CN120367906B - A method for automatic control of equipment in a hydraulic station - Google Patents
A method for automatic control of equipment in a hydraulic stationInfo
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- CN120367906B CN120367906B CN202510856987.5A CN202510856987A CN120367906B CN 120367906 B CN120367906 B CN 120367906B CN 202510856987 A CN202510856987 A CN 202510856987A CN 120367906 B CN120367906 B CN 120367906B
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Abstract
The invention discloses an automatic control method for equipment in a hydraulic station, which particularly relates to the technical field of automatic control of equipment, and aims to timely find potential faults of main equipment by collecting operation data of the main hydraulic pump and carrying out fault prediction, start a preloading process of a standby hydraulic pump according to fault occurrence time, effectively evaluate the stability of the standby pump in a switching process by acquiring oil viscosity abnormality information and load increment stability information in the preloading process of the standby hydraulic pump and calculating the oil viscosity abnormality coefficient and the load increment stability coefficient based on the oil viscosity abnormality information and the load increment stability information, comprehensively analyze the working state of the standby pump, identify potential loading hidden danger in advance, carry out corresponding control through the output of a risk model, avoid equipment faults or unstable systems caused by incorrect switching, dynamically judge the switching risk of the standby hydraulic pump according to comparison of a standby switching hidden danger index and a preset threshold value, and ensure stable operation and seamless switching of a hydraulic system.
Description
Technical Field
The invention relates to the technical field of equipment automation control, in particular to an automatic control method for equipment in a hydraulic station.
Background
The hydraulic system is widely applied to various industrial equipment, such as the fields of mechanical manufacture, aerospace, automobile production, metallurgy and the like, and is used as an efficient energy transmission and control means. The hydraulic station is a core component of the hydraulic system and is mainly responsible for transmitting a power source to the actuator, thereby completing various mechanical operations. Various devices (such as hydraulic pumps, valves, actuators, etc.) in the hydraulic station often need to ensure stability, reliability and efficient response of the system during normal operation. However, in practical application of the hydraulic system, when the main device fails or needs to be switched to the standby device due to load fluctuation, how to ensure seamless switching and quick response between devices is still one of technical problems, and the conventional hydraulic station control system often adopts a hard switching strategy, i.e. the main device is directly switched to the standby device after being unloaded, and the standby device is usually not preheated or operated under low load, which results in problems of starting delay, uneven oil temperature, and the like. The switching mode can not recover the high-efficiency response of the system in a short time, and even can cause hydraulic impact or equipment failure in severe cases, so that the production or operation is interrupted, and the production efficiency and the equipment service life are affected.
Disclosure of Invention
In order to overcome the above-mentioned drawbacks of the prior art, an embodiment of the present invention provides an automatic control method for equipment in a hydraulic station, so as to solve the problems set forth in the above-mentioned background art.
In order to achieve the above purpose, the present invention provides the following technical solutions:
An automatic control method for equipment in a hydraulic station comprises the following steps:
Step S1, collecting operation data of a main hydraulic pump, predicting failure occurrence time of the main hydraulic pump based on the operation data of the main hydraulic pump, and starting a preloading process of a standby hydraulic pump according to the failure occurrence time;
S2, acquiring oil viscosity abnormality information and load increment stability information of a standby hydraulic pump in a preloading process;
Step S3, a standby switching hidden danger risk model is constructed according to oil viscosity abnormal information and load increment stable information in the standby hydraulic pump preloading process, a standby switching hidden danger risk index is output, and potential loading hidden danger in the standby hydraulic pump preloading process is evaluated;
and S4, comparing the risk index of the standby switching hidden trouble with a preset risk index threshold of the standby switching hidden trouble, and performing classified control on the hydraulic pump.
In a preferred embodiment, by installing a high-precision sensor, the operation data of the main hydraulic pump, including temperature, pressure, flow, vibration and oil viscosity, are collected in real time;
The data preprocessing of the collected main hydraulic pump operation data comprises the processing of a missing value and the data standardization;
Taking the fault occurrence time as a target variable, taking the operation data of the main hydraulic pump as a characteristic variable, and constructing a characteristic vector according to the operation data of the main hydraulic pump;
Constructing a fault occurrence time prediction model: Wherein For the output of the time-to-failure prediction model,Is a weight vectorIs to be used in the present invention,As a characteristic variable, a characteristic variable is used,Is a bias term;
Constructing an objective function;
training a failure occurrence time prediction model using the main hydraulic pump historical operating data and minimizing an objective function to find an optimal weight vector Bias term;
Inputting the real-time collected main hydraulic pump operation data into a trained fault occurrence time prediction model to predict the fault occurrence time of the main hydraulic pump;
The starting conditions for starting the pre-loading process of the standby hydraulic pump according to the fault occurrence time are as follows: Wherein For the current time period of time,In order for the time of occurrence of the fault,The lead time required to preload the backup hydraulic pump,Representation of ifAnd (3) withThe pre-loading process of the backup hydraulic pump is started when the values are equal.
