CN102182671A - State analysis monitoring system and method of gas compressor - Google Patents

Abstract

The invention discloses a state analysis and monitoring system of a gas compressor, which comprises a gas pressure sensor, a temperature sensor, a signal conditioner, an engine oil pressure sensor, a vibration sensor, a vibration measuring instrument, a data acquisition card, a display and a computer. The invention also discloses a state analysis and monitoring method of a gas compressor. The gas monitoring is input into a computer, and the following processing is performed: the number is compared with the upper limit and lower limit of a preset safety value, and an alarm is issued if the upper limit or lower limit is exceeded. , and start the fault diagnosis module at the same time; by querying the gas compressor state parameter abnormality and fault correspondence table in the fault diagnosis module, the corresponding abnormal component type can be determined; query the historical maintenance records of various components in the database in the abnormal component type; calculate the status The life values of different components corresponding to abnormal parameters; check in order from the component with the smallest life value to the component with the largest life value, until the faulty part is found, and the state analysis and monitoring is completed.

Description

A state analysis and monitoring system and method for a gas compressor

technical field

The invention relates to the field of monitoring and analysis of electromechanical equipment, and in particular provides a state analysis and monitoring system and method of a gas compressor.

Background technique

Gas compressors play an important role in chemical production processes involving the need for pressurization of gaseous media. Monitoring the working condition of the compressor helps to detect potential failures and predict life in advance, so that measures can be taken as early as possible to reduce or avoid production losses.

At present, most of the monitoring systems used in terms of safety, reliability, availability and economic benefits of the gas compressor system are industrial computers. Using reliability analysis techniques. Some monitoring systems fail to make full use of status monitoring signals even if they use system reliability analysis technology, resulting in failure to monitor equipment well, analyze its operating status in time, and predict its service life.

Contents of the invention

Purpose of the invention: the technical problem to be solved by the present invention is to provide a state analysis and monitoring system and method of a gas compressor for the deficiencies of the prior art, by analyzing and processing the signal of the gas compressor, and using reliability technology to improve System security, reliability and economic benefits.

In order to solve the above technical problems, the present invention discloses a state analysis and monitoring system for a gas compressor, which includes a gas pressure sensor (1), a temperature sensor (2), a signal conditioner (3), an engine oil pressure sensor (4), a vibration Sensor (5), vibrometer (6), data acquisition card (7), display (8), computer (9);

The gas pressure sensor (1), the temperature sensor (2) and the oil pressure sensor (4) are respectively connected to the signal conditioner (3); the vibration sensor (5) is connected to the vibrometer (6) Connected, the vibrometer (6) and the signal conditioner (3) are connected with the data acquisition card (7) respectively; the data acquisition card (7) is connected with the computer (9), and the computer ( 9) Connect with the display (8).

In the present invention, the signal conditioner (3) is used to amplify the signals from the gas pressure sensor (1), the temperature sensor (2) and the engine oil pressure sensor (4), and transmit them to the data acquisition card (7) .

In the present invention, the data acquisition card (7) is based on the PXI (PCI extensions for Instrumentation, PCI extension for instrumentation system) bus technology, and is used to convert the collected signal into an electrical signal and transmit it to the computer.

In the present invention, the vibrometer (6) is used to amplify the vibration signal and transmit it to the data acquisition card (7).

Among the present invention, described computer (9) is equipped with LabVIEW platform software.

The invention also discloses a state analysis and monitoring method of a gas compressor, which inputs gas pressure signals, temperature signals, oil pressure signals, and vibration signals into a computer, and processes the above signals according to the following steps:

Step (1) comparing the gas pressure signal, temperature signal, engine oil pressure signal, and vibration signal with the upper limit and lower limit of the preset safety value, if the upper limit or lower limit is exceeded, an alarm is given, and the fault diagnosis module is started simultaneously;

Step (2) determines the type of corresponding abnormal parts by querying the gas compressor state parameter abnormality and fault correspondence table in the fault diagnosis module;

