Avionics equipment fault diagnosis and prediction system based on artificial intelligence

Abstract

The invention discloses an avionic equipment fault diagnosis and prediction system based on artificial intelligence, and relates to the technical field of avionic equipment fault diagnosis, and the system comprises a receiving module, an obtaining module, an abnormal data generation module, a common cause probability generation module and a correlation analysis module. Generating abnormal data according to the sensing data, generating a common cause failure probability value according to the external environment data and a pre-constructed causal topology model, performing association analysis on the abnormal data and the common cause failure probability value, and judging whether the abnormal data is caused by a single-point hardware fault or environmental factor interference; the method has the beneficial effects that a single-point hardware fault and environmental interference can be distinguished, and the reliability of fault diagnosis of the avionics equipment is improved.

Description

Technical Field

[0001] This invention relates to the field of avionics equipment fault diagnosis technology, and in particular to an avionics equipment fault diagnosis and prediction system based on artificial intelligence. Background Technology

[0002] As an important component of modern civil aircraft, the operational status of avionics equipment directly affects flight safety and economy. With the increasing complexity of airborne systems, how to effectively diagnose and predict faults in key avionics equipment has become an important issue in aviation operation support.

[0003] In existing technologies, fault diagnosis for avionics equipment mainly adopts a fault detection mechanism based on redundant sensor voting. Its core principle is to configure multiple redundant sensors for key parameters and make decisions by comparing the readings of these sensors and following the "majority consensus" principle: that is, when the readings of most sensors are consistent, the value is considered reliable, and the readings of a few sensors that differ from the value by more than a predetermined threshold are judged as faults and isolated. This method can effectively monitor the operating status of the equipment in most flight phases and, to a certain extent, predict potential faults.

[0004] However, the effectiveness of this mechanism is based on the core assumption that "sensor failures occur statistically independently." When an aircraft is taking off or landing at an airport in a tropical or humid region, the humidity of the runway environment may change abruptly in a very short time. Water or pollutants on the wet runway surface may be splashed up by the aircraft wheels or condensed in a high-humidity environment, causing synchronous contamination or physical blockage of multiple static pressure tubes, dynamic pressure tubes, temperature and humidity sensors, etc. This multi-sensor bias caused by common environmental factors undermines the basic assumption of "independent failures," making the readings of redundant sensors highly correlated and consistent, making it difficult to distinguish between single-point failures and environmental interference.

[0005] Therefore, an artificial intelligence-based avionics equipment fault diagnosis and prediction system is proposed. Summary of the Invention

[0006] In view of the above-mentioned prior art, this application is hereby filed. Embodiments of this application provide an artificial intelligence-based avionics equipment fault diagnosis and prediction system, which can distinguish between single-point hardware faults and environmental interference, thereby improving the reliability of avionics equipment fault diagnosis.

[0007] According to one aspect of this application, an artificial intelligence-based avionics equipment fault diagnosis and prediction system is provided, comprising: a receiving module for receiving sensing data from multiple airborne sensors; an acquisition module for acquiring external environment data related to the aircraft's external environment; an anomaly data generation module for generating anomaly data based on the sensing data and a preset physical constraint relationship, the anomaly data indicating whether there are airborne sensors with abnormal readings and the attributes of the anomalies; and a common-cause probability generation module for generating common-cause failure probability values ​​based on the external environment data and a pre-constructed causal topology model, the causal topology model defining the propagation of environmental disturbances via physical means. The path affects the probabilistic relationship of multiple airborne sensors. The common cause failure probability value represents the probability that the current environment causes multiple airborne sensors to have correlated common errors. The correlation analysis module is used to perform correlation analysis between the abnormal data and the common cause failure probability value: if the abnormal data indicates that a single airborne sensor has an abnormal reading and the common cause failure probability value is lower than a preset threshold, then the abnormal reading is determined to be caused by a single point of hardware failure; if the abnormal data indicates that at least two airborne sensors have a coordinated abnormal reading that matches a preset common cause failure feature and the common cause failure probability value is higher than a preset threshold, then the abnormal reading is determined to be caused by the environment.