In a preferred embodiment, the degree of abnormality of the oil viscosity in the pre-loading process of the standby hydraulic pump is measured by acquiring the abnormality information of the oil viscosity in the pre-loading process of the standby hydraulic pump, analyzing the oil viscosity in the pre-loading process of the standby hydraulic pump and calculating an abnormality coefficient of the oil viscosity;
the acquisition logic of the oil viscosity anomaly coefficient is as follows:
the oil viscosity building oil viscosity time sequence in the pre-loading process of the standby hydraulic pump is acquired through a high-precision sensor: Wherein The oil viscosity collected at time T is represented, t= {1,2,., T }, T being a positive integer;
Clustering analysis is carried out on the oil viscosity time sequence, and the oil viscosity anomaly coefficient is calculated as follows:
s21, determining the number SL of initial clustering centers by using a contour coefficient method, and randomly selecting the oil viscosity of SL time points from an oil viscosity time sequence as the initial clustering centers;
Step S22, calculating the Euclidean distance between the oil viscosity and the clustering center at each time point in the oil viscosity time sequence, and distributing the oil viscosity at each time point to the clustering center with the nearest Euclidean distance;
step S23, calculating an average value of the viscosity of the oil liquid in each clustering center, and taking the average value as a new clustering center coordinate;
step S24, repeating the step S22 and the step S23 to update the clustering center in an iterative manner until the clustering center is not changed;
Calculating the deviation of the oil viscosity at each time point and the clustering center where the oil viscosity is located: Wherein The deviation of the oil viscosity and the cluster center where the oil viscosity is positioned is represented,The oil viscosity value at time t is indicated,The oil viscosity value of the kth cluster center is represented;
calculating the viscosity anomaly coefficient of oil: Wherein Is the viscosity anomaly coefficient of the oil liquid,Is the deviation between the viscosity of oil at the time point t and the cluster center where the oil is located,Time pointIs used for controlling the viscosity value of oil liquid,Is the time point contained in the kth cluster center.
In a preferred embodiment, the load increment stability degree of the pre-loading process of the standby hydraulic pump is measured by acquiring the load increment stability information of the pre-loading process of the standby hydraulic pump, analyzing the load increment stability condition of the pre-loading process of the standby hydraulic pump and calculating a load increment stability coefficient;
the acquisition logic of the load increment stability coefficient is as follows:
gradually applying system load to the standby hydraulic pump until the system load of the main hydraulic pump is reached, and recording system load values at different times Response time of stand-by hydraulic pump to system load change at different times;
Calculating the load change speed: Wherein The load change rate at time t is indicated,As the system load value at time t,For the system load value at time t-1,Is a sampling time interval;
calculating response lag time: Wherein In order to respond to the lag time,To reserve the response time of the hydraulic pump to system load changes,Is the expected response time;
Calculating the load increment stable values at different times: Wherein The load representing time t is incremented by a steady value,For the transfer function of the systemIs used to determine the phase angle of (c),WhereinFor the static gain of the system,Is the time constant of the system and,In units of imaginary numbers,Is angular frequency;
Calculating a load increasing stability coefficient: Wherein The stability factor is incremented for the load,The steady value is incremented for the load at time r,Is the time window size.
In a preferred embodiment, a standby potential switching hazard risk model is constructed according to the oil viscosity anomaly coefficient and the load increasing stability coefficient, and a standby potential switching hazard risk index is outputThe model is based on the following formulaIn the followingRespectively representing the preset proportional coefficients of the viscosity anomaly coefficient and the load increment stability coefficient of the oil liquid, andAre all greater than 0.