Gas compressor state parameter abnormality and fault correspondence table

Step (3) Query the historical maintenance records of various components in the database in the category of abnormal components. The historical maintenance records include the abnormal component failure time minus the starting time after installation. Record L as the life value of this type of abnormal components, Then there is L=S-F, where S represents the time when the component starts to be used, and F represents the end time when the abnormal component is set to be used;

Step (4) Calculate the life value of different components corresponding to abnormal state parameters; note that the life in step (3) represents the actual life record value of a certain type of component (such as the number of failures of this type of component is n, then there are n lifespans value), and the life value in this step is to first establish the aging law of the component according to the life value in step (3), and then simulate the life value of the component by computer according to the law.

In step (5), inspection is performed sequentially from the component with the smallest life value to the component with the largest life value, until the faulty component is found, and the state analysis and monitoring is completed.

The calculation method of abnormal part life value in the method step (4) of the present invention comprises the following steps:

Step (41) sorting, sorting the fault times n life values of a certain abnormal component in step (3) from small to large;

Step (42) parameter estimation, calculates the parameter estimation value of the number of times of failure n life-span values obey following function:

The bathtub curve, the probability density function of the bathtub curve is the estimated value of the parameters α and β of f(t)=β/α ^β exp(t/α) ^β exp[1-exp(t/α) ^β ], where t represents Component life variable, the value is L, the parameter α represents the position, and the parameter β represents the shape;

Weibull distribution, the probability density function of Weibull distribution is f(t)=αβt ^β-1 exp(-αt ^β ), wherein, the variable t represents the working life, the parameter α represents the position, and the parameter represents the shape;

Linear increasing probability density function f(t)=(at+b)exp(-1/2at ² -bt), wherein, variable t represents the working life, parameter a represents the slope of the linear function, and parameter b represents the intercept;

Exponential distribution, the probability density function is f(t)=αexp(-at), wherein, the variable t represents the working life, and the parameter α represents the position;

Step (43) uses the formula k=1+3.322lgn to group the life value data after n sorting, and the number of groups is k;

Step (44) calculates the frequency:

Calculate the interval between groups, that is, the group distance Δt=(L _a -S _m )/k, where L _a is the maximum value of life, and S _m is the minimum value;

下限值将组限取成等于下限值

且小于上限值即按半闭区间分配数据。Determine the limit value of each group, the group limit is the upper limit of each group

lower limit Take the group limit equal to the lower limit value

and less than the upper limit That is, data is allocated according to semi-closed intervals.

和小于上限值

进行比较，如若寿命时间t_j满足

则频数Δr_i＝Δr_i+1，并通过ω_i＝Δr_i/n计算频率ω_i。Count the frequency Δr _i falling into each group, according to the life time and the lower limit of each group

and less than the upper limit

For comparison, if the life time t _j satisfies

Then the frequency Δr _i =Δr _i +1, and the frequency ω _i is calculated by ω _i =Δr _i /n.

计算各组的累积频率，其中，r_i为至第i组结束时的累积频数。Step (45) passes

Calculate the cumulative frequency of each group, where r _i is the cumulative frequency until the end of the i-th group.

Calculate the bathtub curve, Weibull distribution, linear increasing function and exponential distribution distribution function F _i ', the integral of the four distribution density functions f(t) of F _i ', where 1≤i≤k;

Calculate k F _i separately, and F _i is the cumulative frequency of the i-th group; F (t ₎ obtained by integrating the distribution probability function after parameter estimation can obtain the value of k individuals, that is, both F _i ' and F _i have k;

Calculate D _i =|F _i -F _i '|, take KS test statistic D=max(D ₁ , D ₂ ,..., D _k );

Step (46) Model inspection, according to the set confidence level δ (generally 0.05, 0.01) and the number of failures n life values check the critical value D _c table of Kolmogorov test to get D _c , judge the above four Does the distribution exist D<D _c :

If only one D value is less than a certain critical value D _c , then the function corresponding to the D value is the selected function;

