CN113719499B - Intelligent fault diagnosis method for electrohydraulic servo valve - Google Patents
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F15—FLUID-PRESSURE ACTUATORS; HYDRAULICS OR PNEUMATICS IN GENERAL
- F15B—SYSTEMS ACTING BY MEANS OF FLUIDS IN GENERAL; FLUID-PRESSURE ACTUATORS, e.g. SERVOMOTORS; DETAILS OF FLUID-PRESSURE SYSTEMS, NOT OTHERWISE PROVIDED FOR
- F15B19/00—Testing; Calibrating; Fault detection or monitoring; Simulation or modelling of fluid-pressure systems or apparatus not otherwise provided for
- F15B19/005—Fault detection or monitoring
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
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Abstract
The invention relates to an intelligent fault diagnosis method for an electrohydraulic servo valve, which can rapidly and accurately position fault points and comprises the following specific steps: 1) And (3) data acquisition: collecting some main parameters of the electrohydraulic servo valve, including current, oil temperature, pressure and valve core displacement; 2) Establishing a mathematical model of the electrohydraulic servo valve during normal operation: the model content is mainly the state value or an allowable range of various variables when the servo valve works normally; 3) Establishing mathematical models of typical faults of the servo valves on the basis of the step 2), so that the faults of the servo valves with higher frequency can be rapidly screened out; 4) And (3) processing the collected real-time servo valve parameters to generate a real-time model, analyzing and comparing the real-time model with the model established in the step (2), and if the model is greatly different from the standard model, comparing the model with a typical fault model, and obtaining a conclusion through the analysis and comparison of the system. The model established by the invention can effectively improve the speed and the precision of detecting the fault of the servo valve.
Description
Technical Field
The invention relates to an electrohydraulic servo valve fault diagnosis technology, in particular to an electrohydraulic servo valve intelligent fault diagnosis method.
Background
The electrohydraulic servo valve is a central nerve of an actuating mechanism of the hydraulic system, if the electrohydraulic servo valve can learn, the difference of the internal change of the system or the difference of the running state can be perceived, and the self-regulation, namely the intellectualization, is realized, so that the intellectualization electrohydraulic servo valve is comparable to the brain of a human.
At present, the on-board hydraulic system only detects the valve core position through the servo valve with a displacement sensor to judge the fault of the servo valve, and the servo valve without the displacement sensor judges the fault of the valve by detecting the change of a pressure sensor or a flow detection device in a pipeline connected with an executing mechanism (load device). Often, the functions of the hydraulic system and the onboard control part are realized by different personnel, and the difference between the professional understanding and the requirements is caused, so that the high fusion judgment is difficult to achieve. In particular to an electro-hydraulic servo valve on an aircraft, the electro-hydraulic servo valve is in extreme environments of high temperature, high pressure, strong vibration, high dynamic state and the like in operation, so that collected signals are greatly disturbed, effective information is easily submerged in noise, and the signal collection and analysis processing of the electro-hydraulic servo valve are extremely difficult.
Along with the increasing complexity of the hydraulic system of the aircraft, the number of monitoring sensors required by key components such as electrohydraulic servo valves and the like is correspondingly increased to ensure the safety and reliability of the hydraulic system of the aircraft, so that the weight of the aircraft is increased, and a series of problems such as performance reduction, oil consumption increase and the like of the aircraft are generated.
Disclosure of Invention
The invention aims to provide an intelligent fault diagnosis method for an electrohydraulic servo valve, which is used for solving the problem of judging valve faults by detecting the change of a pressure sensor or flow detection equipment in a pipeline of a connecting executing mechanism (load equipment), and simultaneously effectively improving the precision and speed of fault detection.
