Civil aviation fault monitoring method and system based on big data

Abstract

The invention discloses a big data-based civil aviation fault monitoring method and system. The method comprises the steps of data acquisition, healthy civil aviation state modeling, to-be-monitored civil aviation modeling, synchronous error modeling, self-adaptive controller design and civil aviation fault monitoring. The invention belongs to the field of fault monitoring, and particularly relates to a civil aviation fault monitoring method and system based on big data. According to the scheme, a fractional derivative is sampled to capture complex nonlinearity existing in civil aviation operation, a fuzzy rule is introduced, external disturbance terms and fault-related nonlinear terms are added, and the misjudgment risk caused by noise deviation is reduced; control input is subjected to saturation smoothing processing, and the response stability of the civil aviation actuator is improved; by defining a civil aviation health state model and a synchronization error, differences caused by parameter uncertainty, external disturbance and control input changes are captured; a dynamic compensation item and a condition pause item are introduced to adapt to the stability in a dynamic civil aviation environment, and sudden change of civil aviation control signals is avoided; and the civil aviation fault monitoring effect is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of fault monitoring, and in particular to a civil aviation fault monitoring method and system based on big data. Background Art

[0002] Civil aviation fault monitoring methods are a general term for the evolution from traditional periodic inspections to modern data-driven and automated technologies. These methods utilize different technical approaches to monitor aircraft health. However, typical civil aviation fault monitoring methods suffer from poor adaptability to small changes caused by environmental disturbances, actuator limitations, and sensor noise, resulting in insufficient fault detection sensitivity. They also struggle to fully capture changes in synchronization errors caused by faults or parameter deviations, leading to decreased control performance when civil aviation actuators reach their limits. This, in turn, results in poor civil aviation fault monitoring effectiveness. Summary of the Invention

[0003] In view of the above situation, in order to overcome the defects of the prior art, the present invention provides a civil aviation fault monitoring method and system based on big data. In view of the problem that the general civil aviation fault monitoring method has poor adaptability to small changes caused by environmental disturbances, actuator limitations and sensor noise, resulting in insufficient fault detection sensitivity, this scheme samples fractional derivatives to capture the complex nonlinearity and historical memory effects existing in civil aviation operations, introduces fuzzy rules to describe the dynamic characteristics of civil aviation under different civil aviation operating conditions, adds external disturbance terms and fault-related nonlinear terms, and reduces the risk of misjudgment due to noise deviation; adopts saturation smoothing processing for control input to reduce the violent fluctuation of control instructions and improve the response of civil aviation actuators. The stability should be achieved; thereby ensuring the sensitivity of fault monitoring; in view of the problem that general civil aviation fault monitoring methods are difficult to fully capture the changes in synchronization errors caused by faults or parameter deviations, and the control performance decreases when the civil aviation actuator reaches its limit, which leads to poor civil aviation fault monitoring effects, this scheme defines the synchronization error between the civil aviation health state model and the civil aviation state model to be monitored, captures the differences caused by parameter uncertainty, external disturbances and control input changes, makes fault monitoring more sensitive, and reduces the risk of misjudgment; by introducing dynamic compensation terms and conditional pause terms, it adapts to the stability in a dynamic civil aviation environment and avoids sudden changes in civil aviation control signals; thereby improving the civil aviation fault monitoring effect.

[0004] The technical solution adopted by the present invention is as follows: The civil aviation fault monitoring method based on big data provided by the present invention comprises the following steps:

[0005] Step S1: data collection;

[0006] Step S2: Establish a healthy civil aviation status model;

[0007] Step S3: Establish a civil aviation model to be monitored;

[0008] Step S4: synchronization error modeling;

[0009] Step S5: designing an adaptive controller;

[0010] Step S6: Civil aviation fault monitoring.

[0011] Furthermore, in step S1, the data collection is to collect civil aviation operation monitoring data; the civil aviation operation monitoring data includes civil aviation operation status data, environmental data and operating parameter data.

[0012] Furthermore, in step S2, the healthy civil aviation state model is expressed as: ;in, is a fractional derivative; is the state vector of civil aviation in a healthy state; L is the total number of fuzzy rules; i is the fuzzy rule index; is the membership function based on the civil aviation state variable z under the i-th fuzzy rule; Is a health model with The parameter matrix of yes Parameter uncertainty; is the parameter matrix related to external disturbance in the healthy state; yes Parameter uncertainty; is an external stimulus signal; Used to describe the nonlinear dynamics under healthy civil aviation conditions.