If there are two or more D values smaller than a certain critical value D _c , then select the function corresponding to the smaller D value as the selected function;

If the D values of all four models are greater than or equal to the critical value D _c , the average value method is directly applied to obtain the average life;

Step (47) Simulate the i-th abnormal component life by t _i =F ^-1 (t _i ), where 1≤i≤m, after m times of simulation, the life of the equipment or component at this time is

For the bathtub curve, the corresponding lifetime calculation formula is t _i =α{ln[1-ln[1-U _i ]]} ^1/β i=1, 2,..., and the corresponding lifetime calculation formula for Weibull distribution is t _i ＝[1/α(ln[1-U _i ])] ^1/β i=1, 2,..., the life calculation formula corresponding to the linear increasing function is i=1, 2,..., the life calculation formula corresponding to the exponential distribution function is t _i =1/α(ln[1-U _i ])i=1, 2,..., where U _i takes [0 , 1] random value;

The critical value D _c table of Kolmogorov test

Beneficial effects: according to the present invention, the continuous recording of data can be realized, the state parameter processing and performance analysis of the gas compressor can be carried out, and the corresponding relationship between the state signal and the fault can be established in combination with the reliability analysis and life prediction of the key equipment of the system. On-line safety monitoring and fault analysis of equipment to improve the safety, reliability, availability and economic benefits of the gas compressor system.

Description of drawings

The advantages of the above and/or other aspects of the present invention will become clearer as the present invention will be further described in detail in conjunction with the accompanying drawings and specific embodiments.

Figure 1 is a schematic diagram of the composition of the data acquisition system.

Figure 2 is a schematic diagram of the basic functions of the application software.

Detailed ways

This embodiment provides a gas compressor state parameter monitoring system and method, including a gas pressure sensor 1, a temperature sensor 2, a signal conditioner 3, an oil pressure sensor 4, a vibration sensor 5, a vibrometer 6, and a PXI data acquisition Card 7, display 8, computer 9, printer 10; wherein gas pressure sensor 1 is connected with signal conditioner 3, temperature sensor 2 is connected with signal conditioner 3, signal conditioner 3 is connected with engine oil pressure sensor 4, signal conditioner 3 is connected with PXI The data acquisition card 7 is connected, the vibration sensor 5 is connected with the vibrometer 6, the vibrometer 6 is connected with the PXI data acquisition card 7, the PXI data acquisition card 7 is connected with the computer 9, and the computer 9 is connected with the display 8 and the printer 10 respectively. Among them, the data acquisition card is a PXI data acquisition card produced by NI Company 7 . The state monitoring and reliability system of the gas compressor is based on the LabVIEW platform to measure the state parameters, and the application software suitable for this system is designed to have the function of database and life prediction.

As shown in Figure 2, temperature signal 11, pressure signal 12, vibration signal 13, historical maintenance records 14, database 15, equipment life distribution model 16, equipment life distribution model 17, predicted equipment life 18, status signal analysis and processing 19, Fault diagnosis 20; wherein, the temperature signal 11 is imported into the database 15, the pressure signal 12 is imported into the database 15, the vibration signal 13 is imported into the database 15, the historical maintenance record 14 is imported into the database 15, and the results of the database 15 are used for the equipment life distribution model 17 And state signal analysis and processing 18, equipment life distribution model 17 predicting equipment life 18, predicting equipment life 18 and state signal analysis and processing 19 for system fault diagnosis 20.

The signal from the corresponding sensor of the gas compressor pressure and temperature monitoring point is transmitted to the signal amplifier, and then transmitted to the PXI data acquisition card for data acquisition, and the corresponding data is input into the computer, and the cause of the fault is found by analyzing the status signal through the application software, and combined Fault tree analysis technology establishes a direct connection between state parameters and failure modes, and establishes an equipment degradation model through the historical maintenance records of key equipment, and applies reliability theory and technology and conducts model inspection, realizes equipment life prediction through software, and finally realizes gas compression Machine system fault diagnosis.