[0008] According to another aspect of this application, an electronic device is provided, including a memory and a processor, the memory being used to store computer-executable instructions, and the processor being used to execute the computer-executable instructions, which, when executed by the processor, implement the functions of the system as described above.

[0009] According to another aspect of this application, a computer storage medium is provided that stores computer-executable instructions thereon, which, when executed by a processor, implement the functions of the system described above.

[0010] Compared with the prior art, the avionics equipment fault diagnosis and prediction system based on artificial intelligence according to the embodiments of this application acquires sensor data through the receiving module, generates abnormal data and common cause failure probability values ​​by combining external environmental data, and uses the correlation analysis module to comprehensively judge the fault type. It can distinguish between single-point hardware failure and cooperative error caused by environmental interference, and has the advantage of improving the reliability of avionics equipment fault diagnosis. Attached Figure Description

[0011] The above and other objects, features, and advantages of this application will become more apparent from the more detailed description of the embodiments of this application in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the embodiments of this application to explain this application and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0012] Figure 1 This is a block diagram of the AI-based avionics equipment fault diagnosis and prediction system of the present invention.

[0013] Figure 2 This is a data flow diagram of the AI-based avionics equipment fault diagnosis and prediction system of the present invention.

[0014] Figure 3 This is a block diagram of an electronic device according to the present invention. Detailed Implementation

[0015] Hereinafter, exemplary embodiments according to this application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments of this application. It should be understood that this application is not limited to the exemplary embodiments described herein.

[0016] Exemplary System

[0017] Figures 1-2 The illustration shows an AI-based avionics equipment fault diagnosis and prediction system according to an embodiment of this application, including a receiving module, an acquisition module, an abnormal data generation module, a common cause probability generation module, and a correlation analysis module.

[0018] In this system, the receiving module is used to receive sensing data from multiple airborne sensors.

[0019] Airborne sensors refer to detection devices installed on aircraft to collect flight parameters or equipment status. Specifically, they can be implemented using sensors such as static pressure tubes, dynamic pressure tubes, temperature and humidity sensors, acceleration sensors, and wheel speed sensors. Their function is to provide real-time data input for fault diagnosis.

[0020] In this system, the acquisition module is used to acquire external environmental data related to the aircraft's external environment.

[0021] External environmental data refers to physical parameters related to the external environment in which the aircraft is located. Specifically, it can be obtained by using data such as runway humidity, temperature, pollutant concentration, and atmospheric pressure. Its purpose is to provide a basis for judging the impact of environmental factors on sensors.

[0022] In this system, the abnormal data generation module is used to generate abnormal data based on the sensor data and the preset physical constraint relationship. The abnormal data is used to indicate whether there are airborne sensors with abnormal readings and the attributes of the abnormality.

[0023] Specifically, generating anomalous data includes the following steps:

[0024] First, calculate the physical constraint residuals of the sensor data based on the physical constraint relationships, which include at least one of the following:

[0025] Aerodynamic equation constraints are used to verify the consistency between airspeed, static pressure and total pressure data of an aircraft. Specifically, the aerodynamic equation constraints establish the mathematical relationship between airspeed, static pressure and total pressure through Bernoulli's equation. When the static pressure tube is blocked and causes abnormal static pressure measurement, this constraint can detect the inconsistency between airspeed and static pressure data.

[0026] Kinematic constraints are used to verify the consistency between airspeed data and wheel speed data when the aircraft touches down. These kinematic constraints are activated during the landing gear touchdown phase. By comparing the data differences between the airspeed sensor and the wheel speed sensor, the situation of airspeed sensor abnormality caused by runway water accumulation can be identified.

[0027] Geometric constraints are used to verify the reading relationships of the same type of sensors at different installation locations under a specific flight attitude. These geometric constraints are based on the relationship between the sensor installation location and the aircraft's aerodynamic shape, establishing the theoretical reading deviation range of sensors at different locations under a specific pitch or roll angle.

[0028] Then, the physical constraint residuals are standardized to generate standardized residuals.

