Unmanned aerial vehicle driving system fault diagnosis method and device

Abstract

The invention discloses an unmanned aerial vehicle driving system fault diagnosis method and device, and relates to the field of fault diagnosis, and the method comprises the steps: constructing a T-S fuzzy fault tree according to the part structure of an unmanned aerial vehicle driving system; the fault rate of each component of the unmanned aerial vehicle driving system is obtained, the fault rate of each bottom event corresponding to each fault degree is determined, and a first fuzzy fault set (including the first fuzzy fault rate of each fault degree) of each middle event and each top event is determined in combination with the T-S fuzzy fault tree; simulating the fault degree of each bottom event, and determining a membership function of each bottom event; determining a membership degree value of each fault degree corresponding to the bottom event based on a membership function of the bottom event, and determining a second fuzzy fault set (including a second fuzzy fault rate of each fault degree) of each middle event and the top event in combination with the T-S fuzzy fault tree; the two fuzzy fault sets of the top event are fault diagnosis results of the unmanned aerial vehicle driving system. According to the invention, the fault diagnosis precision of the unmanned aerial vehicle driving system is improved.

Description

Technical Field

[0001] The present invention relates to the field of fault diagnosis, and in particular to a method and device for diagnosing faults of a UAV drive system. Background Art

[0002] As a commonly used airborne equipment carrier platform, the drive system of multi-rotor drones is one of the core components. The drive system provides power and load for the entire drone, determines the flight performance of the drone, and directly affects the reliability of the drone in performing specific tasks in different climatic environments. A good drive system design makes the drone more durable, reduces maintenance requirements and downtime caused by potential failures, thereby reducing the cost of use. At the same time, any failure in the drive system will increase the risk of the drone losing control and crashing. According to relevant literature, the probability of failure of the drive system accounts for more than 50%. In recent years, the importance of drone drive systems has attracted the attention of some scholars.

[0003] The reliability of UAV systems is generally studied using methods such as Markov chain, Monte Carlo algorithm, Fault Tree Analysis (FTA), Failure Mode and Effect Analysis (FMEA), etc. The failure mechanism and various fault states of the complex system of UAV are analyzed and the reliability calculation method is given. Or the sensor is designed or improved to monitor the working state of the drive system, thereby improving the reliability of the drive system. Yang Cheng et al. used the traditional fault tree model to analyze the failure of the UAV system. Song Y et al. used the discrete time Bayesian network method to solve the uncertainty problem in the complex system, but in the calculation process, it was set that a single component had only two states: failure and normal operation. In a complex system, the states of each component are diverse, and due to replacement of parts, maintenance and other reasons, the states of each component are quite different. Therefore, the academic community has not conducted in-depth research on the problem of calculating the probability of multiple fault states of UAV drive systems. Summary of the invention

[0004] The purpose of the present invention is to provide a method and device for diagnosing faults of a UAV drive system, which can improve the accuracy of fault diagnosis of the UAV drive system.

[0005] To achieve the above object, the present invention provides a method for diagnosing a fault in a UAV drive system, comprising: According to the component structure of the UAV drive system, a TS fuzzy fault tree is constructed; the TS fuzzy fault tree includes a top event, multiple intermediate events, multiple bottom events and multiple TS fuzzy gates; wherein the top event is a UAV drive system fault, and the multiple bottom events are component faults of the UAV drive system; each TS fuzzy gate corresponds to an intermediate event or a top event; Obtain the failure rate of each component of the UAV drive system within a set time period, and determine the failure rate of each bottom event corresponding to each failure degree; According to the failure rates of the bottom events corresponding to the failure degrees and the TS fuzzy fault tree, determining the first fuzzy failure set of the intermediate events and the first fuzzy failure set of the top events; the first fuzzy failure set includes the first fuzzy failure rates corresponding to the failure degrees; Simulate the fault degree of each bottom event and determine the membership function of each bottom event; For any bottom event, determining the membership value of each fault degree corresponding to the bottom event based on the membership function of the bottom event; According to the membership value of each bottom event corresponding to each fault degree and the TS fuzzy fault tree, the second fuzzy fault set of each intermediate event and the second fuzzy fault set of the top event are determined; the second fuzzy fault set includes the second fuzzy fault rate corresponding to each fault degree; the first fuzzy fault set and the second fuzzy fault set of the top event are used as the fault diagnosis results of the UAV drive system.

[0006] To achieve the above objectives, the present invention also provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned UAV drive system fault diagnosis method.