[0013] Furthermore, in step S3, the civil aviation model to be monitored is represented as: ; ;in, It is the real status of civil aviation to be monitored; and is the uncertainty parameter, For state-related parameter compensation, Compensation for parameters related to external excitation; is the input saturation function; u is the command signal; is the external disturbance term; Used to describe the nonlinear changes related to faults in the civil aviation to be monitored; and are the upper and lower limits of the control input respectively; is the hyperbolic tangent function.

[0014] Furthermore, in step S4, the synchronization error modeling is to define the synchronization error between establishing a healthy civil aviation state model and establishing a civil aviation model to be monitored. , the error dynamics is expressed as: ; The parameter estimation error is defined as: ; ;in, reflects the deviation between the uncertainty about the state-related parameters and the estimated value; It reflects the deviation between the uncertainty of the parameters related to external excitation and the estimated value.

[0015] Furthermore, in step S5, the adaptive controller is designed to achieve synchronization between the healthy model and the model to be monitored; a dynamic compensation term is introduced and conditional pause items , the final control law formula is expressed as: ; ; An adaptive update law is introduced to estimate the uncertain parameters in the system, which is expressed as: ; ; ; ; ; ;in, is the control gain matrix; is the robust control coefficient, with the sign function Provide sliding mode compensation; 、 and is the design constant; is the L1 norm; and is the update speed adjustment coefficient; 、 and is the gain correction parameter; is the compensation gain.

[0016] Furthermore, in step S6, the civil aviation fault monitoring is to collect the actual system status and calculate the synchronization error e; set the error threshold ,when When the civil aviation monitoring result is a fault, early warning processing will be carried out.

[0017] The civil aviation fault monitoring system based on big data provided by the present invention includes a data acquisition module, a healthy civil aviation state model establishment module, a civil aviation model establishment module to be monitored, a synchronization error modeling module, an adaptive controller design module and a civil aviation fault monitoring module;

[0018] The data acquisition module collects civil aviation operation monitoring data;

[0019] The healthy civil aviation state model building module uses fractional order modeling and fuzzy logic to build a healthy civil aviation state model;

[0020] The module for establishing a model of civil aviation to be monitored establishes a model of civil aviation to be monitored based on nonlinear changes caused by input saturation, external disturbances, sensor noise and faults;

[0021] The synchronization error modeling module defines and models the synchronization error between the healthy state and the state to be monitored, taking into account parameter estimation errors and dynamic disturbances;

[0022] The adaptive controller design module achieves synchronization between models through online parameter update, dynamic compensation and conditional pause mechanism;

[0023] The civil aviation fault monitoring module performs fault monitoring based on synchronization error.

[0024] The beneficial effects achieved by the present invention using the above scheme are as follows:

[0025] (1) In view of the problem that general civil aviation fault monitoring methods have poor adaptability to small changes caused by environmental disturbances, actuator limitations and sensor noise, resulting in insufficient fault detection sensitivity, this scheme samples fractional derivatives to capture the complex nonlinearity and historical memory effects existing in civil aviation operations, introduces fuzzy rules to describe the dynamic characteristics of civil aviation under different civil aviation operating conditions, adds external disturbance terms and fault-related nonlinear terms, and reduces the risk of misjudgment due to noise deviation; uses saturation smoothing processing on the control input to reduce the sharp fluctuation of the control command and improve the response stability of the civil aviation actuator; thereby ensuring the sensitivity of fault monitoring.

[0026] (2) In view of the problem that general civil aviation fault monitoring methods are difficult to fully capture the changes in synchronization errors caused by faults or parameter deviations, and the control performance decreases when the civil aviation actuator reaches its limit, which leads to poor civil aviation fault monitoring results, this scheme defines the synchronization error between the civil aviation health state model and the civil aviation state model to be monitored, and captures the differences caused by parameter uncertainty, external disturbances and control input changes, making fault monitoring more sensitive and reducing the risk of misjudgment; by introducing dynamic compensation terms and conditional pause terms, it adapts to the stability in the dynamic civil aviation environment and avoids sudden changes in civil aviation control signals; thereby improving the civil aviation fault monitoring effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 A schematic diagram of the process of the civil aviation fault monitoring method based on big data provided by the present invention;

[0028] Figure 2 This is a schematic diagram of the civil aviation fault monitoring system based on big data provided by the present invention.