[0029] Finally, it is determined whether the standardized residual exceeds the preset residual threshold. If so, it is determined that the corresponding airborne sensor has an abnormal reading and abnormal data is generated. The abnormal data includes the airborne sensor identifier with abnormal reading, the severity of the abnormality, and the time information of the abnormality.

[0030] In this system, the common cause probability generation module is used to generate common cause failure probability values ​​based on external environmental data and a pre-built causal topology model. The causal topology model defines the probabilistic relationship of environmental disturbances affecting multiple airborne sensors through physical propagation paths. The common cause failure probability value represents the probability that the current environment will cause multiple airborne sensors to have correlated common errors.

[0031] The construction of the causal topological model includes the following steps:

[0032] First, acquire historical flight data. With sensor calibration data .

[0033] Then, the environmental variables in the external environment data and the measurements of the airborne sensors are associated as environmental nodes and sensor nodes, respectively.

[0034] Specifically, extract the set of external environment variables. and the set of airborne sensor measurements Each environment variable Establish as an environmental node, and record the measurements from each sensor. Establish as sensor nodes to obtain node set .

[0035] Next, based on the physical propagation mechanism, the relationship between environmental nodes and the sensor nodes affected by them is established to form an initial topology. The physical propagation mechanism is represented by a corresponding physical model, which includes at least:

[0036] Aerodynamic model: The mapping between air pressure and velocity field disturbances and sensor reading errors is described using fluid dynamics equations or their linearized models;

[0037] Structural dynamics model: The coupling relationship between external loads, structural response and sensor signals is established using structural vibration dynamics equations;

[0038] Heat conduction and radiation model: The relationship between external temperature disturbance and sensor temperature drift is established using the heat conduction equation;

[0039] Electromagnetic interference model: Maxwell's equations are used to establish the coupling path between lightning or electromagnetic pulses and the sensor circuit.

[0040] When the physical model determines that there exists a arrive When determining an effective propagation path, directed edges are established in the topology. This forms the initial topology.

[0041] Subsequently, based on the correlations in the initial topology, propagation parameters representing the degree of influence of environmental disturbances on airborne sensor readings are generated, i.e., on each edge of the initial topology. Define propagation parameters above. This is used to characterize the conditional probability effect of environmental disturbances on sensor readings. ,in Provided by a physical model or a statistical regression model;

[0042] Finally, based on historical flight data and sensor calibration data, propagation parameters are estimated using maximum likelihood estimation or Bayesian inference.

[0043]

[0044] in, and Let t be the sensor reading and the environmental observation at time t, respectively. If a Bayesian method is used, a prior distribution can be introduced into the likelihood function above. Calculate the posterior distribution ;

[0045] This yields a causal topological model for calculating the common-cause failure probability value. ,in, For the set of causal edges constrained by physical propagation mechanisms, As a set of propagation parameters, this model can calculate the probability of common cause failure of multiple sensors exhibiting correlated common errors, given current environmental observations, through Bayesian inference or belief propagation algorithms.

[0046]

[0047] in, This is the set of airborne sensors that have detected anomalies.

[0048] In this system, the correlation analysis module is used to perform correlation analysis between abnormal data and common cause failure probability values:

[0049] If abnormal data indicates that a single airborne sensor has an abnormal reading and the probability of common cause failure is lower than a preset threshold, then the abnormal reading is determined to be caused by a single point of hardware failure.

[0050] Specifically, if only one recorded sensor in the abnormal data has an abnormal reading, then the abnormal data indicates that a single airborne sensor has an abnormal reading.

[0051] If abnormal data indicates that at least two airborne sensors have a coordinated reading anomaly that matches the preset common cause failure characteristics, and the common cause failure probability value is higher than the preset threshold, then the reading anomaly is determined to be caused by the environment.

[0052] Specifically, the preset common-cause failure characteristics here may include time characteristics, anomaly degree characteristics, and anomaly location characteristics. Determining whether at least two airborne sensors exhibit coordinated reading anomalies matching the preset common-cause failure characteristics includes the following steps:

[0053] First, based on the abnormal time information contained in the abnormal data, it is determined whether the abnormal readings of at least two airborne sensors occurred synchronously within a preset time window. If so, first evidence indicating the existence of coordinated abnormal readings is generated.