In a preferred embodiment, in step S4, the risk index of the backup switch hidden danger is compared with a preset risk index threshold of the backup switch hidden danger, and the hydraulic pump is controlled in a classification manner, specifically as follows:
if the risk index of the standby switching hidden trouble is larger than the threshold value of the risk index of the standby switching hidden trouble, marking the current standby hydraulic pump as switching risk equipment, and carrying out early warning maintenance on the switching risk equipment;
And if the risk index of the standby switching hidden danger is smaller than or equal to the threshold value of the risk index of the standby switching hidden danger, sequencing the standby hydraulic pumps which are not marked as the switching risk equipment according to the risk index of the standby switching hidden danger from small to large, generating a standby equipment switching sequencing table, and switching the standby equipment with the first sequencing position.
The invention has the technical effects and advantages that:
1. According to the invention, through collecting the operation data of the main hydraulic pump and carrying out fault prediction, potential faults of the main equipment can be found in time, the pre-loading process of the standby hydraulic pump is started according to the fault occurrence time, the abnormal oil viscosity information and the load increasing stability information in the pre-loading process of the standby hydraulic pump are obtained in real time, the stability of the standby pump in the switching process is effectively evaluated based on the abnormal oil viscosity information and the load increasing stability coefficient, the working state of the standby pump is comprehensively analyzed, potential loading hidden dangers can be identified in advance when the abnormal oil viscosity or the load increasing instability occurs, corresponding control is carried out through the output of a risk model, the switching risk of the standby hydraulic pump is dynamically judged according to the comparison of the risk index of the standby switching hidden dangers with a preset threshold value, early warning and maintenance are automatically carried out under the condition of higher risk, the performance of the standby equipment is ensured to meet the switching requirement, and when the risk is lower, the standby hydraulic pump with minimum risk is preferentially selected for switching, and the operation and seamless switching of the hydraulic system are ensured.
Drawings
For the convenience of those skilled in the art, the present invention will be further described with reference to the accompanying drawings;
FIG. 1 is a flow chart of a method according to an embodiment of the invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Fig. 1 shows an automatic control method for equipment in a hydraulic station, which comprises the following steps:
Step S1, collecting operation data of a main hydraulic pump, predicting failure occurrence time of the main hydraulic pump based on the operation data of the main hydraulic pump, and starting a preloading process of a standby hydraulic pump according to the failure occurrence time;
S2, acquiring oil viscosity abnormality information and load increment stability information of a standby hydraulic pump in a preloading process;
Step S3, a standby switching hidden danger risk model is constructed according to oil viscosity abnormal information and load increment stable information in the standby hydraulic pump preloading process, a standby switching hidden danger risk index is output, and potential loading hidden danger in the standby hydraulic pump preloading process is evaluated;
step S4, comparing the risk index of the standby switching hidden trouble with a preset risk index threshold of the standby switching hidden trouble, and performing classified control on the hydraulic pump;
Step S1, collecting operation data of a main hydraulic pump, predicting failure occurrence time of the main hydraulic pump based on the operation data of the main hydraulic pump, and starting a preloading process of a standby hydraulic pump according to the failure occurrence time;
By installing a high-precision sensor, the operation data of the main hydraulic pump are collected in real time, wherein the operation data comprise a plurality of parameters such as temperature, pressure, flow, vibration, oil viscosity and the like;
The high-precision sensor for collecting the operation data of the main hydraulic pump comprises a temperature sensor (such as a thermocouple and an RTD, which can be installed at the outlet of the hydraulic pump to monitor the oil temperature of the hydraulic pump), a pressure sensor (such as a strain type pressure sensor and a piezoresistive type pressure sensor, which can be installed at the outlet of the hydraulic pump to monitor the output pressure in real time), a flow sensor (such as a vortex shedding flowmeter, an electromagnetic flowmeter and a volumetric flow sensor, which can be installed at the outlet of the hydraulic pump to monitor the output flow of the pump), a vibration sensor (such as an acceleration sensor, which can be installed at the surface of the hydraulic pump to monitor the surface vibration signal of the hydraulic pump), and an oil viscosity sensor (such as a rotary viscosity sensor and an oscillating viscosity sensor, which can be installed in the hydraulic tank to monitor the oil viscosity);
The data preprocessing of the collected main hydraulic pump operation data comprises missing value processing (if missing data exists, the missing data can be filled by using a mean filling or other interpolation methods), and data standardization (data is usually required to be standardized due to different sensor data sizes, for example, Z-score standardization is adopted);
Taking the fault occurrence time as a target variable, taking the operation data of the main hydraulic pump as a characteristic variable, and constructing a characteristic vector according to the operation data of the main hydraulic pump: Wherein A feature vector representing the time t is represented,Respectively representing the temperature, pressure, flow, vibration and oil viscosity at the moment t;