In order to achieve the above purpose, the technical scheme of the invention is as follows: an intelligent fault diagnosis method for an electrohydraulic servo valve can rapidly and accurately locate fault points, and comprises the following specific steps:
step 1: collecting key parameters of an electrohydraulic servo valve;
step 2: establishing a reference mathematical model according to the state of parameters of the electrohydraulic servo valve in normal operation, wherein the reference mathematical model integrates the change characteristics of various variable factors and the reference of parameter states of the electrohydraulic servo valve in actual operation for a plurality of times, so that the reference mathematical model has ultrahigh flexibility and ultrahigh generalization capability, and can rapidly discriminate the acquired parameters with abnormal states, thereby accurately analyzing fault places; in addition, in order to rapidly process the data, a parameter training set is established on the basis of a reference model and used for rapidly sorting the acquired parameters, so that the failure diagnosis efficiency is improved; the reference mathematical model is used as a reference template to be compared with the generated electrohydraulic servo valve real-time mathematical model, and analysis and operation are carried out, so that the aim of rapid diagnosis is fulfilled;
step 3: setting up a plurality of types of mathematical models of typical faults of the servo valve on the basis of the step2, and dividing the faults into typical faults and atypical faults according to the types of faults, wherein the typical faults comprise abrasion of a working edge and clamping stagnation of a valve core, the atypical faults comprise too high and too low oil temperature, too high and too low oil pressure, and the faults of a temperature sensor, a pressure sensor and a valve coil are opened;
step 4: parameter processing, establishing a real-time mathematical model, carrying out parameter training on key data acquired from an electrohydraulic servo valve, and enabling the acquired data to quickly establish the real-time mathematical model through training, so that the acquired data can be compared with an established reference mathematical model and a typical fault mathematical model;
step 5: and (3) analyzing and comparing the real-time mathematical model generated in the step (4) with the established standard mathematical model and the typical fault mathematical model, and finally obtaining a conclusion.
Further, the key parameters of the electrohydraulic servo valve collected in the step 1 include: the feedback current, the oil temperature, the pressure, the valve core displacement and the like are parameters participating in establishing a mathematical model of the servo valve during working, and the state of the parameters can display the working state of the servo valve to a certain extent.
Further, the typical fault mathematical model established in the step3 is used for diagnosing the fault of the servo valve, a matching matrix K of the deduced performance is adopted, and the matching matrix K is used for carrying out matching analysis operation, and the specific process is as follows:
step3.1: a matching matrix K is obtained and is used to calculate,
[NWM] m×n ＝[K] m×n [TFM] n×n
[NWM] m×n is a state matrix of the servo valve during normal operation, [ TFM ]] n×n Is a state matrix at the time of a typical failure,
step3.2: the content of the matching matrix K is fault-matched,
f 1 ,f 2 ,...,f m ,f n a matrix that is matched for each fault condition.
Further, the step4 processes the collected servo valve parameter data, and the process is as follows:
step4.1: noise reduction processing is carried out on the servo valve sampling data, the collected servo valve parameter data form a matrix A of m rows and n columns, singular value decomposition is carried out, and the decomposition is as follows:
wherein U represents the similarity direction between the data in each dimension, V shows the similarity degree between each piece of data, sigma is a diagonal matrix, the value on the diagonal is a singular value, the number of non-zero singular values is the rank of the matrix, T is a transposed symbol, and m and n are integers greater than 1;
when the selected dimension data are related, the singular value has zero value; if not, the singular values are all non-zero values; the selected data in each dimension are irrelevant, the difference between the singular values is larger, noise is considered to exist, the singular values smaller than the data threshold are zeroed by setting the data threshold, and the matrix is synthesized again to eliminate the noise data;
step4.2: several main parameters after noise reduction are processed,
delta is normalized valve core displacement, p is normalized pressure, T is normalized temperature, I is normalized current, delta s For sampling displacement, p s For sampling pressure, T s For sampling temperature, I s For sampling current, delta max For maximum spool displacement, delta c To correspond to a given spool displacement, p max At maximum pressure, p g For a given pressure T max For the upper temperature limit, T e At ambient temperature, I c To correspond to a given temperature;
step4.3: solving a real-time mathematical model,
in order to sample the matrix of samples,
normalizing the matrix for the model;
[R-tM] n×n is a real-time model matrix.