[0054] Then, based on the severity of the anomalies contained in the abnormal data, it is determined whether the reading anomalies of at least two airborne sensors meet the preset severity correlation conditions. If so, a second piece of evidence indicating the existence of coordinated reading anomalies is generated.

[0055] Next, based on the identifiers of the airborne sensors contained in the abnormal data, at least two physical installation locations of airborne sensors are extracted from a preset identifier-installation location mapping table. It is then determined whether the physical installation locations of at least two airborne sensors are adjacent. If so, a third piece of evidence indicating the existence of a cooperative reading anomaly is generated.

[0056] Finally, if at least one of the first, second, and third pieces of evidence exists, it is determined that at least two airborne sensors exhibit a cooperative reading anomaly that matches the preset common cause failure characteristics.

[0057] In summary, the core innovation of this application lies in combining physical constraints with a causal topological model. Through correlation analysis between abnormal data and common cause failure probability values, it effectively distinguishes between single-point hardware failure and sensor collaborative failure caused by environmental factors. Traditional methods rely on sensor redundancy voting mechanisms, which cannot identify related errors caused by common environmental factors. However, this solution establishes a causal influence probability model from environmental variables to sensor groups and combines the temporal, spatial, and physical quantity characteristics of abnormal data to achieve accurate identification of common cause failures, thus solving the problem of misjudgment caused by the synchronous failure of multiple sensors under harsh environments such as high humidity.

[0058] Exemplary electronic devices

[0059] Figure 3 A block diagram of an electronic device according to an embodiment of this application is illustrated.

[0060] like Figure 3 As shown, the electronic device includes one or more processors and memory.

[0061] A processor can be a central processing unit (CPU) or other form of processing unit with data processing and / or instruction execution capabilities, and can control other components in an electronic device to perform desired functions.

[0062] The memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc.

[0063] In one example, the electronic device may also include input devices and output devices, which are interconnected via a bus system and / or other forms of connection mechanism (not shown).

[0064] Of course, for the sake of simplicity, Figure 3 Only some of the components of the electronic device relevant to this application are shown in this illustration; components such as buses, input / output interfaces, etc., are omitted. In addition, the electronic device may include any other suitable components depending on the specific application.

[0065] Exemplary computer-readable media

[0066] Embodiments of this application may also be computer-readable storage media storing computer program instructions thereon, which, when executed by a processor, cause the processor to perform the steps described in the "Exemplary Methods" section above according to the various embodiments of this application.

[0067] Computer-readable storage media may take the form of any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may, for example, include, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0068] The basic principles of this application have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this application are merely examples and not limitations, and should not be considered as essential features of each embodiment of this application. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the application to the necessity of employing the aforementioned specific details for implementation.

[0069] The block diagrams of devices, apparatuses, devices, and systems involved in this application are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0070] It should also be noted that in the apparatus, equipment, and methods of this application, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered as equivalent solutions of this application.

[0071] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of this application. Therefore, this application is not intended to be limited to the aspects shown herein, but rather to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0072] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this application to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. An artificial intelligence-based avionics equipment fault diagnosis and prediction system, characterized in that, The system includes: The receiving module is used to receive sensing data from multiple airborne sensors; The acquisition module is used to acquire external environment data related to the aircraft's external environment. An abnormal data generation module is used to generate abnormal data based on the sensing data and the preset physical constraint relationship. The abnormal data is used to indicate whether there are airborne sensors with abnormal readings and the attributes of the abnormality. The common cause probability generation module is used to generate a common cause failure probability value based on the external environment data and the pre-built causal topology model. The causal topology model defines the probabilistic relationship of environmental disturbances affecting multiple airborne sensors through physical propagation paths. The common cause failure probability value represents the probability that the current environment causes multiple airborne sensors to have correlated common errors. The correlation analysis module is used to perform correlation analysis between the abnormal data and the common cause failure probability value: If the abnormal data indicates that a single airborne sensor has an abnormal reading, and the probability value of common cause failure is lower than a preset threshold, then the abnormal reading is determined to be caused by a single point of hardware failure. If the abnormal data indicates that at least two of the airborne sensors have a coordinated reading anomaly that matches a preset common cause failure characteristic, and the probability value of the common cause failure is higher than a preset threshold, then the reading anomaly is determined to be caused by the environment.