The invention also discloses a state analysis and monitoring method of a gas compressor, which inputs gas pressure signals, temperature signals, oil pressure signals, and vibration signals into a computer, and processes the above signals according to the following steps:

Step (1) comparing the gas pressure signal, temperature signal, engine oil pressure signal, and vibration signal with the upper limit and lower limit of the preset safety value, if the upper limit or lower limit is exceeded, an alarm is given, and the fault diagnosis module is started simultaneously;

Step (2) determines the type of corresponding abnormal parts by querying the gas compressor state parameter abnormality and fault correspondence table in the fault diagnosis module;

Gas compressor state parameter abnormality and fault correspondence table

Step (3) Query the historical maintenance records of various components in the database in the category of abnormal components. The historical maintenance records include the abnormal component failure time minus the starting time after installation. Record L as the life value of this type of abnormal components, Then there is L=S-F, where S represents the time when the component starts to be used, and F represents the end time when the abnormal component is set to be used;

Step (4) Calculate the life value of different components corresponding to abnormal state parameters; note that the life in step (3) represents the actual life record value of a certain type of component (such as the number of failures of this type of component is n, then there are n lifespans value), and the life value in this step is to first establish the aging law of the component according to the life value in step (3), and then simulate the life value of the component by computer according to the law.

In step (5), inspection is performed sequentially from the component with the smallest life value to the component with the largest life value, until the faulty component is found, and the state analysis and monitoring is completed.

The calculation method of abnormal part life value in the method step (4) of the present invention comprises the following steps:

Step (41) sorting, sorting the fault times n life values of a certain abnormal component in step (3) from small to large;

Step (42) parameter estimation, calculates the parameter estimation value of the number of times of failure n life-span values obey following function:

The bathtub curve, the probability density function of the bathtub curve is the estimated value of the parameters α and β of f(t)=β/α ^β exp(t/α) ^β exp[1-exp(t/α) ^β ], where t represents Component life variable, the value is L, the parameter α represents the position, and the parameter β represents the shape;

Weibull distribution, the probability density function of Weibull distribution is f(t)=αβt ^β-1 exp(-αt ^β ), wherein, the variable t represents the working life, the parameter α represents the position, and the parameter represents the shape;

Linear increasing probability density function f(t)=(at+b)exp(-1/2at ² -bt), wherein, variable t represents the working life, parameter a represents the slope of the linear function, and parameter b represents the intercept;

Exponential distribution, the probability density function is f(t)=αexp(-at), wherein, the variable t represents the working life, and the parameter α represents the position;

Step (43) uses the formula k=1+3.322lgn to group the life value data after n sorting, and the number of groups is k;

Step (44) calculates the frequency:

Calculate the interval between groups, that is, the group distance Δt=(L _a -S _m )/k, where L _a is the maximum value of life, and S _m is the minimum value;

下限值

将组限取成等于下限值

且小于上限值即按半闭区间分配数据。Determine the limit value of each group, the group limit is the upper limit of each group

lower limit

Take the group limit equal to the lower limit value

and less than the upper limit That is, data is allocated according to semi-closed intervals.

和小于上限值进行比较，如若寿命时间t_j满足

则频数Δr_i＝Δr_i+1，并通过ω_i＝Δr_i/n计算频率ω_i。Count the frequency Δr _i falling into each group, according to the life time and the lower limit of each group

and less than the upper limit For comparison, if the life time t _j satisfies

Then the frequency Δr _i =Δr _i +1, and the frequency ω _i is calculated by ω _i =Δr _i /n.

计算各组的累积频率，其中，r_i为至第i组结束时的累积频数。Step (45) passes

Calculate the cumulative frequency of each group, where r _i is the cumulative frequency until the end of the i-th group.

