and the static characteristic data extraction process efficiency are deteriorated.

The present invention has been made in view of the above-mentioned circumstances, and automatically evaluates process data collected from a plant and automatically generates only static characteristic data necessary for plant performance evaluation and soundness confirmation. It is possible to extract and thin out other process data, eliminate the burden on experts for process data evaluation and static characteristic data extraction processing, and greatly improve process data evaluation processing efficiency and static characteristic data extraction processing efficiency It is an object of the present invention to provide a process data evaluation method and device, and a static characteristic data extraction method and device capable of performing the process.

[0013]

According to the first aspect of the present invention, at least one parameter is obtained from a plurality of continuous process data for each parameter representing the state of the plant collected from the plant. And specify
A plant data evaluation method for evaluating a plurality of continuous process data of a specified parameter and determining the stationarity thereof, wherein the plurality of process data of the specified parameter is converted into two or more continuous plant data, respectively. Dividing the plurality of process data of each group into statistical data for each group to obtain a maximum data value and a minimum data value for each group, respectively. Calculating a difference between the maximum data value and the minimum data value for each group obtained by this step and a threshold value arbitrarily set in advance for each group. Whether or not two or more process data constituting each group is in a steady state by comparing each The and a step of determining each for each group.

According to the second aspect of the present invention, at least one parameter is designated and designated from a plurality of continuous process data for each parameter representing the state of the plant collected from the plant. A process data evaluation method for evaluating a plurality of continuous process data of a parameter and determining its stationarity, wherein a statistical process is performed on the plurality of process data of the specified parameter to obtain a standard deviation of the specified parameter. And comparing the standard deviation obtained in this step with a threshold value arbitrarily set in advance, and determining whether or not the plurality of process data is in a steady state based on the comparison result. Have.

According to the third aspect of the present invention, at least one parameter is designated and designated from a plurality of continuous process data for each parameter representing the state of the plant collected from the plant. A process data evaluation method for evaluating a plurality of continuous process data of parameters and determining the stationarity thereof, wherein the plurality of process data of the designated parameter includes two or more continuous plant data, respectively. A step of dividing into two or more groups continuous in series, a step of statistically processing two or more process data of each divided group for each group to obtain a moving average value for each group, The moving average of the group and the moving average of the group that was statistically processed immediately before in time series for each group Determining whether or not the plant data of each group is in the same steady state as the plant data of the group that has been statistically processed immediately before in time series by comparing the reference data set with the value as the center. Have.

According to the fourth aspect of the present invention, at least one parameter is designated and designated from a plurality of continuous process data for each parameter representing the state of the plant collected from the plant. A process data evaluation method for evaluating a plurality of continuous process data of parameters and determining the stationarity thereof, wherein the plurality of process data of the designated parameter includes two or more continuous plant data, respectively. Dividing the group into a plurality of groups that are continuous in series; and performing statistical processing on each of the two or more process data of each of the divided groups for each group to obtain a maximum data value of the two or more process data of each of the divided groups. Determining the minimum data value, and determining the maximum and minimum data values for each group. The data value is compared with the maximum data value and the minimum data value of the group that has been statistically processed immediately before in each group in time series. Judging whether or not it is in the same steady state as the plant data.

According to the fifth aspect of the present invention, at least one parameter is designated and specified from a plurality of continuous process data for each parameter representing the state of the plant collected from the plant. A static characteristic data extraction method for extracting static characteristic data from a plurality of continuous process data of a parameter, wherein the plurality of process data of the specified parameter are skipped by an arbitrarily set skip width and set. 2. The step of selecting the process data closest to the selected skip width, and the step of determining and determining the maximum data value and the minimum data value according to claim 1, wherein whether each of the selected process data is in a steady state is determined, or Performing the step of determining the standard deviation and the determining step according to claim 2. Comprises a step of determining, a step of extracting the plant data is determined to be a steady state as static characteristic data by.

According to the sixth aspect of the present invention, at least one parameter is designated and designated from a plurality of continuous process data for each parameter representing the state of the plant collected from the plant. A static characteristic data extraction method for extracting static characteristic data from a plurality of continuous process data of a parameter, wherein the plurality of process data of the specified parameter are skipped by an arbitrarily set skip width and set. A step of selecting process data having process values exceeding the skip width, and a step of determining maximum and minimum data values and determining whether each of the selected process data is in a steady state, Alternatively, the step of determining the standard deviation and the determination step according to claim 2 are executed. Comprises a step of determining, a step of extracting the plant data is determined to be a steady state as static characteristic data by.

According to the seventh aspect of the present invention, at least one parameter is designated and specified from a plurality of continuous process data for each parameter representing the state of the plant collected from the plant. A plant data evaluation device that evaluates a plurality of continuous process data of parameters and determines the stationarity thereof, wherein the plurality of process data of the designated parameter includes a plurality of continuous plant data. Means for dividing into two or more groups, and statistically processing two or more process data of each divided group for each group to obtain a maximum data value and a minimum data value for each group, respectively. Means for determining the difference in data values for each group, and the maximum value for each group determined by this means. By comparing the difference between the data value and the minimum data value with a threshold value arbitrarily set in advance for each group, it is determined whether or not two or more process data constituting each group is in a steady state. Means for determining each group.

According to the eighth aspect of the present invention, at least one parameter is designated from a plurality of continuous process data for each parameter representing the state of the plant collected from the plant, and the designated parameter is designated. A static characteristic data extraction device for extracting static characteristic data from a plurality of continuous process data of a parameter, wherein the plurality of process data of the designated parameter are skipped by an arbitrarily set skip width, and are set. A step of determining the maximum data value and the minimum data value according to claim 1, wherein the means for selecting the process data closest to the skip width, respectively, determines whether each of the selected process data is in a steady state, or Performing the step of determining the standard deviation and the determining step according to claim 2. Comprises means for determining, and means for extracting the plant data is determined to be a steady state as static characteristics data with.