Further, the comparison of the real-time mathematical model and the typical fault mathematical model in the step5 is as follows:
step5.1: solving a similarity matrix k of the real-time mathematical model and the typical fault mathematical model:
[NWM] m×n ＝[k] m×n [R-tM] n×n
step5.2: the matrix K is compared with the matching matrix K,
contrast matching arrayAnd the position with very high similarity in the matrix k can locate the fault point.
The invention has the advantages and beneficial effects that:
1. the invention proposes an idea of establishing a real-time model of a servo valve: the sampling data is standardized to build a real-time servo valve model. Compared with other models, the model has less training parameters and high training speed, and effectively improves the fault diagnosis efficiency and precision of the electrohydraulic servo valve.
2. The invention can be used in the aviation field, can be also applied to other industrial fields with higher automation degree and convenient information acquisition in a conversion way, and has larger practical application value.
Drawings
FIG. 1 is a block diagram of the intelligent fault diagnosis method of the electrohydraulic servo valve;
FIG. 2 is a flow chart of a fault handling model;
fig. 3 is a model diagnostic flow chart.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
As shown in fig. 1 to 3, the intelligent fault diagnosis method of the electrohydraulic servo valve specifically comprises the following steps:
the first step, key parameters of the electrohydraulic servo valve including temperature, pressure, current and valve core displacement are collected.
And secondly, carrying out standardized processing on the acquired key parameters, and generating a real-time mathematical model of the electrohydraulic servo valve from the processed parameters.
And thirdly, carrying out matching operation on the real-time mathematical model and the established typical fault mathematical model.
And step four, outputting the fault type according to the operation result of the step three.
First, the established typical fault mathematical model in the third step is introduced, and the specific steps are as follows:
the typical fault mathematical model established in the step3 needs to diagnose the fault of the electrohydraulic servo valve, and the fault model is not used for carrying out matching operation directly, but a matching matrix K is deduced, and the matching analysis operation is carried out by using the matrix K, so that the specific process is as follows:
step3.1: a matching matrix K is obtained and is used to calculate,
[NWM] m×n ＝[K] m×n [TFM] n×n
[NWM] m×n is a state matrix of the servo valve during normal operation, [ TFM ]] n×n Is a state matrix at the time of a typical failure,
step3.2: the content of the matching matrix K is fault-matched,
f 1 ,f 2 ,...,f m ,f n for a matrix that matches each fault condition,
further, the specific steps of the fourth step of data processing are as follows:
step4.1: noise reduction processing is carried out on the electrohydraulic servo valve sampling data, the acquired electrohydraulic servo valve parameter data form a matrix A of m rows and n columns, singular value decomposition is carried out, and the decomposition is as follows:
wherein U represents the similarity direction between the data in each dimension, V shows the similarity degree between each piece of data, sigma is a diagonal matrix, the value on the diagonal is a singular value, the number of non-zero singular values is the rank of the matrix, T is a transposed symbol, and m and n are integers greater than 1;
when the selected dimension data are related, the singular value has zero value; if not, the singular values are all non-zero values; the selected dimension data are uncorrelated, the difference between the singular values is larger, noise is considered to exist, the singular values smaller than the data threshold are zeroed by setting the data threshold, and the matrix is synthesized again to eliminate the noise data.
Step4.2: the data after the noise reduction is processed,
delta is normalized valve core displacement, p is normalized pressure, T is normalized temperature, I is normalized current, delta s For sampling displacement, p s For sampling pressure, T s For sampling temperature, I s For sampling current, delta max For maximum spool displacement, delta c To correspond to a given spool displacement, p max At maximum pressure, p g For a given pressure T max For the upper temperature limit, T e At ambient temperature, I c To correspond to a given temperature.
Step4.3: solving a real-time mathematical model,
in order to sample the matrix of samples,
normalizing the matrix for the model;
[R-tM] n×n for real-time model matrix
And fifth step, comparing the real-time mathematical model with a typical fault mathematical model, wherein the process is as follows:
step5.1: solving a similarity matrix k of the real-time model and the typical fault mathematical model:
[NWM] m×n ＝[k] m×n [R-tM] n×n
step5.2: the matrix K is compared with the matching matrix K,
contrast matching arrayAnd the position with very high similarity in the matrix k can locate the fault point.