Calculate the bathtub curve, Weibull distribution, linear increasing function and exponential distribution distribution function F _i ', the integral of the four distribution density functions f(t) of F _i ', where 1≤i≤k;

Calculate k F _i separately, and F _i is the cumulative frequency of the i-th group; F (t ₎ obtained by integrating the distribution probability function after parameter estimation can obtain the value of k individuals, that is, both F _i ' and F _i have k;

Calculate D _i =|F _i -F _i '|, take KS test statistic D=max(D ₁ , D ₂ ,..., D _k );

Step (46) Model inspection, according to the set confidence level δ (generally 0.05, 0.01) and the number of failures n life values check the critical value D _c table of Kolmogorov test to get D _c , judge the above four Does the distribution exist D<D _c :

If only one D value is less than a certain critical value D _c , then the function corresponding to the D value is the selected function;

If there are two or more D values smaller than a certain critical value D _c , then select the function corresponding to the smaller D value as the selected function;

If the D values of all four models are greater than or equal to the critical value D _c , the average value method is directly applied to obtain the average life;

Step (47) Simulate the i-th abnormal component life by t _i =F ^-1 (t _i ), where 1≤i≤m, after m times of simulation, the life of the equipment or component at this time is

i＝1，2，...，指数分布函数对应的寿命计算式为t_i＝1/α(ln[1-U_i])i＝1，2，...，其中U_i取[0，1]随机值；For the bathtub curve, the corresponding lifetime calculation formula is t _i =α{ln[1-ln[1-U _i ]]} ^1/β i=1, 2,..., and the corresponding lifetime calculation formula for Weibull distribution is t _i ＝[1/α(ln[1-U _i ])] ^1/β i=1, 2,..., the life calculation formula corresponding to the linear increasing function is

i=1, 2,..., the life calculation formula corresponding to the exponential distribution function is t _i =1/α(ln[1-U _i ])i=1, 2,..., where U _i takes [0 , 1] random value;

The critical value D _c table of Kolmogorov test

Example:

The present embodiment provides a kind of application situation of gas compressor state parameter monitoring system and method, by gas pressure sensor 1, detection pressure signal is passed to signal conditioner 3, passes to PXI data acquisition card (7) after signal processing, And transmit it to the computer, through the LabVIEW software platform, the pressure of the primary exhaust valve is read out as 0.63 MPa, and compared with the set upper limit of 0.62 MPa, it is judged that the exhaust pressure is higher than the normal exhaust pressure, and the state of the gas compressor is checked The parameter abnormality and fault correspondence table establishes that the main potential fault modes include damage to the exhaust valve, damage to the suction valve, excessive gap between the piston ring and the cylinder, and pressure gauge failure. diagnostic module;

Gas compressor status parameter abnormality and fault correspondence table:

Check the maintenance records of the exhaust valve, suction valve, piston ring and cylinder, and pressure gauge failure from the database through the software, and calculate the exhaust valve, suction valve, piston ring according to the starting time and failure time respectively The gap between the air cylinder and the pressure gauge is too large and the service life of the pressure gauge is shown in Table 3 (unit: hour), and the corresponding failure times n are shown in Table 3. Component detection life table

Number of failures Vent Suction valve Clearance between piston ring and cylinder pressure gauge 1 2578 4790 4829 7890 2 590 2571 763 10957 3 4502 7725 7903 6890 4 1793 7890 6783 5372 5 3367 13481 9903 7693 6 2790 8902 3648 8738 7 1680 5704 6720 7383 8 4721 4217 7374 7562 9 1480 5820 6837 5637 10 2092 5821 9012 5694 11 5071 6890 5378 6704 12 2695 6093 6721 3783 13 773 8902 3264 7003 14 1679 3680 6346 4582 15 4903 5272 3267 7904 16 4720 6890 7413 7098 17 1890 4270 2367 18 3805 9863 7893 19 3407 8632 20 4704 twenty one 3534 twenty two 3572

twenty three 6730 twenty four 3107

For 24, 18, 19 and 16, sort the life data of the exhaust valve to get 590, 773, 1480, 1659, 1680, 1793, 1890, 2092, 2578, 2695, 2790, 3072, 3107, 3367, 3407, 3534, 3805, 4321, 4502, 4704, 4720, 4903, 5071, 6730, the estimated bathtub curve parameters are position parameter α is 3207.3, shape parameter β is 1.019; Weibull distribution position parameter α is 2515.7, shape parameter β is 1.141; The linear increasing parameter a is 0.3271, the parameter b is 0.8352, and the exponential distribution is 3508.6;