[0021]

Embodiments of the present invention will be described below with reference to the accompanying drawings.

(First Embodiment) FIG. 1 is a block diagram showing a schematic configuration of a static characteristic data extracting apparatus according to a first embodiment of the present invention.

According to FIG. 1, a static characteristic data extracting device 1
Indicates a plurality of measurement items (parameters) corresponding to a plurality of equipment and the like from a plant 2 such as a nuclear power plant.
A data server 3 that automatically collects a plurality of continuous process data and accumulates the data for each parameter; A memory 5 having an internal storage device (ROM / RAM) for holding processing programs and temporarily storing processing data and an external storage device (such as a magnetic disk) for storing static characteristic data, and a processing program stored in the memory 5 Reads a plurality of process data of the parameters specified by the parameter specifying unit 4 from the data server 3, evaluates the read plurality of process data, and extracts static characteristic data necessary for performance evaluation and soundness check of the plant 2. Evaluation / extraction processor (hereinafter simply referred to as processor) It includes a 6, and a display unit 7 for displaying the extracted static characteristic data.

Next, the static characteristic data extracting device 1 of the present embodiment
Will be described focusing on the processing of the processor 6, in particular.

For example, in the process of starting and stopping the plant 2, plant data representing the state of the plant 2 is continuously collected for each parameter and stored in the data server 3.

At this time, the operator for the evaluation extraction process manually operates the parameter specifying unit 4 to specify, for example, a specific parameter.

The processor 6 performs the processing shown in FIG. 2 according to the processing program stored in the memory 5 according to the parameter specified by the parameter specifying section 4.

That is, the processor 6 reads from the data server 3 a plurality of time-series continuous process data corresponding to the designated parameter (step S).
1), each of the process data and the N pieces of data before and after the process data (N is arbitrarily specified via the parameter specifying unit 4; for example, in this embodiment, 2
) (Step S2).

There are two ways of grouping in the process of step S2.

For example, assuming that a plurality of process data of designated parameters is time-series data D having 12 points, as shown in FIG. 3, if the method is a method of grouping by dividing points (3 points) to be compared. , GA1 to GA3, and the data is thinned out. On the other hand, as shown in FIG. 3, if the data is sequentially grouped in time series for each data, there are seven groups of GB1 to GB7. In this embodiment, it is assumed that the latter grouping is performed.

Next, the processor 6 sets the minimum data value (minimum value) of the plurality of process data of each divided group.
Min and the maximum data value (maximum value) Max are obtained (step S3).

Then, the processor 6 determines the difference between the obtained minimum value Min and maximum value Max for each group (absolute value of difference, hereinafter, in this specification, “difference” is “absolute value of difference”).
Is obtained for each group (step S
4). The difference between the maximum value Max and the minimum value Min is referred to as a fluctuation width f (f = | Max−Min |).

For example, if the plurality of process data of the designated parameter is the time-series data DA having a score of 7, as shown in FIG. 4, the maximum value Max, the minimum value Min, and the variation range f
Are required respectively.

Subsequently, the processor 6 compares the obtained variation width f for each group with a threshold value S arbitrarily set in advance and held in the memory 5. It is determined whether or not there is (step S5).

Here, as shown in FIG. 5, a plurality of process data of the designated parameter is a time-series data DB having a score of 8.
(D1 to D8), the data group (data D1
, D3, data D2 to D4,..., Data D6 to D8), the variation width f is compared with the threshold value S, and only the smallest variation width f ′ (data D5 to D7) is equal to or smaller than the threshold value S. In other words, when f ′ ≦ S,
The result of the determination in step S5 is YES, and the processor 6 sets the process data D5 to D7 corresponding to the variation range f '.
Is determined to be in a steady state, that is, static characteristic data (step S6), and the process data D5 to D7 in the steady state are extracted and stored in the memory 5 (step S7), and the process ends.

On the other hand, the fluctuation widths f (data D1 to D3,..., Data D4 to D) other than the fluctuation width f '(data D5 to D7)
6, if the data D6 to D8) are larger than the threshold value S,
That is, if f> S, the result of the determination in step S5 is NO, and the processor 6 sets the process data D1 to D3,.
D6, the process data D6 to D8 are determined to be in an excessive state,
The process data in the excessive state is thinned out without being stored in the memory 5, and the process is terminated (step S8).

As a result, only the steady state process data (static characteristic data) from the plurality of process data of the designated parameters is automatically stored in the memory 5, and the unsteady state process data is thinned out.

As described above, according to the static characteristic data extracting device 1 of the present embodiment, a plurality of process data for each parameter collected from the plant 2 is automatically converted according to the fluctuation range of continuous data. By performing the evaluation, only the static characteristic data can be automatically extracted and stored in the memory 5.

Therefore, the time required for the process data evaluation / static characteristic data extraction processing can be greatly reduced by eliminating the process data evaluation / static characteristic data extraction processing by an expert, and the process data evaluation / static characteristic data extraction processing can be labor-saving. This can be realized respectively, and the process data evaluation processing efficiency and the static characteristic data extraction processing efficiency can be improved.

(Second Embodiment) In the static characteristic data extracting apparatus 1A of the present embodiment, the processing program of the processor 6A stored in the memory 5A and the processing of the processor 6A according to this processing program are described in FIG. Therefore, the description of the configuration of the static characteristic data extraction device 1A is omitted, and the description will focus on the processing of the processor 6A.