Claims (3)
1. An intelligent fault diagnosis method for an electrohydraulic servo valve can rapidly and accurately locate a fault point and is characterized by comprising the following specific steps:
step 1: collecting key parameters of an electrohydraulic servo valve; feedback current, oil temperature, pressure and valve core displacement; the parameters are the parameters participating in establishing a mathematical model of the servo valve during working, and the state of the parameters can display the working state of the servo valve to a certain extent;
step 2: carrying out standardized treatment on the collected key parameters, and generating a real-time mathematical model of the electrohydraulic servo valve from the treated parameters;
step 3: establishing a plurality of typical fault mathematical models of the servo valve on the basis of the step2, and dividing the faults into typical faults and atypical faults according to the types of faults, wherein the typical faults comprise abrasion of a working edge and clamping stagnation of a valve core, and the atypical faults comprise too high and too low oil temperature, too high and too low oil pressure, faults of a temperature sensor and a pressure sensor and open circuits of a valve coil;
step 4: performing matching operation on the real-time mathematical model and the established typical fault mathematical model;
the comparison of the real-time mathematical model with the typical fault mathematical model is as follows:
step4.1: solving a similarity matrix k of the real-time mathematical model and the typical fault mathematical model:
[NWM] m×n ＝[k] m×n [R-tM] n×n
wherein: [ NWM] m×n Is a state matrix of the servo valve during normal operation, [ R-tM ]] n×n Is a real-time model matrix;
step4.2: the matrix K is compared with the matching matrix K,
contrast matching arrayAnd the position with very high similarity in the matrix k can conveniently locate the fault point;
wherein: f (f) 1 ,f 2 ,...,f m, f n A matrix that matches each fault state;
step 5: and (4) outputting the fault type according to the operation result of the step (4).
2. The intelligent fault diagnosis method for the electrohydraulic servo valve according to claim 1, wherein: the specific process of the step2 is as follows:
step2.1: noise reduction processing is carried out on the servo valve sampling data, the collected servo valve parameter data form a matrix A of m rows and n columns, singular value decomposition is carried out, and the decomposition is as follows:
wherein U represents the similarity direction between the data in each dimension, V shows the similarity degree between each piece of data, sigma is a diagonal matrix, the value on the diagonal is a singular value, the number of non-zero singular values is the rank of the matrix, T is a transposed symbol, and m and n are integers greater than 1;
when the selected dimension data are related, the singular value has zero value; if not, the singular values are all non-zero values; the selected data in each dimension are irrelevant, the difference between the singular values is larger, noise is considered to exist, the singular values smaller than the data threshold are zeroed by setting the data threshold, and the matrix is synthesized again to eliminate the noise data;
step2.2: several main parameters after noise reduction are processed,
delta is normalized valve core displacement, p is normalized pressure, T is normalized temperature, I is normalized current, delta s For sampling displacement, p s For sampling pressure, T s For sampling temperature, I s For sampling current, delta max For maximum spool displacement, delta c To correspond to a given spool displacement, p max At maximum pressure, p g For a given pressure T max For the upper temperature limit, T e At ambient temperature, I c To correspond to a given temperature;
step2.3: solving a real-time mathematical model,
in order to sample the matrix of samples,
normalizing the matrix for the model;
[R-tM] n×n is a real-time model matrix.
3. The intelligent fault diagnosis method for the electrohydraulic servo valve according to claim 1, wherein: the typical fault mathematical model established in the step3 is used for diagnosing the fault of the servo valve, a matching matrix K is adopted for the estimation, and the matching matrix K is used for carrying out matching analysis operation, and the specific process is as follows:
step3.1: a matching matrix K is obtained and is used to calculate,
[NWM] m×n ＝[K] m×n [TFM] n×n
[NWM] m×n is a state matrix of the servo valve during normal operation, [ TFM ]] n×n For a state matrix at typical failure, step3.2: the content of the matching matrix K is fault-matched,
f 1, f 2 ,...,f m, f n a matrix that is matched for each fault condition.
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