[0029] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention. DETAILED DESCRIPTION

[0030] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0031] In the description of the present invention, it should be understood that terms such as "up", "down", "front", "back", "left", "right", "top", "bottom", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the system or element referred to must have a specific direction, be constructed and operated in a specific direction. Therefore, they should not be understood as limiting the present invention.

[0032] Example 1, see Figure 1 The present invention provides a civil aviation fault monitoring method based on big data, which includes the following steps:

[0033] Step S1: Data collection: collecting civil aviation operation monitoring data;

[0034] Step S2: Establish a healthy civil aviation state model; use fractional order modeling and fuzzy logic to construct a healthy civil aviation state model;

[0035] Step S3: Establishing a model of the civil aviation to be monitored; establishing a model of the civil aviation to be monitored based on nonlinear changes caused by input saturation, external disturbances, sensor noise, and faults;

[0036] Step S4: Synchronization error modeling: define and model the synchronization error between the healthy state and the state to be monitored, taking into account parameter estimation errors and dynamic disturbances;

[0037] Step S5: Design an adaptive controller; achieve synchronization between models through online parameter update, dynamic compensation and conditional pause mechanism;

[0038] Step S6: Civil aviation fault monitoring: Fault monitoring is performed based on synchronization error.

[0039] Example 2, see Figure 1 This embodiment is based on the above embodiment. In step S1, the civil aviation operation monitoring data includes civil aviation operation status data, environmental data and operating parameter data.

[0040] Example 3, see Figure 1 This embodiment is based on the above embodiment. In step S2, a healthy civil aviation state model is established to describe the dynamics of civil aviation under normal conditions. Fractional order is used to capture the complex nonlinearity and memory effects of civil aviation, so that the model can more accurately reflect the operating characteristics of civil aviation under normal conditions. The healthy civil aviation state model is expressed as: ;in, is a fractional derivative, and the Caputo derivative is used; is the state vector of civil aviation in a healthy state; L is the total number of fuzzy rules; i is the fuzzy rule index; is the membership function based on the civil aviation state variable z under the i-th fuzzy rule; Is a health model with The parameter matrix reflects the inherent dynamics of the system; yes The parameter uncertainty describes the small disturbance within the normal range; is the parameter matrix related to external disturbance in the healthy state; yes Parameter uncertainty; It is an external excitation signal, which is a known disturbance introduced by the environment and operation during normal operation; Used to describe the nonlinear dynamics under healthy civil aviation conditions; fuzzy rules use fuzzy logic to describe the dynamic characteristics of civil aviation under different civil aviation conditions.

[0041] Example 4, see Figure 1 This embodiment is based on the above embodiment. In step S3, the civil aviation model to be monitored is established to model and describe the civil aviation state that may have faults or parameter deviations. The input saturation and external disturbances existing in actual measurements are taken into account. The dynamic changes introduced by sensor noise, actuator saturation, and faults are comprehensively considered to ensure that the model is still valid under actual working conditions. The saturation function is smoothed to avoid sudden changes. The civil aviation model to be monitored is expressed as: ; ;in, It is the real status of civil aviation to be monitored; and It is an uncertainty parameter estimated online by an adaptive algorithm to compensate for the difference between the healthy model and the model to be monitored. For state-related parameter compensation, Compensation for parameters related to external excitation; is the input saturation function, which indicates the control input truncation caused by the actuator limitation; u is the command signal, which acts on the civil aviation through the actuator to adjust the dynamic behavior of the civil aviation and compensate for the state deviation; is the external disturbance term, reflecting sensor noise, environmental interference and other external influences; Used to describe the nonlinear changes related to faults in the civil aviation to be monitored; and are the upper and lower limits of the control input respectively; It is a hyperbolic tangent function; the actuator adjusts the attitude, speed and direction of civil aviation through adjusting components.