The processor 6A reads a plurality of chronologically continuous process data corresponding to the designated parameter from the data server 3 as in step S1 (FIG. 6; step S10). The process data is statistically processed to obtain a moving average value (step S11), and a standard deviation of the obtained moving average value is obtained (step S12).

For example, a plurality of process data of designated parameters is time-series process data DC (D1 to D
Assuming that 7), as shown in the upper part of FIG. 7, the moving average value of the process data DC is obtained as the process data D4, and the standard deviation corresponding to the process data D4 is obtained as shown in the lower part of FIG.

Then, the processor 6A compares the standard deviation corresponding to the obtained moving average value with a threshold S1 arbitrarily set in advance and held in the memory 5, and the standard deviation is equal to or smaller than the threshold S1. (Step S1)
3).

Now, as shown in FIG. 7, since the standard deviation corresponding to the moving average value is equal to or smaller than the threshold value S1, the standard deviation ≦
S1, that is, the result of the determination in step S13 is YES
And the processor 6A outputs the corresponding process data D
1 to D7 are determined to be in the steady state, that is, static characteristic data (step S14), and the process data D1 in the steady state is determined.
To D7 are extracted and stored in the memory 5A (step S1).
5), end the processing.

On the other hand, if the standard deviation is larger than the threshold value S1, that is, if the standard deviation> S1, the result of the determination in step S13 is NO, and the processor 6
A determines that the corresponding process data D1 to D7 is in an excessive state, and stores the excessive process data in the memory 5A.
And the process ends (step S1).
6).

As a result, as in the first embodiment, only the process data in the steady state (static characteristic data) is automatically stored in the memory 5A from among the plurality of process data of the designated parameters. Process data is thinned out.

As described above, also in the static characteristic data extracting apparatus 1A of this embodiment, a plurality of process data for each parameter collected from the plant 2 are automatically evaluated according to the moving average standard deviation. By doing so, only the static characteristic data can be automatically extracted and stored in the memory 5A, so that the time required for the process data evaluation / static characteristic data extraction processing can be significantly reduced, and the process data evaluation / static characteristic data extraction processing can be saved. And the improvement of process data evaluation processing efficiency and static characteristic data extraction processing efficiency.

In the present embodiment, the standard deviation of the moving average is used as the standard deviation of the time-series continuous process data. However, the present invention is not limited to this. It is also possible to use a standard deviation (eg, an intermediate value).

(Third Embodiment) In the static characteristic data extracting apparatus 1B of this embodiment, the processing program of the processor 6B stored in the memory 5B and the processing of the processor 6B according to this processing program are described in FIG. Therefore, the description of the configuration of the static characteristic data extraction device 1B is omitted, and the description will focus on the processing of the processor 6B.

The processor 6B reads a plurality of chronologically continuous process data corresponding to the designated parameter from the data server 3 (FIG. 8; step S20), similarly to step S1, and reads the chronologically continuous process data. The process data is stored in a predetermined number (for example, three or more) of arbitrary groups Gr. 1 to Gr. n (FIG. 3; grouping similar to GB1 to GB7; step S21).

Next, the processor 6B checks each group G
r. 1 to Gr. n process data for each group is continuously statistically processed, and the respective moving average values Av (G
1), Av (G2),..., Av (Gn) are obtained (step S22).

Subsequently, the processor 6B continuously calculates the moving average value Av (G
1) to Av (Gn), based on the moving average value Av (G1) statistically processed earlier in time series, and the reference moving average value Av (G1) and an arbitrarily set threshold value (S ) Is set based on the above (Step S23).

Next, the processor 6B compares the moving average value Av (G2) obtained by performing statistical processing immediately after the reference moving average value Av (G1) in the time series with the judgment reference range Av.
(G1) ± S, and this determination reference range Av (G1)
Within ± S (within the upper and lower limits), the moving average value Av (G
2) is included, that is, “Av (G1) −S
≦ Av (G2) ≦ Av (G1) + S ”is determined (step S24).

Here, as shown in FIG.
v (G2) is a criterion range Av (G1) -S to Av
(G1) + S (that is, “Av (G
2) <Av (G1) -S or Av (G1) + S <
Av (G2) ”), the result of the determination in step S24 is NO, and the processor 6B sets the group Gr. Process data of Group Gr. It is determined that it is not the same steady state as the process data of No. 1 (step S25),
A criterion range Av (G2) ± based on a threshold value (S) based on the moving average value Av (G2) of the group G2.
S is set (step S26), and all groups Gr.
1 to Gr. It is determined whether the steady state determination processing has been completed for n (step S27).

Now, the group Gr. Since the determination process for No. 2 has been completed, the process of step S27 is NO.
And the processor 6B performs the same processing as in step S23,
The determination reference range Av (G2) ± S is set based on the reference moving average value Av (G2) and the threshold value (S) (step S28), and the process proceeds to step S24.
By reading “1 → 2” and “2 → 3”, whether the moving average value Av (G3) immediately after the reference moving average value Av (G2) is within the determination reference range Av (G2) ± S, That is,
“Av (G2) −S ≦ Av (G3) ≦ Av (G2) +
It is determined whether or not “S” is satisfied.

At this time, as shown in FIG. 9, the moving average value Av (G3) is equal to the determination reference range Av (G2) -S to Av.
(G2) + S, the result of the determination in step S24 is YES, and the processor 6B sets the group Gr.
3 is the process data of the group Gr. 2 is determined to be in the same steady state as the process data of the group Gr.
3 is extracted and temporarily stored in the memory 5B (step S29), and all the group Gr. 1-G
r. It is determined whether the steady state determination process has been completed for n (steps S30A and S30B).

If the group Gr. n = Group Gr.
8, the process at step S30 becomes NO, and the process moves to step S24 to move to the next moving average value Av (G
4) Same determination reference range Av (G2) -S to A
The determination processing of step S24 is performed using v (G2) + S.