Constructing a fault occurrence time prediction model: Wherein As the output of the failure occurrence time prediction model (the output of the failure occurrence time prediction model is the failure occurrence time),Is a weight vectorIs to be used in the present invention,As a characteristic variable, a characteristic variable is used,Is a bias term;
Constructing an objective function: Wherein For regularization terms, to prevent overfitting,For the prediction error of each sample,,As a total number of samples,Is a superparameter for controlling trade-off between error and model complexity;
training a failure occurrence time prediction model using the main hydraulic pump historical operating data and minimizing an objective function to find an optimal weight vector Bias term;
Inputting the real-time collected main hydraulic pump operation data into a trained fault occurrence time prediction model to predict the fault occurrence time of the main hydraulic pump;
The starting conditions for starting the pre-loading process of the standby hydraulic pump according to the fault occurrence time are as follows: Wherein For the current time period of time,In order for the time of occurrence of the fault,The lead time required to preload the backup hydraulic pump,Representation of ifAnd (3) withStarting a preloading process of the backup hydraulic pump when the values are equal;
It should be noted that, the above formulas are all dimensionality removal and numerical calculation, and common dimensionality removal methods include Min-Max normalization, Z-Score normalization, and the like, which are not described herein;
S2, acquiring oil viscosity abnormality information and load increment stability information of a standby hydraulic pump in a preloading process;
the method comprises the steps of analyzing the oil viscosity condition of the pre-loading process of the standby hydraulic pump by acquiring the oil viscosity abnormality information of the pre-loading process of the standby hydraulic pump, and calculating the oil viscosity abnormality coefficient to measure the oil viscosity abnormality degree of the pre-loading process of the standby hydraulic pump;
the acquisition logic of the oil viscosity anomaly coefficient is as follows:
the oil viscosity building oil viscosity time sequence in the pre-loading process of the standby hydraulic pump is acquired through a high-precision sensor: Wherein The oil viscosity collected at time T is represented, t= {1,2,., T }, T being a positive integer;
Clustering analysis is carried out on the oil viscosity time sequence, and the oil viscosity anomaly coefficient is calculated as follows:
s21, determining the number SL of initial clustering centers by using a contour coefficient method, and randomly selecting the oil viscosity of SL time points from an oil viscosity time sequence as the initial clustering centers;
It should be noted that, the contour coefficient method is a commonly used clustering effect evaluation method, the best initial clustering quantity can be selected by calculating the contour coefficient, the contour coefficient is used for measuring the compactness of each point and the similar points and the separation of each point and the nearest class, and the calculation formula of the contour coefficient is as follows: Wherein The profile coefficients are represented by a set of coefficients,The average distance between the sample point and all other sample points in the same class is used to represent the degree of aggregation of the sample points,The average distance between the sample point and all the nearest sample points in different classes is used for representing the separation degree of the sample point, and the number of clusters with the largest profile coefficient is selected as the initial number of cluster centers SL by calculating the profile coefficient under different cluster numbers;
Step S22, calculating the Euclidean distance between the oil viscosity and the clustering center at each time point in the oil viscosity time sequence, and distributing the oil viscosity at each time point to the clustering center with the nearest Euclidean distance;
In an alternative example, the Euclidean distance is calculated by calculating, for each time point t, the Euclidean distance of the oil viscosity value from all cluster centers ,:WhereinThe oil viscosity value at time t is,The oil viscosity value is the k clustering center;
step S23, calculating an average value of the viscosity of the oil liquid in each clustering center, and taking the average value as a new clustering center coordinate;
step S24, repeating the step S22 and the step S23 to update the clustering center in an iterative manner until the clustering center is not changed;
Calculating the deviation of the oil viscosity at each time point and the clustering center where the oil viscosity is located: Wherein The deviation of the oil viscosity and the cluster center where the oil viscosity is positioned is represented,The oil viscosity value at time t is indicated,The oil viscosity value of the kth cluster center is represented;
calculating the viscosity anomaly coefficient of oil: Wherein Is the viscosity anomaly coefficient of the oil liquid,Is the deviation between the viscosity of oil at the time point t and the cluster center where the oil is located,Time pointIs used for controlling the viscosity value of oil liquid,A time point contained in the kth cluster center;
It should be noted that, the above formulas are all dimensionality removal and numerical calculation, and common dimensionality removal methods include Min-Max normalization, Z-Score normalization, and the like, which are not described herein;