[0042] By performing the above operations, in order to address the problem that general civil aviation fault monitoring methods have poor adaptability to small changes caused by environmental disturbances, actuator limitations and sensor noise, resulting in insufficient fault detection sensitivity, this scheme samples fractional-order derivatives to capture the complex nonlinearities and historical memory effects existing in civil aviation operations, introduces fuzzy rules to describe the dynamic characteristics of civil aviation under different civil aviation operating conditions, adds external disturbance terms and fault-related nonlinear terms, and reduces the risk of misjudgment due to noise deviation; adopts saturation smoothing processing for control inputs to reduce the sharp fluctuations of control commands and improve the response stability of civil aviation actuators, thereby ensuring the sensitivity of fault monitoring.

[0043] Example 5, see Figure 1 This embodiment is based on the above embodiment. In step S4, the synchronous error modeling is to define the synchronous error between establishing a healthy civil aviation state model and establishing a civil aviation model to be monitored. , the error dynamics is expressed as: ; The parameter estimation error is defined as: ; ;in, reflects the deviation between the uncertainty about the state-related parameters and the estimated value; It reflects the deviation between the uncertainty of the parameters related to external excitation and the estimated value.

[0044] Example 6, see Figure 1 This embodiment is based on the above embodiment. In step S5, the adaptive controller is designed to achieve synchronization between the healthy model and the monitored model in the presence of input saturation, external disturbances and faults, thereby ensuring the accuracy and real-time nature of fault detection; by updating parameters online and adaptively, the system uncertainty is compensated and the reliability of civil aviation safety monitoring is improved; the dynamic compensation term is introduced. and conditional pause items , which prevents the controller performance from degrading when the input is saturated, ensuring the stability and synchronization performance of the civil aviation health monitoring system; the final control law formula is expressed as: ; ; An adaptive update law is introduced to estimate the uncertain parameters in the system, which is expressed as: ; ; ; ; ; ;in, is the control gain matrix, which is used to adjust the error compensation between the healthy state and the actual state; is the robust control coefficient, with the sign function Provide sliding mode compensation; 、 and is a design constant used to adjust the update rate of the adaptive law; is the L1 norm; and is the update speed adjustment coefficient; 、 and is the gain correction parameter; is the compensation gain.

[0045] By performing the above operations, the general civil aviation fault monitoring method has difficulty in fully capturing the changes in synchronization errors caused by faults or parameter deviations, and the control performance degrades when the civil aviation actuator reaches its limit, which leads to poor civil aviation fault monitoring results. This solution defines the synchronization error between the civil aviation health state model and the civil aviation state model to be monitored, captures the differences caused by parameter uncertainty, external disturbances and control input changes, makes fault monitoring more sensitive, and reduces the risk of misjudgment; by introducing dynamic compensation terms and conditional pause terms, it adapts to the stability in the dynamic civil aviation environment and avoids sudden changes in civil aviation control signals, thereby improving the civil aviation fault monitoring effect.

[0046] Example 7, see Figure 1 This embodiment is based on the above embodiment. In step S6, the civil aviation fault monitoring is to collect the actual system status and calculate the synchronization error e; set the error threshold ,when When the civil aviation monitoring result is a fault, early warning processing will be carried out.

[0047] Example 8, see Figure 2 This embodiment is based on the above embodiment. The civil aviation fault monitoring system based on big data provided by the present invention includes a data acquisition module, a healthy civil aviation state model establishment module, a civil aviation model establishment module to be monitored, a synchronization error modeling module, an adaptive controller design module and a civil aviation fault monitoring module;

[0048] The data acquisition module collects civil aviation operation monitoring data;

[0049] The healthy civil aviation state model building module uses fractional order modeling and fuzzy logic to build a healthy civil aviation state model;

[0050] The module for establishing a model of civil aviation to be monitored establishes a model of civil aviation to be monitored based on nonlinear changes caused by input saturation, external disturbances, sensor noise and faults;

[0051] The synchronization error modeling module defines and models the synchronization error between the healthy state and the state to be monitored, taking into account parameter estimation errors and dynamic disturbances;

[0052] The adaptive controller design module achieves synchronization between models through online parameter update, dynamic compensation and conditional pause mechanism;

[0053] The civil aviation fault monitoring module performs fault monitoring based on synchronization error.