As shown in FIG. 9, the moving average Av (G
4) to Av (G7) are all the determination reference range Av (G7).
2) Since it is within the range of -S to Av (G2) + S, the processing of step S24 and step S29 is repeated, and the group Gr. 4-Gr. 7 is the process data of the group Gr. It is determined that it is in the same steady state as the process data of No. 2 (step S30A).

On the other hand, as shown in FIG.
(G8) is a criterion range Av (G2) -S to Av (G
2) Since it is out of the range of + S, the result of the determination in step S24 is NO, and the group Gr. Corresponding to the moving average value Av (G8). 8 includes the group Gr.
It is determined that it is not the same steady state as the process data of No. 2 (see step S25).

At this time, all groups Av (G1) to
Since the steady state determination processing for the group Av (G8) has been completed, the determination processing in step S27 is YES, and the processor 6B proceeds to the processing in step S31 shown in FIG.

The processor 6B controls each group Gr. 1 to
Gr. 8 is extracted as process data of the same steady state by the steady state determination processing of No. 8 and temporarily stored in the memory 5B. 2-Gr. 7 (for example, group Gr.2) in the process data of No. 7
Is read from the memory 5B (step S31).

Now, as shown in FIG. 11, the read group Gr. 2 is the same steady state process data as D (t
1) to D (tk = t8), the processor 6B
The read group Gr. 2, the process data D (t1 + Δt = t5) after a time interval Δt arbitrarily set in advance from the first process data D (t1) of the process data D (t1) to D (t8) arranged in time series. The same group Gr. Is extracted and stored in the memory 5B. 2 other process data in the same steady state (process data D (t1) to (excluding process data D (t1 + Δt))
D (tk)) is deleted from the memory 5B and thinned out (step S32). If there is no process data matching D (t1 + Δt), the closest process data is extracted.

Then, the processor 6B determines that all the groups Gr. 2-Gr. It is determined whether or not the process data extraction / thinning-out processing has been completed for No. 7 (step S33).

Now, the group Gr. Since the process data extraction / thinning-out process for the group Gr.2 has been completed, the process of step S33 becomes NO, and the processor 6B sends another group Gr. 3-Gr. 7 is repeatedly performed on the process data of each group Gr. 2-Gr. Extract only one process data (static characteristic data) every 7
Thin out other process data.

Then, all the groups G in the same steady state
r. 2-Gr. When the process data extraction / thinning-out processing ends for step 7, the determination in step S33 becomes YES, and processor 6B ends the processing.

As a result, in the memory 5B, in addition to extracting only the process data in the steady state (static characteristic data) from the plurality of process data of the designated parameter and thinning out the process data in the unsteady state, When a plurality of process data in the steady state is continuous in time series, only one process data is extracted from the plurality of process data in the same steady state, and the other process data in the same steady state is thinned out. be able to.

Therefore, in the static characteristic data extracting apparatus 1B of this embodiment, a process based on automatically extracting only static characteristic data, which is process data in a steady state, from a plurality of process data collected from the plant 2. In addition to significantly reducing the time required for data evaluation and static characteristic data extraction processing, saving labor in process data evaluation and static characteristic data extraction processing, and improving process data evaluation processing efficiency and static characteristic data extraction processing efficiency, multiple Since a large amount of static characteristic data is not stored in the memory 5B, a large free space in the memory 5B can be secured.

In the present embodiment, the first process data D (t
The process data D (t1 + Δt) after a time interval Δt arbitrarily set in advance is extracted from 1) and stored in the memory 5B to thin out other process data, but the present invention is not limited to this. .

For example, as shown in FIG. 12, the read process data of the same steady state of the read group is represented by D (t1).
To D (tk = t9), the process data D
It is also possible to extract the process data D (t5) located in the middle in time series from (t1) to D (t9) and store it in the memory 5B to thin out other process data.
In the extraction / thinning process, when there is a plurality of process data located in the middle in a time series (when there are a plurality of process data in a group), either one of the plurality of process data or an average The value is extracted as representative process data.

An average value can be used as another example of the process data extraction / thinning process.

That is, as shown in FIG. 13, the processor 6B replaces the step S32 of FIG. 10 with the read group Gr. 2 process data D (t1) to
D (t8) is statistically processed and its average value [｛D (t1) +
D (t2) +... + D (t7) + Dt8} / 8]
The average value is referred to as Group Gr. 2 process data D (t
1) to D (t8) are stored in the memory 5B as representative values (representative process data). The second process data D (t1) to D (t8) can be deleted from the memory 5B and thinned out (see step S32A).

(Fourth Embodiment) In the static characteristic data extracting apparatus 1C of this embodiment, the processing program for the processor 6C stored in the memory 5C and the processing of the processor 6C according to this processing program are described in FIG. Therefore, the description of the configuration of the static characteristic data extraction device 1C is omitted, and the description will focus on the processing of the processor 6C.

The processor 6C reads a plurality of process data corresponding to the designated parameter from the data server 3 as in steps S1 and S20 (FIG. 1).
4; Step S40), as in Step S21, read the process data into a specified number of arbitrary groups G
r. 1 to Gr. n (step S4)
1).

Next, the processor 6C checks each group G
r. 1 to Gr. n of the process data is continuously statistically processed for each group, and the maximum data value and the minimum data value Max (G1), Min (G1), and Ma are obtained.
x (G2) and Min (G2),..., Max (Gn)
And Min (Gn) are obtained (step S42).

Subsequently, the processor 6C continuously calculates the maximum data value and the minimum data value Max (G1) and Min (G1) to Ma of each group obtained by statistical processing.
The maximum data value and the minimum data value Max that have been statistically processed earlier in time series in x (Gn) and Min (Gn)
(G1) and Min (G1) are set as reference values (step S43).

