CN109795713B - Fault diagnosis method for aileron actuator based on Simulink model - Google Patents
Fault diagnosis method for aileron actuator based on Simulink model Download PDFInfo
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Abstract
The invention discloses a fault diagnosis method for an aileron actuator based on a Simulink model, belonging to the field of fault diagnosis of a direct drive valve type redundancy aileron actuator, and mainly comprising the following steps of: analyzing the structure of the aileron actuator and building a simulation model of the aileron actuator; analyzing the occurrence probability and severity of the failure mode of the actuator, constructing a harmfulness matrix diagram, and combing the key failure mode to be diagnosed; implanting different types of faults into the actuator simulation model, and collecting fault data; analyzing the fault data characteristics, adopting different fault diagnosis methods for different types of fault modes, establishing an overall fault diagnosis rule, and finally realizing accurate fault diagnosis for the actuator so as to solve the problem of fault diagnosis of multiple fault modes of the aileron actuator.
Description
Technical Field
The invention relates to the technical field of fault diagnosis of a direct drive valve type redundancy aileron actuator, in particular to a fault diagnosis method of an aileron actuator based on a Simulink model.
Background
With the continuous development and progress of aviation technology, the precision requirement and the complexity of a flight control system are higher and higher. Once the corresponding system or equipment is out of order, huge property loss and casualties are brought, and therefore, the reliability of the flight control system needs to be continuously improved. Statistics show that a considerable part of the causes of the damage of the airplane come from faults of the flight control system, and the faults of the flight control system are caused by key components such as ailerons, elevators and the like. It follows that the ability of an aircraft to ensure proper operation depends in large part on whether the aileron actuators are operating properly. The aileron actuator needs to keep normal operation, and not only needs to meet the preset requirements on the aspects of control precision, response speed and the like, but also needs to ensure that the reliability meets the requirements. Therefore, the aileron actuator is designed to ensure the reliability by considering the redundant design. When a certain part or channel of the actuator fails, the corresponding other part or channel can be used for ensuring the normal work of the actuator. However, the redundancy is not sufficient, and the excessive redundancy ensures high reliability of operation, but increases the weight and volume of the aircraft, which is disadvantageous for the overall aircraft design. Therefore, a three-redundancy or four-redundancy design scheme is generally adopted for the aileron actuator to ensure the reliability of the system. In conclusion, the aileron actuator is used as an important execution component of the aircraft control system, and fault diagnosis of the aileron actuator is of great significance to the aspects of keeping equipment intact, ensuring the flight quality and flight safety of the aircraft, reducing maintenance and guarantee cost and the like.
The currently proposed actuator fault diagnosis methods mainly include three types:
1. the fault diagnosis method based on the model comprises the following steps: the core idea of the method is to construct a model to estimate the normal output value of the actuator, and compare the real output value of the actuator with the estimated output value to form a residual error. When the actuator works normally, the residual error is theoretically zero; and when the actuator fails, the residual is non-zero. And finally, extracting fault characteristics from the residual error signal and realizing fault diagnosis through a corresponding fault diagnosis algorithm. The models used are generally of two types: a mathematical model and an observer. The mathematical model is an accurate model which is established based on a control equation of the actuator and can completely describe a control loop of the actuator; the observer is a fitting of the nonlinear relation between the input and the output of the actuator, and commonly used observers include a kalman filter, a support vector machine, a neural network and the like.
2. The fault diagnosis method based on knowledge comprises the following steps: the method introduces a lot of knowledge and failure information of the actuator, and judges whether the actuator fails or not and a failure mode through the knowledge and experience. Common knowledge-based fault diagnosis methods are: fuzzy inference based methods and knowledge base based methods; the fault diagnosis method based on fuzzy reasoning judges the fault through the symptom according to a certain mapping relation between a fuzzy set symptom space and a fault state space, uses the concept of fuzzy logic to explain the fuzzy relation between the equipment fault phenomenon and the fault generation reason, and uses the element membership degree and the fuzzy relation equation in the fuzzy set theory to solve the fault diagnosis problem. The basic principle of the fault diagnosis method based on the knowledge base is as follows: and managing the knowledge of the diagnosis object by the knowledge library, extracting the knowledge to a fault rule set, and when the actual information is matched with a certain part of the rule, corresponding to the corresponding fault.