The viscosity abnormality coefficient of the oil liquid is a key index for measuring the viscosity abnormality degree of the oil liquid of the standby hydraulic pump in the preloading process. The viscosity of oil is the resistance coefficient of the flow of hydraulic oil in a hydraulic system, and directly influences the working efficiency of a hydraulic pump and the stability of the system. In the operation process of the hydraulic system, the viscosity of the oil may be affected by various factors, such as temperature change, oil aging, load fluctuation, etc., so as to affect the working performance of the backup hydraulic pump. Potential hidden danger can be timely identified through calculating the viscosity anomaly coefficient of the oil, and unstable operation of the standby pump caused by the viscosity anomaly of the oil is prevented, so that more accurate and efficient fault early warning and risk control are realized. The larger abnormal oil viscosity coefficient indicates that the viscosity of the oil is obviously deviated from the normal range in the preloading process of the standby hydraulic pump, which is probably caused by the factors of low oil temperature, aging of the oil, entering of pollutants into a system and the like. When the viscosity of the oil is abnormally increased, the working flow of the hydraulic pump can be influenced, the pressure fluctuation is increased, the abrasion in the pump body can be possibly increased, and even the hydraulic pump can be blocked or can not be started when serious. Therefore, the larger oil viscosity anomaly coefficient is an early warning signal of potential faults of the backup pump system, and the system is reminded to pay special attention to the temperature and viscosity of oil when the backup pump system is switched, so that unstable starting of the backup hydraulic pump or incapability of effectively bearing load due to overlarge oil viscosity is prevented. In contrast, a smaller viscosity anomaly coefficient of the oil indicates that the viscosity of the oil changes less in the pre-loading process of the standby hydraulic pump, and the standby hydraulic pump is in a normal working range. This generally means that the hydraulic oil is in a more optimal working state and the backup pump is able to maintain stability and efficiency while taking over the load. The smaller abnormal coefficient shows that the temperature control, filtering and lubrication effects of the hydraulic system are good, oil is not polluted or excessively aged, the work load of the hydraulic pump can be stably transferred from the main pump to the standby pump, the response delay in system switching is reduced, and the efficiency and safety of the switching process are improved. Potential loading hidden danger in the pre-loading process of the standby hydraulic pump is estimated based on the oil viscosity abnormal coefficient, real-time and accurate oil viscosity deviation information can be provided, and the hydraulic system is helped to monitor the working state of the standby pump. By timely detecting the abnormal change of the viscosity of the oil, the possible working risk of the hydraulic pump can be identified in advance, such as overload or difficult starting of the pump body caused by the too high viscosity of the oil, or insufficient lubrication and aggravation of abrasion of the pump body caused by the too low viscosity of the oil. Therefore, the system can take corresponding precautions before the problem occurs, and the situation that the standby pump cannot be effectively switched or is unstable in long-term operation is avoided, so that the reliability and the operation efficiency of the whole hydraulic system are improved.
The operation state of the backup hydraulic pump is monitored in real time through the high-precision sensor, oil viscosity data at each moment are obtained, and the data are constructed into a time sequence to represent oil viscosity values acquired at a time point t. The data not only can provide basis for subsequent fault prediction and performance evaluation, but also can provide real-time feedback for abnormal conditions in the pre-loading process of the standby hydraulic pump. Next, cluster analysis is used to identify outliers in the oil viscosity data. The cluster analysis is an unsupervised learning method, and can group oil viscosity values in a time sequence and judge whether abnormal fluctuation exists or not by calculating the characteristic value of each cluster group. In the cluster analysis, the number of cluster centers is first determined by a contour coefficient method. The contour coefficient method is a standard for evaluating the clustering effect, and the most suitable clustering number is judged by calculating the similarity between each data point and the clustering center. The method not only can maximize the compactness and the separation degree of the clusters, but also can automatically select proper cluster numbers, thereby ensuring the accuracy of cluster analysis. After the cluster number is determined, an initial cluster center is randomly selected from the oil viscosity time sequence, and Euclidean distances between the oil viscosity at each time point and all the cluster centers are calculated. The viscosity of the oil at each time point is distributed to the closest cluster center according to the distance from the cluster center, and the process can divide the data into several groups with similar characteristics. And continuing to calculate the average value of the oil viscosity in each clustering center along with the progress of clustering, and taking the average value as a new clustering center coordinate. This process is repeated until the cluster center has stabilized, i.e., the position of the cluster center is no longer changed. Through the cluster analysis, the deviation of the oil viscosity at each time point and the cluster center to which the oil viscosity belongs can be calculated. The magnitude of the deviation directly reflects the degree of abnormality in the oil viscosity at that point in time. In general, if the oil viscosity deviation at a certain time point is large, it indicates that there is a large difference between the oil viscosity at that time point and the normal state, and it may indicate that there is an abnormality in the hydraulic system, such as that the oil viscosity is too high or too low. Thereby further calculating the viscosity anomaly coefficient of the oil.