Next, the processor 6C outputs the maximum / minimum data value Ma obtained by performing statistical processing immediately after the reference maximum / minimum data value Max (G1) / Min (G1) in time series.
x (G2) · Min (G2) is compared with the reference maximum / minimum data value Max (G1) · Min (G1), and the data range between the maximum data value Max (G2) and the minimum data value Min (G2) is used as a reference. Whether it is out of the data range between the maximum data value Max (G1) and the reference minimum data value Min (G1), that is, “Max (G2) <Min
(G1) It is determined whether orMax (G1) <Min (G2) is satisfied (step S44).

Here, the group Gr. 1 to Gr. As shown in FIG. 15, Min (G2) is within the range of the reference range Max (G1) to Min (G1) (that is, "Min (G1) <Min (G2) or Max", as shown in FIG.
(G2) <Max (G1) ”), the result of the determination in step S44 is NO, and the processor 6B sets the group Gr. Process data of Group Gr. 1 is determined to be in the same steady state as the process data of the group Gr. 2 is extracted and temporarily stored in the memory 5C (step S45), and all the group Gr.
1 to Gr. It is determined whether or not the steady state determination processing has been completed for n (steps S46A and S46B).

Now, the group Gr. n = Group Gr. 5
Then, the process of step S46B becomes NO, and the process shifts to the process of step S44, where the next maximum data value Max is set.
(G3) and the minimum data value Min (G3) with the same criterion (maximum data value Max (G1) and Mi
The determination processing in step S44 is performed using n (G1).

As shown in FIG. 15, the maximum data value Max
(G3) and minimum data value Min (G3) to maximum data value Max (G4) and minimum data value Min (G4)
Is within the range of the reference range Max (G1) to Min (G1), the processing of steps S45 to S46 is repeated, and the group Gr. 3-Gr. 4 is group Gr. It is determined that it is in the same steady state as the first process data (step S46A).

On the other hand, as shown in FIG. 15, the maximum data value Max (G5) and the minimum data value Min (G5) are
Since all of them are out of the reference range Max (G1) to Min (G1), the result of the determination in step S44 is YES, and the processor 6C sets the maximum data value Max (G5).
And the group Gr. Corresponding to the minimum data value Min (G5). 5 includes the group Gr. It is determined that it is not the same steady state as the first process data (step S47).

Then, the processor 6C determines that all the groups Gr. 1 to Gr. n (= Gr.5) is determined as to whether or not the steady state determination processing has been completed (step S48).
B).

At this time, the group Gr. If 5 is not the last group, the determination in step S48B is NO, and the processor 6C uses the determined maximum data value Max (G5) and minimum data value Min (G5) as a reference (step S43), as in step S43. S49), the maximum data value Max (G6) and the minimum data value Min (G6) immediately after that are in the reference range Max (G1) to Min (G6).
It is determined whether or not it is out of 1).

In this embodiment, the group Gr. 5 is the last group, the determination in step S48A is YE
S, and the processor 6B proceeds to step S in FIG.
13 to step S33 (or steps S31, S32A and S3 in FIG. 13 described above).
By performing the process data extraction / thinning-out process of 3), each group Gr. 1 to
Gr. And extracting only one representative process data for each group from the plurality of process data of
Other process data of the same steady state can be thinned out.

As a result, as in the fourth embodiment, the processing data evaluation and static characteristic data extraction processing time can be significantly reduced.
Labor saving process data evaluation and static characteristic data extraction processing
In addition to improving the process data evaluation processing efficiency and the static characteristic data extraction processing efficiency, the free space of the memory 5C can be increased.

(Fifth Embodiment) In the static characteristic data extracting apparatus 1D of the present embodiment, the processing program for the processor 6D stored in the memory 5D and the processing of the processor 6D according to this processing program are described in FIG. Therefore, the description of the configuration of the static characteristic data extraction device 1D will be omitted, and the description will focus on the processing of the processor 6D. Further, in the present embodiment, for example, the plant 2
Is assumed to be continuously collected for each parameter and stored in the data server 3.

At this time, the processor 6D determines in step S
As in 1, the process data corresponding to the designated parameter is read from the data server 3 (FIG. 16; step S50).

Then, the processor 6D uses, for example, the fact that the process value of the designated parameter in the plant start-up process tends to increase with an increase in the output. An upper limit value (with process data) arbitrarily set from a lower limit value (with process data) (a) (or a process data having a process value close to the specified lower limit value) arbitrarily set via the unit 4 In (d),
Using an arbitrarily set step width (b), a target value ｛c (x) = a + bx, c (x) ≦ d, x = 0, 1,
2,..., N}, and the first set value c (x) = c
The process data closest to (0) = a is searched for and extracted (step S51).

Next, the processor 6D determines whether the extracted process data is an evaluation extraction process based on the variation range described in the first embodiment (see steps S3 to S5, except that the process data before and after the extracted process data is not included). To form a group), or execute an evaluation extraction process (see steps S11 to S13) based on the standard deviation described in the second embodiment to determine whether the extracted process data is in a steady state. (Step S
52).

At this time, the result of the determination in step S52 is N
O, that is, if it is determined that the extracted process data is not in the steady state, the processor 6D executes c (x)
= C (0) = a is extracted next to the process data (step S53), and the process returns to the determination process of step S52 to determine whether or not the vehicle is in a steady state.

The processing of steps S52 to S53 is repeated, and when it is determined that the extracted process data is in the steady state (step S52 → YES), the processor 6D extracts the determined process data and stores it in the memory 5D. (Step S54), and it is determined whether or not the above-described process data evaluation / extraction processing has been completed for all set values between the lower limit value (a) and the upper limit value (d) (step S55).