3. The fault diagnosis method based on data comprises the following steps: the method directly depends on the type and the characteristics of the data nodes, firstly adopts a data processing method to extract fault characteristics, and then classifies the fault characteristics by different classification methods. Compared with the fault diagnosis method based on the model, the fault diagnosis method based on the data does not need to establish a complex model, but can realize the classification of the fault by carrying out the most appropriate processing on the data.
Disclosure of Invention
In view of the above, in order to solve the above problems in the prior art, an object of the present invention is to provide a fault diagnosis method based on an aileron actuator Simulink model, so as to achieve the purpose of performing fault diagnosis on an aileron actuator by analyzing acquired aileron actuator sensor data under the condition of fixed command driving.
In order to achieve the purpose, the invention adopts the technical scheme that: a fault diagnosis method for an aileron actuator based on a Simulink model mainly comprises the following steps:
step 1: according to the structure and the working principle of the direct drive valve type aileron actuator, a complete aileron actuator simulation model is built;
step 2: analyzing the occurrence probability and severity of the failure mode of the aileron actuator, and carding out the key failure mode needing to be diagnosed;
and step 3: aiming at the key fault mode needing to be diagnosed in the step 2, implanting different types of faults into the aileron actuator simulation model to finish the acquisition of fault data;
and 4, step 4: by analyzing the characteristics of fault data under different fault modes, dividing the key fault modes to be diagnosed into four types, adopting different fault diagnosis methods aiming at different types of fault modes, and establishing an integral fault diagnosis rule on the basis;
step 41: dividing the fault into a channel fault and a non-channel fault according to whether the difference between the four-channel data exceeds a fault threshold value, and judging a specific fault channel according to the difference between every two channels;
step 42: further dividing the non-channel faults into non-hydraulic faults and hydraulic faults, and judging specific fault modes of the non-hydraulic faults by adopting a model residual error-based method;
step 43: the hydraulic fault is judged to be a left hydraulic system fault or a right hydraulic system fault according to the response delay time of the system;
step 44: comparing the hydraulic fault judgment result with a display result of the hydraulic fault detector, and judging whether the hydraulic fault detector has a fault;
step 45: and if the judgment results in the steps are all non-fault, judging that the aileron actuator is in a normal state.
Further, the aileron actuator simulation model built in the step 1 comprises a Simulink model and an AMEsim model built based on the aileron actuator structure and the control equation.
Further, in the step 2, a hazard matrix map of the failure mode of the aileron actuator is established according to the occurrence probability and the severity of the failure mode of the aileron actuator.
Further, the collecting signal of the fault data in the step 3 comprises: flight control instruction signals, force motor coil current signals, direct drive valve displacement sensor signals and actuator cylinder displacement sensor signals.
Furthermore, the current signal of the coil of the force motor, the signal of the displacement sensor of the direct drive valve and the signal of the displacement sensor of the actuating cylinder all comprise four channels of A/B/C/D.
Further, the method based on model residuals mentioned in step 42 is: establishing a residual error library based on a normal Simulink model of an aileron actuator, and then taking a Pearson correlation coefficient between a residual error signal to be diagnosed and each residual error signal in the residual error library as a classification basis, wherein a calculation formula of the Pearson correlation coefficient is as follows:
the invention has the beneficial effects that:
1. according to the invention, the Simulink and AMEstim simulation models of the actuator are built by analyzing the structure and the working principle of the aileron actuator; on the basis of establishing a hazard matrix diagram of the failure mode of the aileron actuator, combing a key failure mode needing to be diagnosed; different types of fault modes are implanted into the aileron actuator AMEstim model to complete the collection of fault data; by analyzing the characteristics of fault data under different fault modes, the fault modes to be diagnosed can be classified into four types, different fault diagnosis methods are adopted for different types of fault modes, and the integral fault diagnosis rule of the aileron actuator is established on the basis.