The load increment stability of the pre-loading process of the standby hydraulic pump is measured by acquiring the load increment stability information of the pre-loading process of the standby hydraulic pump, analyzing the load increment stability of the pre-loading process of the standby hydraulic pump and calculating a load increment stability coefficient;
the acquisition logic of the load increment stability coefficient is as follows:
gradually applying system load to the standby hydraulic pump until the system load of the main hydraulic pump is reached, and recording system load values at different times Response time of stand-by hydraulic pump to system load change at different times;
Calculating the load change speed: Wherein The load change rate at time t is indicated,As the system load value at time t,For the system load value at time t-1,Is a sampling time interval;
calculating response lag time: Wherein In order to respond to the lag time,To reserve the response time of the hydraulic pump to system load changes,Is the expected response time;
Calculating the load increment stable values at different times: Wherein The load representing time t is incremented by a steady value,For the transfer function of the systemIs used to determine the phase angle of (c),WhereinFor the static gain of the system,Is the time constant of the system and,In units of imaginary numbers,Is angular frequency;
It should be noted that the number of the substrates, The static gain of the system is indicative of the proportional relationship between the system input and output,The time constant of the system is a parameter (set according to the time condition) for measuring the response speed of the system,Is imaginary number unit%),The frequency of load change is represented as angular frequency;
Calculating a load increasing stability coefficient: Wherein The stability factor is incremented for the load,The steady value is incremented for the load at time r,Is the time window size;
It should be noted that, the above formulas are all dimensionality removal and numerical calculation, and common dimensionality removal methods include Min-Max normalization, Z-Score normalization, and the like, which are not described herein;
the load increment stability coefficient is used for measuring the load increment stability degree in the pre-loading process of the standby hydraulic pump. The load increment stability coefficient reflects the response stability of the hydraulic pump when the load is gradually increased, and is directly related to the working stability and reliability of the hydraulic system when the load is changed. A large incremental stability factor of the load generally indicates that the system is able to maintain a stable response during a gradual increase in load, that the performance of the hydraulic pump is not unduly affected by load fluctuations, and that the system is able to successfully carry the intended load, thereby avoiding overload, vibration, or other instability. A small load increase stability factor may suggest that the system has a response lag or instability during the load increase, which may cause problems such as overheating of the pump, pressure fluctuations or equipment failure, and even in extreme cases, may cause breakdown of the system or equipment damage. The potential loading hidden danger in the pre-loading process of the standby hydraulic pump is evaluated based on the load increment stability coefficient, so that the method has remarkable beneficial effects. First, the incremental load stability factor may provide a quantitative basis for the dynamic stability of the hydraulic system. The load increasing process of the system is generally slow at the initial stage of starting the standby hydraulic pump, and if the standby hydraulic pump cannot stably adapt to load change in the process, the failure of the main pump may not be dealt with. At the moment, the stability of the system in the load increasing process can be effectively monitored based on the evaluation of the load increasing stability coefficient, timely early warning is provided for the system, and the reliability and response speed of the standby pump are improved.