Since the evaluation / extraction process for the process data corresponding to the lower limit a has been completed, the determination in step S55 is NO, and the processor 6D
Retrieves and extracts the process data closest to the next set value (c (x) = c (1) = a + b (step S5).
6) The determination processing in step S52 is performed on the process data close to the extracted set value.

For example, as shown in FIG. 17, process data c (0) = a to c (1) = a +
Process data within the range b is skipped and c (1)
The process data DJ closest to = a + b is not extracted because it is not in a steady state and is skipped.

The process data DK next to c (1) = a + b is extracted because it is in the steady state.
Stored in the memory 5D.

Hereinafter, the process data corresponding to the set values c (2), c (3),..., C (n) based on x = 2, 3,.
By executing the processing of No. 4, each set value c (0) to c (0) to c
One steady state process data corresponding to (n) can be extracted and stored in the memory 5D.

When the steady state determination processing for the process data corresponding to the set value c (n) of x = n is completed, the processor 6D determines YES in step S55.
And the process ends.

As described above, according to the present embodiment,
From the process data continuously collected in time series, only the minimum steady-state process data necessary for the static characteristic evaluation of the plant 2 is extracted, and the other transient process data and the same steady-state process data are extracted. Since the process data can be thinned out, as in the third and fourth embodiments,
Dramatically shorten the process data evaluation / static characteristic data extraction processing time, save labor in process data evaluation / static characteristic data extraction processing, improve process data evaluation processing efficiency and static characteristic data extraction processing efficiency, and increase free space in the memory 5D. Can be realized respectively.

(Sixth Embodiment) In the static characteristic data extracting apparatus 1E of this embodiment, the processing program for the processor 6E stored in the memory 5E and the processing of the processor 6E according to this processing program are described in FIG. Therefore, the description of the configuration of the static characteristic data extraction device 1E will be omitted, and the description will focus on the processing of the processor 6E. Further, in the present embodiment, for example, the plant 2
Is assumed to be continuously collected for each parameter and stored in the data server 3.

At this time, the processor 6E determines in step S
As in the case of step 1, a plurality of process data corresponding to the designated parameter is read from the data server 3 (FIG. 18; step S60).

Then, similarly to the fifth embodiment, the processor 6E utilizes the fact that, for example, the process value of the designated parameter in the plant start-up process tends to increase with an increase in output, and uses the plurality of time-series plant data. Out of the above, for example, a process value arbitrarily set via the parameter specifying unit 4 (process data having) is set as an initial point, and the process data having this initial point process value (or the process data closest to the initial point process value) A value プ ロ セ ス c (x) which is a target with an arbitrarily set step width (b) between an arbitrarily set upper limit (with process data) (d) and an arbitrarily set upper limit (with process data) (a). ) =
a + bx, c (x) ≦ d, x = 0, 1, 2,..., n}, and process data larger than the initial set value c (x) = c (0) = a in time series A search is performed, and the first process data is extracted in a time series (step S61).

Next, as in the fifth embodiment, the processor 6E converts the extracted process data into an evaluation and extraction process based on the fluctuation range described in the first embodiment (see steps S3 to S5) or Evaluation extraction processing based on the standard deviation described in the second embodiment (step S11
To S13) to determine whether the extracted process data is in a steady state (step S62).

At this time, the result of the determination in step S62 is N
O, that is, when it is determined that the extracted process data is not in the steady state, the processor 6E extracts the next process data in a time series from the process data determined in step S62 (step S63). Returning to the determination process of S62, it is determined whether or not the vehicle is in a steady state.

The processing of steps S62 to S63 is repeated, and when it is determined that the extracted process data is in a steady state (step S62 → YES), processor 6E extracts the determined process data and stores it in memory 5E. (Step S64), and it is determined whether or not the above-described process data evaluation / extraction process has been completed for all set values between the lower limit value (a) and the upper limit value (d) (step S65).

Since the evaluation / extraction process for the process data corresponding to the lower limit a has been completed, the determination in step S65 is NO, and the processor 6E
Is the following set value (c (x) = c (1) = a + bxx (x
= 1), process data larger than (1) is searched in time series, the first process data is extracted in time series (step S66), and step S66 is performed on the extracted process data.
A determination process of 62 is performed.

For example, as shown in FIG. 19, process data c (0) = a to c (1) = a +
Process data within the range b is skipped and c (1)
= A + b and the first process data DP in time series are not extracted because they are not in a steady state and are skipped.

Then, c (1) is larger than a + b,
The next process data DQ in time series with respect to the process data DP is extracted because it is in a steady state,
E is stored.

The process data corresponding to the set values c (2), c (3),..., C (n) based on x = 2, 3,.
By executing the processing of No. 4, each set value c (0) to c (0) to c
One steady state process data corresponding to (n) can be extracted and stored in the memory 5E.

When the processor 6E completes the steady state determination processing for the process data corresponding to the set value c (n) of x = n, the determination in step S65 is YES.
And the process ends.

As described above, according to the present embodiment,
As in the fifth embodiment, only the minimum steady state process data necessary for the static characteristic evaluation of the plant 2 is extracted from the process data continuously collected in time series.
Since the other process data in the transient state and the process data in the same steady state can be thinned out, the process data evaluation / static characteristic data extraction processing time can be significantly reduced, and the process data evaluation / static characteristic data can be reduced as in the fifth embodiment. Labor saving of the extraction process, improvement of the process data evaluation process efficiency and the static characteristic data extraction process efficiency, and increase of the free space of the memory 5E can be respectively realized.

According to the above-described first to sixth embodiments, one parameter is designated via the parameter designation unit 4 and a plurality of time-series continuous process data corresponding to the designated one parameter is designated. Although the process data in the steady state is extracted from the above, the present invention is not limited to this, and a plurality of designated parameters may be designated.