2. The invention is based on the diagnosis rule and core, and combines different fault diagnosis methods, can realize the fault diagnosis of multiple fault modes of the aileron actuator by a simpler and faster algorithm, and converts the algorithm into a complete Simulink model for packaging, so as to be convenient for the application of actual engineering.
Drawings
FIG. 1 is a flow chart of a method for diagnosing a fault of an aileron actuator based on a Simulink model according to the present invention;
FIG. 2 is a Simulink simulation model diagram of an aileron actuator in the aileron actuator fault diagnosis method based on the Simulink model provided by the invention;
FIG. 3 is an AMEsim simulation model diagram of an aileron actuator in the aileron actuator fault diagnosis method based on the Simulink model provided by the invention;
FIG. 4 is a hazard matrix diagram of a failure mode of an aileron actuator in the aileron actuator failure diagnosis method based on the Simulink model provided by the invention;
FIG. 5a is a diagram of a force motor coil current signal when the DDV zero offset out-of-tolerance fault value is + 1;
FIG. 5b is a diagram of a force motor coil current signal when the DDV zero offset out-of-tolerance fault value is + 2;
FIG. 6a is a signal diagram of a DDV displacement sensor with a DDV zero-offset out-of-tolerance fault value of + 1;
FIG. 6b is a signal diagram of a DDV displacement sensor with a DDV zero-offset out-of-tolerance fault value of + 2;
FIG. 7a is a diagram of the actuator displacement sensor signal when the DDV zero offset out-of-tolerance fault value is + 1;
FIG. 7b is a diagram of the actuator displacement sensor signal when the DDV zero offset out-of-tolerance fault value is + 2;
FIG. 8 is an overall diagnosis rule of an aileron actuator diagnosis model in the aileron actuator fault diagnosis method based on the Simulink model provided by the invention;
FIG. 9 is a flow chart of channel fault diagnosis in the method for diagnosing faults of an aileron actuator based on a Simulink model provided by the invention;
FIG. 10 is a diagnostic flowchart of a method for diagnosing faults based on model residuals in a method for diagnosing faults of an aileron actuator based on a Simulink model according to the present invention;
FIG. 11 is a flow chart of hydraulic fault diagnosis in the method for diagnosing faults of an aileron actuator based on a Simulink model provided by the invention;
FIG. 12 is the aileron actuator fault diagnosis Simulink model established in example 1;
fig. 13 is the fault data to be diagnosed in embodiment 1;
fig. 14 is a failure diagnosis result in embodiment 1;
fig. 15 is the confidence of the failure diagnosis result in embodiment 1.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention.
Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In order to solve the problem of fault diagnosis of an aileron actuator under a fixed instruction, the invention provides a fault diagnosis method based on a Simulink model, as shown in FIG. 1, which comprises the following steps:
step 1: according to the structure and the working principle of the direct drive valve type aileron actuator, a Simulink simulation model and an AMEstim simulation model of the aileron actuator are built.
Step 2: and (3) establishing a hazard matrix diagram of the failure modes of the aileron actuator according to the occurrence probability and the severity of the failure modes of the aileron actuator, and combing 20 key failure modes needing to be diagnosed.
And step 3: aiming at the key fault mode combed in the step 2, implanting different types of faults into an AMEstim model of the aileron actuator so as to complete the collection of fault data; wherein, the acquisition signal to the fault data in this step includes: flight control command signals, force motor coil current signals (A/B/C/D four channels), second-stage direct drive valve displacement sensor signals (A/B/C/D four channels) and actuator displacement sensor signals (A/B/C/D four channels).
And 4, step 4: and (3) aiming at the fault data acquired in the step (3), classifying the key fault modes to be diagnosed into four categories by analyzing the characteristics of the fault data under different fault modes, adopting different fault diagnosis methods for different types of fault models, establishing an integral fault diagnosis rule on the basis, and finally obtaining a fault diagnosis result.