These data are grouped, by k=1+3.322lg24=5.585, then the number of groups is approximately 6, and the group interval Δt is 1023, then there are six intervals, which are respectively [590, 1613), [1613, 2636), [ 2636, 3659), [3659, 4682), [4682, 5705) and [5705, 6730], the six interval frequencies are 3, 6, 7, 3, 4, 1, and the cumulative frequency F _i is 0.125, 0.375 , 0.667, 0.792, 0.958 and 1.

Calculate the corresponding distribution function when t _i is 1613, 2636, 3659, 4682, 5705 and 6730 according to the parameter estimates of the above four distributions, and calculate the test statistics of bathtub curve, Weibull distribution, linear increasing function and exponential distribution respectively The amounts D were 0.431, 0.205, 0.319 and 0.496, respectively.

The given confidence level δ is 0.05, and the critical value D _c of the Kolmogorov test shows that the number of failures is 24. D _c is 0.268. Compared with the test statistic D of KS, it is known that only the Weibull distribution The test statistic D of KS is 0.205 and less than 0.268, so it is judged that the exhaust valve obeys the Weibull distribution, and the position parameter α is 2515.7, and the shape parameter β is 1.141;

Use the same method to judge that the life distribution of the suction valve is a Weibull distribution. The position parameter α of the Weibull distribution of the suction valve is 4815.7, and the shape parameter β is 1.017. The gap between the piston ring and the cylinder is too large to obey the Weibull distribution, and the position parameter α is 7018. , the shape parameter β is 1.062, the pressure gauge obeys linear increase, the parameter a is 0.2629, and the parameter b is 0.7274.

i＝1，2，...模拟100次其活塞环与气缸间隙过大为5381.3小时，对这些寿命值进行排序，由2471.4＜4759.7＜5381.3＜6409.8，知道发生故障的部件顺序为排气阀、吸气阀、压力表和活塞环与气缸间隙过大，此时先检查排气阀是否发生故障，如没有故障，则检查吸气阀，以此类推，如果均没有发生故障，则考虑是否存在其它原因。Finally, by t _i =[1/2517(ln[1-U _i ])] ^1/1.141 i=1, 2, ... simulating 100 times its exhaust valve life is 2471.4 hours, by t _i =[1/ 4815.7(ln[1-U _i ])] ^1/1.017 i=1, 2,... Simulating 100 times, the exhaust valve life is 4759.7 hours, by t _i =[1/7018(ln[1-U _i ])] ^1/1.062 i=1, 2, ... simulating 100 times the gap between the piston ring and the cylinder is too large for 6409.8 hours, by

i=1, 2,... 100 simulations, the gap between the piston ring and the cylinder is too large for 5381.3 hours, sort these life values, from 2471.4<4759.7<5381.3<6409.8, it is known that the sequence of the faulty parts is the exhaust valve , the suction valve, pressure gauge, and the gap between the piston ring and the cylinder are too large. At this time, first check whether the exhaust valve is faulty. If there is no fault, check the suction valve, and so on. If there is no fault, then consider whether There are other reasons.

The gas compressor state monitoring and reliability analysis system and method of the present invention acquire object information by collecting multiple variable data of different types through different sensors. And through the graphical programming control method of LabVIEW, the temperature, pressure, vibration and other physical quantities of the gas compressor can be collected and entered into the computer for analysis. Analyze the cause of the fault through the status signal, and combine the fault tree analysis and the reliability analysis technology of equipment aging to realize the fault diagnosis and improve the reliability and availability of the equipment. Therefore, compared with the traditional gas compressor unit monitoring platform, the safety and reliability of the system can be improved.