In this case, the above-described process data evaluation / static characteristic data extraction processing is performed for each of a plurality of time-series continuous process data corresponding to a plurality of designated parameters.

[0111]

As described above, according to the process data evaluation method and apparatus, the static characteristic data extraction method and apparatus, and the storage medium storing the program according to the present invention, the process data corresponding to the designated parameter is manually processed. The process can be performed automatically without using the process, because unnecessary process data can be automatically thinned out by extracting only the static characteristic data without using the process. The time required for the data evaluation / static characteristic data extraction processing can be significantly reduced, and the process data evaluation / static characteristic data extraction processing can be labor-saving. Therefore, the efficiency of the process data evaluation processing and the efficiency of the static characteristic data extraction processing can be improved, and the efficiency of the plant performance evaluation and soundness confirmation processing can be greatly improved.

In particular, according to the present invention, a continuous steady state is found in the process data of the specified specific parameter from the continuous process data for each of the plurality of parameters, and the steady state of the plant is determined. It is possible to detect a portion that continues for a long time. Therefore,
This eliminates the need to manually identify long-term steady-state locations, which has been done manually.
It is possible to contribute to labor saving of static characteristic data extraction processing and improvement of plant performance evaluation / soundness confirmation processing efficiency.

Further, according to the present invention, it is possible to extract the representative value at a place where the plant is in a steady state, and thin out other process data. Therefore, it is possible to omit the process of extracting the representative process data of the place where the steady state is continuous, which has been manually performed in the past, and to save the process data evaluation / static characteristic data extraction process and to evaluate the plant performance and the soundness. This can contribute to improvement in processing efficiency.

Further, according to the present invention, it is possible to extract the minimum process data necessary for the static characteristic evaluation from a plurality of process data continuously collected from the plant. State and transient state identification and process data thinning can be omitted,
This can contribute to labor saving in process data evaluation / static characteristic data extraction processing and improvement in plant performance evaluation / soundness confirmation processing efficiency.

[Brief description of the drawings]

FIG. 1 is a block diagram showing a schematic configuration of a static characteristic data extraction device according to a first embodiment of the present invention.

FIG. 2 is a schematic flowchart illustrating an example of processing of a processor illustrated in FIG. 1;

FIG. 3 is a view for explaining process data grouping processing in FIG. 2;

FIG. 4 is a diagram for explaining a maximum value, a minimum value, and a fluctuation range in the first embodiment.

FIG. 5 is a view for explaining a determination process in step S5 in FIG. 2;

FIG. 6 is a schematic flowchart illustrating an example of processing of a processor according to a second embodiment of the present invention.

FIG. 7 is a view for explaining a process of obtaining a standard deviation in step S12 and a determination process in step S13 in FIG. 6;

FIG. 8 is a schematic flowchart illustrating an example of processing of a processor according to a third embodiment of the present invention.

FIG. 9 is a view for explaining the determination processing in step S24 in FIG. 9;

FIG. 10 is a schematic flowchart illustrating an example of processing of a processor according to a third embodiment.

FIG. 11 is a view for explaining the extraction processing in step S32 in FIG. 10;

FIG. 12 is a view for explaining another example of the extraction processing in step S32.

FIG. 13 is a schematic flowchart showing another example of the processing of the processor in the third embodiment.

FIG. 14 is a schematic flowchart illustrating an example of processing of a processor according to a fourth embodiment of the present invention.

FIG. 15 is a view for explaining the determination processing in step S44 in FIG. 14;

FIG. 16 is a schematic flowchart illustrating an example of processing of a processor according to a fifth embodiment of the present invention.

FIG. 17 is a view for explaining the determination processing in step S52 in FIG. 16;

FIG. 18 is a schematic flowchart illustrating an example of processing of a processor according to a sixth embodiment of the present invention.

FIG. 19 is a view for explaining the determination processing in step S62 in FIG. 18;

[Explanation of symbols]

 DESCRIPTION OF SYMBOLS 1, 1A-1E Static characteristic data extraction device 2 Plant 3 Data server 4 Parameter designation part 5, 5A-5E memory 6, 6A-6E Evaluation extraction processor 7 Display part

Claims (13)