Example 1
The invention is further illustrated by example 1. Embodiment 1 specifically provides a fault diagnosis method based on an aileron actuator Simulink model, which specifically includes the following steps:
step 1: according to the structure and the working principle of the aileron actuator, a Simulink simulation model and an AMEstim simulation model of the aileron actuator are built. The aileron actuator selected in the embodiment is a direct drive valve type aileron actuator, and consists of three parts, namely a servo control loop, a hydraulic system and a mechanical system. The servo control loop receives an instruction from a flight control computer and realizes the control of the working mode of the aileron actuator according to the control law requirement; the hydraulic system is generally composed of an electromagnetic valve, a servo valve, a mode conversion valve and the like, and realizes the functions of hydraulic supply and regulation; the mechanical system mainly comprises various execution components such as an actuating cylinder and the like, and finally executes the received flight control instruction. In order to ensure the working reliability of the aileron actuator, the direct drive valve type aileron actuator adopts the design of electric redundancy, mechanical redundancy and hydraulic redundancy. When the aileron actuator works normally, the flight control command is converted by the servo control loop and transmitted to a force motor for directly driving a valve (DDV) and driving a DDV valve core to move, and load flow brought by the valve core movement is returned to the middle valve through mode selection and acts on two cavities of the double-cavity tandem type actuating cylinder respectively to push the actuating cylinder to move. Four-redundancy linear displacement sensors are respectively arranged on the DDV and the actuating cylinder to form closed-loop control of an inner loop and an outer loop. The main structure of the servo control circuit is shown in FIG. 2, and the main structure of the hydraulic system is shown in FIG. 3.
Step 2: the failure modes of the aileron actuator are numerous, and failure diagnosis for each failure mode is impossible, so that the failure modes need to be combed. Based on the probability of occurrence and severity of the aileron actuator failure mode, a hazard matrix map of aileron actuator failure modes may be established, as shown in FIG. 4.
In the hazard matrix map, the distribution of failure modes may be divided into four quadrants. The failure modes in the first quadrant have high occurrence probability and high severity, and once the failure modes occur, the overall performance of the aileron actuator is greatly influenced, so redundancy design needs to be adopted for improvement in design aiming at the failure modes; the failure modes in the second quadrant, although less severe, have a high probability of occurrence, and in order to ensure high reliability of operation of the aileron actuator, these failure modes need to be taken into account; the fault modes in the third quadrant have low occurrence probability and low severity, are considered in terms of economy and algorithm efficiency, and generally do not carry out fault diagnosis; the failure mode in the fourth quadrant has a high severity although the occurrence probability is low, and once it occurs, it has a great influence on the aileron actuator, and therefore such a failure also requires failure diagnosis. In summary, key failure modes requiring failure in 20 are combed out as shown in Table 1.
Table 120 failure modes to be diagnosed
And step 3: and (3) aiming at the key fault mode combed in the step (2), changing parameters of each structural part of the aileron actuator in the aileron actuator AMEstim model, and respectively simulating different fault modes of the aileron actuator, as shown in a table 1 and a figure 4. Through the implantation of different failure modes, corresponding failure data can be obtained, and the collected data comprises 4 types as shown in table 2. The aileron actuator is an electrical quad design with 4 electrical channels, so the collected MOT, DDV and RAM also contain four channels of data. The function of the hydraulic fault monitor (FD) is to report a fault when the hydraulic pressure of the hydraulic system is lost, and the judgment of the FD fault is a logic judgment depending on whether the fault diagnosis result is consistent with the fault report information, so that the simulation of fault data is not needed. In the embodiment, the method for diagnosing the faults of the aileron actuator under the fixed instruction is adopted, and the instructions used for acquiring the fault data are periodic square wave signals with the frequency of 1Hz and the amplitude of +/-5V. Taking the DDV zero-offset out-of-tolerance fault mode as an example, three types of output signals under different fault degrees are obtained as shown in fig. 5 a-5 b, fig. 6 a-6 b, and fig. 7 a-7 b.