2. The avionics equipment fault diagnosis and prediction system based on artificial intelligence according to claim 1, characterized in that, The construction of the causal topological model includes: Acquire historical flight data and sensor calibration data; The environmental variables in the external environment data and the measurements of the airborne sensors are respectively associated as environmental nodes and sensor nodes; Based on the physical propagation mechanism, establish the correlation between environmental nodes and sensor nodes affected by them to form an initial topology; Based on the correlations in the initial topology, propagation parameters representing the degree of influence of environmental disturbances on airborne sensor readings are generated; Based on the historical flight data and sensor calibration data, the propagation parameters are estimated to obtain the causal topology model used to calculate the common cause failure probability value.

3. The avionics equipment fault diagnosis and prediction system based on artificial intelligence according to claim 1, characterized in that, The step of generating abnormal data based on the relationship between the sensing data and preset physical constraints includes: Calculate the physical constraint residuals of the sensing data based on the physical constraint relationships; The physical constraint residuals are standardized to generate standardized residuals; Determine whether the standardized residual exceeds a preset residual threshold. If so, determine that the corresponding airborne sensor has an abnormal reading and generate the abnormal data. The abnormal data includes the airborne sensor identifier of the abnormal reading, the severity of the abnormality, and the time of the abnormality.

4. The avionics equipment fault diagnosis and prediction system based on artificial intelligence according to claim 1 or 3, characterized in that, The physical constraint relationship includes at least one of the following: Aerodynamic equation constraints used to verify the consistency between aircraft airspeed, static pressure and total pressure data; Kinematic constraints used to verify the consistency between airspeed data and wheel speed data when the aircraft touches down; Geometric constraints used to verify the reading relationships of the same type of sensors installed at different locations under a specific flight attitude.

5. The avionics equipment fault diagnosis and prediction system based on artificial intelligence according to claim 4, characterized in that, The indication that at least two of the airborne sensors exhibit coordinated reading anomalies matching preset common-cause failure characteristics includes: Based on the abnormal time information contained in the abnormal data, it is determined whether the abnormal readings of the at least two airborne sensors occurred synchronously within a preset time window. If so, first evidence indicating the existence of the coordinated reading abnormality is generated.

6. The avionics equipment fault diagnosis and prediction system based on artificial intelligence according to claim 5, characterized in that, The indication that at least two of the airborne sensors exhibit a coordinated reading anomaly matching a preset common-cause failure characteristic also includes: Based on the severity of the anomalies contained in the abnormal data, it is determined whether the reading anomalies of the at least two airborne sensors meet the preset severity correlation conditions. If so, a second determination evidence indicating the existence of the cooperative reading anomaly is generated.

7. The avionics equipment fault diagnosis and prediction system based on artificial intelligence according to claim 6, characterized in that, Before determining that the at least two airborne sensors exhibit a coordinated reading anomaly matching a preset common-cause failure characteristic, the method further includes: Based on the identifiers of the airborne sensors contained in the abnormal data, the physical installation locations of at least two airborne sensors are extracted from a preset identifier-installation location mapping table. Determine whether the physical installation locations of the at least two airborne sensors are adjacent; if so, generate third evidence indicating the presence of the abnormal synergy readings.

8. The avionics equipment fault diagnosis and prediction system based on artificial intelligence according to claim 7, characterized in that, If at least one of the first determination evidence, the second determination evidence, and the third determination evidence exists, then it is determined that the at least two airborne sensors have a cooperative reading anomaly that matches the preset common cause failure characteristics.

9. An electronic device comprising a memory and a processor, characterized in that: The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions, which, when executed by the processor, implement the functions of the system as described in any one of claims 1 to 8.

10. A computer storage medium storing computer-executable instructions thereon, characterized in that: When the computer-executable instructions are executed by the processor, they implement the functions of the system as described in any one of claims 1 to 8.

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