It should be noted that a stepwise increasing load is applied to the backup hydraulic pump until the load reaches a predetermined load level of the main hydraulic pump. The increase in load is stepwise with the aim of observing the response of the backup hydraulic pump at different load levels. At each load stage, the load value of the system and the response time of the standby hydraulic pump to the load change are recorded, and the load change speed at each time point is calculated. The load change speed reflects the speed of the system load change. The load change is usually gradual, but its speed of change can have a significant impact on the stability of the system. If the load changes too fast, the system may not respond in time, resulting in instability or overload. Thus, by calculating the load change rate, we can identify the trend of the load change that may cause instability of the system, and the response delay time refers to the delay time of the backup hydraulic pump in response to the load change. Because the response of the backup pump is not immediate due to the inertia of the hydraulic pump and the nature of the hydraulic oil, it is necessary to calculate the time required for the backup hydraulic pump to reach the expected response after a load change, further analyze the load step up stability of the system, and use the phase angle of the system transfer function to calculate the load step up stability value. The transfer function phase angle reflects the dynamic response characteristics of the system to load changes. The instantaneous response capability of the system to load changes can be obtained by calculating the load incremental stability values at different time points, and finally, the load incremental stability coefficient is a quantitative evaluation of the overall performance of the load incremental stability in a time window. The coefficient evaluates the adaptation of the backup hydraulic pump to the load increment by calculating the load increment stable value at each time point in the time window;
Step S3, a standby switching hidden danger risk model is constructed according to oil viscosity abnormal information and load increment stable information in the standby hydraulic pump preloading process, a standby switching hidden danger risk index is output, and potential loading hidden danger in the standby hydraulic pump preloading process is evaluated;
constructing a standby potential switching hazard risk model according to the oil viscosity abnormal coefficient and the load increment stability coefficient, and outputting a standby potential switching hazard risk index The model is based on the following formulaIn the followingRespectively representing the preset proportional coefficients of the viscosity anomaly coefficient and the load increment stability coefficient of the oil liquid, andAre all greater than 0;
It should be noted that, the above formulas are all dimensionality removal and numerical calculation, and common dimensionality removal methods include Min-Max normalization, Z-Score normalization, and the like, which are not described herein; Setting is performed according to actual conditions, for example, an expert weighting method is adopted, that is, experts in the related field are invited to determine preset scaling factors of various indexes through professional opinion investigation and comprehensive evaluation, for example, May be 0.5, 0.5;
According to the calculation expression, the larger the oil viscosity abnormality coefficient is, the smaller the load increment stability coefficient is, the larger the standby switching hidden danger index is, the higher the oil viscosity abnormality and the load increment instability are, the higher the risk exists in the preloading process of the standby hydraulic pump, the instability and even faults in the switching process can be caused, otherwise, the smaller the oil viscosity abnormality coefficient is, the larger the load increment stability coefficient is, the smaller the standby switching hidden danger index is, the relatively stable the preloading process of the standby hydraulic pump is, the main pump load can be smoothly taken over, the hidden danger in the preloading process is small, and the switching operation is continued;
In step S4, the risk index of the standby switching hidden trouble is compared with a preset risk index threshold of the standby switching hidden trouble, and the hydraulic pump is controlled in a classified manner, specifically as follows:
If the risk index of the standby switching hidden trouble is larger than the threshold value of the risk index of the standby switching hidden trouble, indicating that the high risk exists in the preloading process of the standby hydraulic pump, marking the current standby hydraulic pump as switching risk equipment, and carrying out early warning maintenance on the switching risk equipment;
If the risk index of the standby switching hidden danger is smaller than or equal to the threshold value of the risk index of the standby switching hidden danger, the preloading process of the standby hydraulic pump is relatively stable, the standby hydraulic pump which is not marked as the switching risk equipment is ranked according to the risk index of the standby switching hidden danger from small to large, a standby equipment switching ranking table is generated, and the standby equipment with the first ranking is subjected to switching operation.
According to the invention, through collecting the operation data of the main hydraulic pump and carrying out fault prediction, potential faults of the main equipment can be found in time, the pre-loading process of the standby hydraulic pump is started according to the fault occurrence time, the abnormal oil viscosity information and the load increasing stability information in the pre-loading process of the standby hydraulic pump are obtained in real time, the stability of the standby pump in the switching process is effectively evaluated based on the abnormal oil viscosity information and the load increasing stability coefficient, the working state of the standby pump is comprehensively analyzed, potential loading hidden dangers can be identified in advance when the abnormal oil viscosity or the load increasing instability occurs, corresponding control is carried out through the output of a risk model, the switching risk of the standby hydraulic pump is dynamically judged according to the comparison of the risk index of the standby switching hidden dangers with a preset threshold value, early warning and maintenance are automatically carried out under the condition of higher risk, the performance of the standby equipment is ensured to meet the switching requirement, and when the risk is lower, the standby hydraulic pump with minimum risk is preferentially selected for switching, and the operation and seamless switching of the hydraulic system are ensured.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation.
It should be understood that, in various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present application.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (3)
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
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