[0054] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0055] While the embodiments of the present invention have been shown and described, it will be apparent to those skilled in the art that various changes, modifications, substitutions, and alterations can be made to the embodiments without departing from the principles and spirit of the invention.

[0056] The present invention and its embodiments are described above. This description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if a person skilled in the art is inspired by this and, without departing from the purpose of the present invention, designs structures and embodiments similar to this technical solution without inventiveness, they shall fall within the scope of protection of the present invention.

Claims

1. A civil aviation fault monitoring method based on big data, characterized by: The method comprises the following steps: Step S1: Data collection: collecting civil aviation operation monitoring data; Step S2: Establish a healthy civil aviation status model; Step S3: Establish a civil aviation model to be monitored; Step S4: synchronization error modeling; Step S5: designing an adaptive controller; Step S6: Civil aviation fault monitoring.

2. The civil aviation fault monitoring method based on big data according to claim 1, characterized in that: In step S2, the healthy civil aviation state model is expressed as: ;in, is a fractional derivative; is the state vector of civil aviation in a healthy state; L is the total number of fuzzy rules; i is the fuzzy rule index; is the membership function based on the civil aviation state variable z under the i-th fuzzy rule; Is a health model with The parameter matrix of yes Parameter uncertainty; is the parameter matrix related to external disturbance in the healthy state; yes Parameter uncertainty; is an external stimulus signal; Used to describe the nonlinear dynamics under healthy civil aviation conditions.

3. The civil aviation fault monitoring method based on big data according to claim 2, characterized in that: In step S3, the civil aviation model to be monitored is expressed as: ； ;in, It is the real status of civil aviation to be monitored; and is the uncertainty parameter, For state-related parameter compensation, Compensation for parameters related to external excitation; is the input saturation function; u is the command signal; is the external disturbance term; Used to describe the nonlinear changes related to faults in the civil aviation to be monitored; and are the upper and lower limits of the control input respectively; is the hyperbolic tangent function.

4. The civil aviation fault monitoring method based on big data according to claim 1, characterized in that: In step S4, the synchronous error modeling is to define the synchronous error between the healthy civil aviation state modeling and the civil aviation modeling to be monitored. , the error dynamics is expressed as: ; The parameter estimation error is defined as: ; ;in, reflects the deviation between the uncertainty about the state-related parameters and the estimated value; It reflects the deviation between the uncertainty of the parameters related to external excitation and the estimated value.

5. The civil aviation fault monitoring method based on big data according to claim 1, characterized in that: In step S5, the adaptive controller is designed to achieve synchronization between the healthy model and the model to be monitored; the dynamic compensation term is introduced and conditional pause items , the final control law formula is expressed as: ； ; An adaptive update law is introduced to estimate the uncertain parameters in the system, which is expressed as: ; ; ; ; ; ;in, is the control gain matrix; is the robust control coefficient, with the sign function Provide sliding mode compensation; 、 and is the design constant; is the L1 norm; and is the update speed adjustment coefficient; 、 and is the gain correction parameter; is the compensation gain.

6. The civil aviation fault monitoring method based on big data according to claim 1, characterized in that: In step S6, the civil aviation fault monitoring is to collect the actual system status and calculate the synchronization error e; set the error threshold ,when When the civil aviation monitoring result is a fault, early warning processing will be carried out.

7. A civil aviation fault monitoring system based on big data, for implementing the civil aviation fault monitoring method based on big data as claimed in any one of claims 1 to 6, characterized in that: It includes data acquisition module, healthy civil aviation state model building module, monitored civil aviation model building module, synchronization error modeling module, adaptive controller design module and civil aviation fault monitoring module; The data acquisition module collects civil aviation operation monitoring data; The healthy civil aviation state model building module uses fractional order modeling and fuzzy logic to build a healthy civil aviation state model; The module for establishing a model of civil aviation to be monitored establishes a model of civil aviation to be monitored based on nonlinear changes caused by input saturation, external disturbances, sensor noise and faults; The synchronization error modeling module defines and models the synchronization error between the healthy state and the state to be monitored, taking into account parameter estimation errors and dynamic disturbances; The adaptive controller design module achieves synchronization between models through online parameter update, dynamic compensation and conditional pause mechanism; The civil aviation fault monitoring module performs fault monitoring based on synchronization error.

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