[Claims] At least one parameter is designated from a plurality of continuous process data for each parameter representing a state of the plant collected from a plant, and a plurality of continuous process data of the designated parameter is evaluated to evaluate the parameter. A plant data evaluation method for determining stationarity, wherein a plurality of process data of the specified parameters,
A step of dividing into two or more groups each including two or more continuous plant data; a maximum data value and a minimum data of each group by statistically processing two or more process data of each divided group for each group; Calculating the difference between the maximum data value and the minimum data value obtained for each group; and setting the difference between the maximum data value and the minimum data value for each group obtained in this step arbitrarily in advance. And determining whether or not two or more process data items constituting each group are in a steady state by comparing each of the threshold values with the threshold value for each group. Characteristic process data evaluation method. 2. Designating at least one parameter from a plurality of continuous process data for each parameter representing a state of the plant collected from a plant, evaluating a plurality of continuous process data of the designated parameter, and A process data evaluation method for determining stationarity, comprising the steps of: statistically processing a plurality of process data of the specified parameter to obtain a standard deviation of the specified parameter; And determining whether the plurality of process data is in a steady state based on a result of the comparison. 3. Designating at least one parameter from a plurality of continuous process data for each parameter representing a state of the plant collected from a plant, evaluating a plurality of continuous process data of the designated parameter, and A process data evaluation method for determining stationarity, wherein a plurality of process data of the specified parameter,
A step of dividing into a plurality of time-series continuous groups each including two or more continuous plant data, and a process of statistically processing two or more process data of each divided group for each group, and moving each group; The step of calculating the average value, and comparing the obtained moving average value of each group with the reference range set around the moving average value of the group statistically processed immediately before each group in time series. Determining whether or not the plant data of each group is in the same steady state as the plant data of the group that has been statistically processed immediately before in time series. 4. Designating at least one parameter from a plurality of continuous process data for each parameter representing a state of the plant collected from a plant, evaluating a plurality of continuous process data of the designated parameter, and A process data evaluation method for determining stationarity, wherein a plurality of process data of the specified parameter,
A step of dividing the data into two or more groups in a time series including two or more continuous plant data; and performing a statistical process on the two or more process data of each of the divided groups for each group to obtain a maximum value for each group. A step of calculating a data value and a minimum data value, respectively, and a calculated maximum data value and a minimum data value of each group, and a maximum data value and a minimum data value of a group that has been statistically processed immediately before each group in time series; Determining whether or not the plant data of each group is in the same steady state as the plant data of the group that has been statistically processed immediately before in time series. Evaluation method. 5. Extracting any one process data from two or more process data of each group determined to be in the same steady state as representative process data of each group, thereby obtaining another process data of each group. 5. The process data evaluation method according to claim 3, further comprising the step of: 6. The thinning-out step includes, after a lapse of a predetermined time arbitrarily designated from process data serving as a time-series starting point in two or more process data of each group, representing process data representative of each group. 6. A step of thinning out other process data of each group by extracting the process data as process data.
The described process data evaluation method. 7. A process for thinning out other process data of each group by extracting time-series intermediate process data from two or more process data of each group as representative process data of each group. 6. The process data evaluation method according to claim 5, further comprising the step of: 8. Designating at least one parameter from a plurality of continuous process data for each parameter representing a state of the plant collected from a plant, and converting static characteristic data from a plurality of continuous process data of the designated parameter. A method for extracting static characteristic data to be extracted, wherein a plurality of process data of the specified parameters are skipped by an arbitrarily set skip width, and process data closest to the set skip width is selected. And a step of determining a maximum data value and a minimum data value according to claim 1 or a step of determining and determining a standard deviation according to claim 2, wherein whether each selected process data is in a steady state is determined. The step of determining by performing Static characteristic data extraction method characterized in that the plant data determined to that provided with a step of extracting as a static characteristic data. 9. At least one parameter is designated from a plurality of continuous process data for each parameter representing the state of the plant collected from the plant, and static characteristic data is obtained from the plurality of continuous process data of the designated parameter. A static characteristic data extraction method for extracting, wherein a plurality of process data of the specified parameter are skipped at an arbitrarily set skip width, and process data having a process value exceeding the set skip width is obtained. The step of determining the maximum and minimum data values according to claim 1 or the step of determining and determining the standard deviation according to claim 2, wherein each of the selecting step and each of the selected process data is in a steady state. Steps to determine by performing the steps Static characteristic data extraction method characterized in that the plant data determined to that provided with a step of extracting as a static characteristic data. 10. Designating at least one parameter from a plurality of continuous process data for each parameter representing a state of the plant collected from a plant, evaluating a plurality of continuous process data of the designated parameter, and A plant data evaluation device for determining stationarity, wherein a plurality of process data of the specified parameter,
Means for dividing into a plurality of groups each including two or more continuous plant data;
Means for statistically processing more than one process data for each group to determine a maximum data value and a minimum data value for each group, and a difference between the determined maximum data value and minimum data value for each group; Two or more processes constituting each group by comparing the difference between the maximum data value and the minimum data value for each group obtained by the means with a threshold value arbitrarily set in advance for each group. Means for judging whether data is in a steady state or not for each group. 11. Designating at least one parameter from a plurality of continuous process data for each parameter representing a state of the plant collected from a plant, and converting static characteristic data from a plurality of continuous process data of the designated parameter. A static characteristic data extraction device for extracting a plurality of pieces of process data of the specified parameter with an arbitrarily set skip width, and respectively selecting process data closest to the set skip width. 2. The step of determining a maximum data value and a minimum data value according to claim 1, and determining whether each of the selected process data is in a steady state.
Or means for determining by executing the step of determining the standard deviation and the determining step according to claim 2;
Means for extracting plant data determined to be in a steady state as static characteristic data. 12. Designating at least one parameter from a plurality of continuous process data for each parameter representing a state of the plant collected from a plant, evaluating a plurality of continuous process data of the designated parameter, and In a storage medium storing a computer-readable program for determining stationarity, a plurality of process data of the specified parameters,
A procedure in which the computer divides the data into a plurality of groups each including two or more continuous plant data; and a step in which the computer subjects the two or more process data of each divided group to statistical processing for each group by the computer. A maximum data value and a minimum data value for each group, and a difference between the maximum data value and the minimum data value obtained for each group; and a maximum value for each group obtained by executing this procedure on a computer. By comparing the difference between the data value and the minimum data value with a threshold value arbitrarily set in advance for each group by the computer, two or more process data constituting each group are in a steady state. A procedure for determining whether or not each of the groups is included in each group. Media. 13. Designating at least one parameter from a plurality of continuous process data for each parameter representing a state of the plant collected from a plant, and converting static characteristic data from a plurality of continuous process data of the designated parameter. In a storage medium storing a computer-readable program for extracting, a plurality of process data of the specified parameter,
The computer skips the skip width arbitrarily set, the procedure for selecting the process data closest to the set skip width, and whether each selected process data is in a steady state,
A step of making the computer execute the steps of determining and determining the maximum data value and the minimum data value according to claim 1 or the steps of determining and determining the standard deviation according to claim 2, and a steady state. Extracting plant data determined as static data by the computer as static characteristic data.