Table 24 type data collection
And 4, step 4: and (4) comparing and analyzing the fault data collected in the step (3) and searching the characteristics of the fault data in different fault modes. According to the characteristics expressed by the fault data, the faults are divided into two types: channel faults and non-channel faults; the non-channel fault can be further judged to be a non-hydraulic fault and a hydraulic fault. Different fault diagnosis methods are adopted for distinguishing each type of fault, and a final fault diagnosis result can be obtained. The overall fault diagnosis rule established from the above analysis is shown in fig. 8.
The channel fault means that one of the 4 electrical channels has a fault, and therefore the control precision of the whole aileron actuator is influenced. Channel failures are of three types: the force motor coil is disconnected, the tracking precision of the DDV displacement sensor is out of tolerance, and the tracking precision of the actuator cylinder displacement sensor is out of tolerance. The characteristics exhibited by the fault data for these three fault modes can be differentiated according to the diagnostic rules of fig. 9. And recording the absolute value of the maximum difference between every two A/B/C/D four-channel data as delta, wherein if delta exceeds a fault threshold, delta is 1, and otherwise, delta is 0. The determination of a particular failed channel may be implemented according to table 3.
TABLE 3 concrete failure path determination method
The non-hydraulic fault is judged by adopting a fault diagnosis method based on model residual errors, and the method is characterized in that a residual error library is constructed. And simulating the fault actuator by changing the middle part value of the normal aileron actuator Simulink model or introducing a numerical offset module to construct a fault actuator model. Aiming at four fault modes of DDV zero deviation out-of-tolerance, DDV displacement sensor position accuracy out-of-tolerance, actuator cylinder zero deviation out-of-tolerance and actuator cylinder displacement sensor position accuracy out-of-tolerance, four different fault models are constructed. Meanwhile, a normal aileron actuator Simulink model is implanted into the diagnosis model, and the data generated by the model is respectively subtracted from the data generated by the four fault models, so that four different residual errors can be obtained: residual 1, residual 2, residual 3 and residual 4, namely a constructed residual library. And inputting the data source with diagnosis into the diagnosis model, and subtracting the data source with diagnosis from the data generated by the normal aileron actuator Simulink model to obtain a residual error to be diagnosed. And then, solving correlation coefficients between the residual to be diagnosed and the four residuals in the residual library, wherein the fault mode corresponding to the maximum correlation coefficient is the fault mode of the data to be diagnosed. The flow of the fault diagnosis method based on model residuals is shown in fig. 10. The calculation method of the correlation coefficient adopts a Pearson correlation coefficient, and the calculation formula is as follows:
the aileron actuator is designed by double hydraulic systems, and the structures of the left hydraulic system and the right hydraulic system are not consistent. The influence of the hydraulic pressure drop of the left system on the actuator is relatively small, and the influence of the hydraulic pressure drop of the right system on the actuator is large. By the characteristic, the specific fault hydraulic system can be judged according to the residual error between the actuator displacement sensor data and the normal data. The hydraulic failure determination flow is shown in fig. 11. The fault detector for the left hydraulic system is FD1 and the fault detector for the right hydraulic system is FD 2. If the FD1 reports a fault when the left hydraulic system is determined to be fault-free, the FD1 is determined to be fault; similarly, if it is determined that the right hydraulic system is not in failure and FD2 reports a failure, FD2 is determined to be in failure. If all the above diagnosis results are non-failure, it is determined that the aileron actuator is in a normal state.
And 5: and establishing a complete aileron actuator fault diagnosis model based on the diagnosis rules and the diagnosis algorithm and based on the Simulink platform. As shown in fig. 12, the inputs of the aileron actuator fault diagnosis model are flight control instructions and data sources to be diagnosed, and the judgment is performed by the corresponding judgment module according to the fault classification rule; adopting different fault diagnosis algorithms to carry out fault diagnosis aiming at different types of fault modes; obtaining a final fault diagnosis result and a diagnosis result confidence coefficient; and finally, displaying the diagnosis result through a display module.