The monitoring of gas compressor state parameters is the process of using sensors to measure physical quantities such as temperature, pressure, and vibration of the compressor, converting them into appropriate intermediate quantities, such as voltage or current, and displaying, identifying, analyzing, and trending through the LabVIEW platform. The LabVIEW platform is a software platform based on instruments or virtual instruments. Through the graphical programming control method of LabVIEW, multiple variable data of physical quantities such as temperature, pressure, and vibration of the gas compressor can be collected and entered into the computer for analysis. The data acquisition system needs to use appropriate sensors and supporting hardware, and the corresponding software will convert and transmit the data obtained from the sensors. Apply NI's hardware products, use the LabVIEW development platform, and use Matlab's ability to process complex algorithms to quickly and high-quality develop applications that meet the requirements. Using this platform can enhance the ability to build scientific and engineering systems, and provides a convenient way to implement instrument programming and data acquisition systems. It can also be used to conduct principle research, design, test and implement instrument systems, thereby greatly improving work efficiency.

The present invention provides an idea and method of a state analysis and monitoring system and method of a gas compressor. There are many methods and approaches to specifically realize the technical solution. The above description is only a preferred embodiment of the present invention. Those of ordinary skill in the art can make some improvements and modifications without departing from the principle of the present invention, and these improvements and modifications should also be regarded as the protection scope of the present invention. All components that are not specified in this embodiment can be realized by existing technologies.

Claims (7)

1. the state analysis monitoring system of a gas compressor, it is characterized in that, comprise gas pressure sensor (1), temperature transducer (2), signal conditioner (3), oil pressure sensor (4), vibration transducer (5), vibrometer (6), data collecting card (7), display device (8), computer (9);

Described gas pressure sensor (1), state temperature transducer (2) and oil pressure sensor (4) is connected with described signal conditioner (3) respectively; Described vibration transducer (5) is connected with described vibrometer (6), and described vibrometer (6) and signal conditioner (3) are connected with described data collecting card (7) respectively; Described data collecting card (7) is connected with described computer (9), and described computer (9) is connected with described display device (8).

2. the state analysis monitoring system of a kind of gas compressor according to claim 1, it is characterized in that, described signal conditioner (3) is used for the signal that gas pressure sensor (1), temperature transducer (2) and oil pressure sensor (4) transmit is amplified, and is transferred to data collecting card (7).

3. the state analysis monitoring system of a kind of gas compressor according to claim 1 is characterized in that, described data collecting card (7) is based on the PX bussing technique, and the signal that is used for gathering is converted into electrical signal, is transferred to computer.

4. the state analysis monitoring system of a kind of gas compressor according to claim 1 is characterized in that, described vibrometer (6) is used for oscillating signal is amplified processing, and is transferred to data collecting card (7).

5. the state analysis monitoring system of a kind of gas compressor according to claim 1 is characterized in that, described computer (9) is equipped with the LabVIEW platform software.

6. the state analysis monitoring method of a gas compressor is characterized in that, gas pressure signal, temperature signal, engine oil pressure signal, oscillating signal is imported computer, and according to following steps above signal is handled:

Step (1) compares the upper and lower bound of gas pressure signal, temperature signal, engine oil pressure signal, oscillating signal and predefined safety value, as exceeds the upper limit or lower limit is then reported to the police, and starts fault diagnosis module simultaneously;

Step (2) is unusual and fault correspondence table by the gas compressor status parameter in the inquiry fault diagnosis module, thereby determines corresponding unusual variety of components;

The historical maintenance record of various parts in database in the unusual variety of components of step (3) inquiry, described historical maintenance record comprises after unusual parts down time deducts its installation brings into use the time, note L is such unusual parts life value, L=S-F is then arranged, wherein on behalf of parts, S bring into use the time, and on behalf of unusual component settings, F use terminal time;

The life value of the different parts of step (4) computing mode abnormal parameters correspondence;

Step (5) is that order is checked by the parts of life value minimum to the parts of life value maximum, up to finding trouble unit, finishing stage analysis monitoring.