In this embodiment, to check the feasibility of the diagnostic model, fault diagnosis is performed for two fault modes, namely DDV zero-offset overshoot and actuator zero-offset overshoot. In the diagnostic model, the fault number given to the DDV zero offset out is 5, and the fault number given to the ram zero offset out is 10. The data to be diagnosed consists of two sections of fault data, namely fault data of DDV zero deviation and over-tolerance (in a fault mode 5) in 0-2s and fault data of actuating cylinder zero deviation and over-tolerance (in a fault mode 10) in 2-4s, and signals of a DDV displacement sensor in the data with diagnosis are shown in figure 13. The data to be diagnosed is sent to the fault diagnosis model, and the diagnosis result given by the fault diagnosis model is shown in fig. 14 and fig. 15. Since the fault diagnosis result needs to be given based on data accumulated for a while, 0-2s are in the data accumulation stage, there is a delay of 2s in the diagnosis result. As can be seen from the figure, the fault diagnosis model gives accurate fault diagnosis results for both different fault modes.
It is to be noted that the foregoing is only illustrative of some of the principles of the invention, since numerous modifications and variations will readily occur to those skilled in the art. Therefore, it is intended that the present disclosure not be limited to the exact construction and operation illustrated and described, but that all modifications and equivalents that may be resorted to are intended to fall within the scope of the invention as claimed.
Claims (5)
1. A fault diagnosis method for an aileron actuator based on a Simulink model is characterized by mainly comprising the following steps of:
step 1: according to the structure and the working principle of the direct drive valve type aileron actuator, a complete aileron actuator simulation model is built;
step 2: analyzing the occurrence probability and severity of the failure mode of the aileron actuator, and carding out the key failure mode needing to be diagnosed;
and step 3: aiming at the key fault mode needing to be diagnosed in the step 2, implanting different types of faults into the aileron actuator simulation model to finish the acquisition of fault data;
and 4, step 4: by analyzing the characteristics of fault data under different fault modes, dividing the key fault modes to be diagnosed into four types, adopting different fault diagnosis methods aiming at different types of fault modes, and establishing an integral fault diagnosis rule on the basis;
step 41: dividing the fault into a channel fault and a non-channel fault according to whether the difference between the four-channel data exceeds a fault threshold value, and judging a specific fault channel according to the difference between every two channels;
step 42: further dividing the non-channel faults into non-hydraulic faults and hydraulic faults, and judging specific fault modes of the non-hydraulic faults by adopting a model residual error-based method;
step 43: the hydraulic fault is judged to be a left hydraulic system fault or a right hydraulic system fault according to the response delay time of the system;
step 44: comparing the hydraulic fault judgment result with a display result of the hydraulic fault detector, and judging whether the hydraulic fault detector has a fault;
step 45: and if the judgment results in the steps are all non-fault, judging that the aileron actuator is in a normal state.
2. The aileron actuator fault diagnosis method based on the Simulink model according to claim 1, characterized in that the aileron actuator simulation model constructed in the step 1 comprises the Simulink model and the AMEsim model constructed based on the aileron actuator structure and the control equation.
3. The method for diagnosing the failure of the aileron actuator based on the Simulink model according to claim 1, wherein the hazard matrix map of the failure mode of the aileron actuator is established in the step 2 according to the occurrence probability and the severity of the failure mode of the aileron actuator.
4. The method for diagnosing the fault of the aileron actuator based on the Simulink model according to claim 1, wherein the step 3 of collecting the fault data comprises the following steps: flight control instruction signals, force motor coil current signals, direct drive valve displacement sensor signals and actuator cylinder displacement sensor signals.
5. The Simulink model-based aileron actuator fault diagnosis method of claim 4, wherein the force motor coil current signal, the direct drive valve displacement sensor signal, and the actuator displacement sensor signal each include four A/B/C/D channels.
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