7. the state analysis monitoring method of a kind of gas compressor according to claim 6 is characterized in that, the computational methods of unusual parts life value comprise the steps: in the step (4)

Step (41) ordering is carried out ascending order with the number of stoppages n life value of (3) unusual parts of step and is sorted;

Step (42) parameter estimation, calculate n life value of the number of stoppages and obey following function parameters estimated value:

Tub curve, tub curve probability density function are f (t)=beta/alpha ^βExp (t/ α) ^βExp[1-exp (t/ α) ^β] parameter alpha and the estimated value of β, wherein, t represents the component life variable, is worth to be that on behalf of position, parameter beta, L, parameter alpha represent shape;

Weibull distribution, Weibull distribution probability density function are f (t)=α β t ^β-1Exp (α t ^β), wherein, variable t represents operating life, on behalf of position, parameter, parameter alpha represent shape;

Linear increment probability density function f (t)=(at+b) exp (1/2at ²-bt), wherein, variable t represents operating life, and parameter a represents the slope of linear function, and parameter b is represented intercept;

Exponential distribution, probability density function are that (at), wherein, variable t represents operating life to f (t)=α exp, and parameter alpha is represented the position;

Life value data after step (43) uses formula k=1+3.322lgn to n ordering are divided into groups, and packet count is k;

Step (44) calculated rate:

(the L apart from Δ t=is promptly organized at interval between calculating group and the group _a-S _m)/k, wherein, L _aBe the maximum value in life-span, S _mBe minimum value;

Determine each group group limit value, the group limit i.e. the CLV ceiling limit value of each group

Lower limit To organize to limit to get and become to equal lower limit

And less than CLV ceiling limit value

Promptly press the semi-closed interval distribute data.

Statistics falls into the frequency Δ r of each group _i, according to life time and each lower class limit value With less than CLV ceiling limit value

Compare, if life time t _jSatisfy Frequency Δ r then _i=Δ r _i+ 1, and pass through ω _i=Δ r _i/ n calculated rate ω _i

Step (45) is passed through

Calculate the cumulative frequency of each group, wherein, r _iCumulative frequency when finishing for organizing to i.

Calculate the distribution function F of tub curve, Weibull distribution, linear increasing function and exponential distribution respectively _i', F _i' the integration of four kinds of distribution density function f (t), 1≤i≤k wherein;

Calculate k F respectively _i, F _iIt is the cumulative frequency of i group; F _iCalculate and to obtain k individual numerical value, i.e. F by obtaining F (t) that the distribution probability functional integration obtains after the parameter estimation _i' and F _iAll there be k;

Calculate D _i=| F _i-F _i' |, get the test statistics D=max (D of K-S ₁, D ₂..., D _k);

Step (46) model testing is looked into the critical value D of kolmogorov test according to setting a confidence level δ and a number of stoppages n life value _cTable draws D _c, judge whether top four kinds of distributions exist D＜D _c:

If have only a D value less than a certain critical value D _c, then the pairing function of this D value is selected function;

If two or more D values are arranged less than a certain critical value D _c, then choosing the less pairing function of D value is selected function;

If the D value of all four kinds of models is all more than or equal to critical value D _c, directly use mean value method to obtain its life-span average;

Step (47) is by t _i=F ^-1(ti) the i time unusual component life of simulation, wherein 1≤i≤m simulates after m time, and then this moment, equipment or part life were

To its corresponding Life Calculation formula of tub curve is t _i=α { ln[1-ln[1-U _i]] ^{1/ β}I=1,2 ...;

The Life Calculation formula of Weibull distribution correspondence is t _i=[1/ α (ln[1-U _i])] ^{1/ β}I=1,2 ...;

The Life Calculation formula of linear increasing function correspondence is

I=1,2 ...;

The Life Calculation formula of exponential distribution function correspondence is t _i=1/ α (ln[1-U _i]) i=1,2 ..., U wherein _iGet [0,1] random value.

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