[0007] According to the specific embodiment provided by the present invention, the present invention discloses the following technical effects: the present invention constructs a TS fuzzy fault tree according to the component structure of the UAV drive system, and based on the TS fuzzy fault tree, two methods are used to calculate the failure rate of the drive system: (1) the first fuzzy failure rate of the drive system is calculated according to the failure rate of the bottom event; (2) the second fuzzy failure rate of the drive system is calculated according to the membership value of each failure degree corresponding to the bottom event. The fuzzy failure of the top event calculated by the above two methods is used as the fault diagnosis result of the drive system, which improves the fault diagnosis accuracy of the UAV drive system. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0009] Figure 1 This is a flow chart of the UAV drive system fault diagnosis method provided by the present invention.

[0010] Figure 2A schematic diagram of a simple TS fuzzy fault tree.

[0011] Figure 3 Schematic diagram of the TS fuzzy fault tree of the multi-rotor UAV drive system.

[0012] Figure 4 Schematic diagram of the membership function.

[0013] Figure 5 This is a diagram of the internal structure of a computer device. DETAILED DESCRIPTION

[0014] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0015] The purpose of the present invention is to provide a method and device for diagnosing faults of a UAV drive system. The structural composition and working principle of a multi-rotor UAV drive system are analyzed by a TS fuzzy fault tree modeling method, the causes of the faults and the corresponding possibilities are listed, the failure probability of each component is characterized by a fuzzy number, and the correlation between each component or between the component and the drive system is characterized by a TS fuzzy gate, so as to realize the failure rate calculation of a multivariable system under the conditions of fuzzy fault mechanism and diverse fault states, and more accurately predict the failure degree of the drive system by recording and accumulating daily drive system fault data.

[0016] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0017] like Figure 1 As shown, the UAV drive system fault diagnosis method provided by the present invention includes the following steps S1 to S6.

[0018] S1: Construct a TS fuzzy fault tree based on the component structure of the UAV drive system.

[0019] Considering the uncertainty of the relationship between events in the drive system, the present invention uses TS fuzzy gates and fuzzy numbers to describe the relationship between events and the degree of fault, thereby constructing a new fault tree - TS fuzzy fault tree. The TS fuzzy fault tree consists of a series of IF-THEN rules, which can be used to describe the static event relationship, so that the TS fuzzy gate can be constructed using the TS fuzzy fault tree.

[0020] The TS fuzzy fault tree includes a top event, multiple intermediate events, multiple bottom events, and multiple TS fuzzy gates. The top event is the failure of the UAV drive system, and the multiple bottom events are the failures of various components of the UAV drive system. Each TS fuzzy gate corresponds to an intermediate event or a top event.

[0021] Each TS fuzzy gate includes multiple rules, each rule is used to specify the fuzzy possibility of each fault degree of the upper-level event corresponding to the TS fuzzy gate, and the fault degree of the lower-level event of the upper-level event. The upper-level event corresponding to the TS fuzzy gate is an intermediate event or a top event. The lower-level event of the upper-level event is a bottom event and / or an intermediate event.

[0022] Figure 2 is a simple TS fuzzy fault tree, in which x 1, x 2, x 3 is the bottom event, Y For intermediate events, T Top event, top event T The fuzzy possibility of the bottom event x 1 and intermediate events Y The fault data is obtained, and the intermediate events Y The bottom event x 2 and x The fuzzy failure rate of 3 is calculated.

[0023] In the traditional fault tree, the failure probability is obtained based on the historical maintenance data. However, due to the lack of historical data and the environmental diversity of the drive system, the failure probability cannot be accurately obtained. The present invention introduces fuzzy logic into the fault tree, converts the failure data obtained based on the expert system and experience into fuzzy failure rate, and thus calculates the failure probability of the drive system. Similarly, the fault status of each component of the drive system can also be characterized by fuzzy numbers.

[0024] The present invention represents the failure rate and failure degree of each component of the driving system with fuzzy numbers, and divides the failure degree into three levels: no failure, slight failure, and severe failure, which are represented by fuzzy numbers 0, 0.5, and 1 respectively.

[0025] The following assumptions are made: ① The rule of the T-S fuzzy gate is recorded as , m is the total number of rules; ②The bottom event is recorded as , n is the total number of bottom events; ③ The failure probability of the bottom event is recorded as , k n For the n The bottom event k The value is selected appropriately according to the actual situation. is the failure probability of the first failure state of the bottom event; ④ The failure degree of the bottom event is recorded as , g n For the n The bottom event g The fault degree should be selected appropriately according to the actual situation. is the first fault degree of the bottom event; ⑤The fault degree of the upper event is recorded as , q n is the total number of parent events, is the fault severity of the first superior event.

[0026] The present invention takes the multi-rotor UAV drive system failure as the top event, and then disassembles the structure of its three main components to obtain intermediate events and bottom events. As an implementation method, the TS fuzzy fault tree of the multi-rotor UAV drive system is as follows: Figure 3 As shown, Figure 3 The numbers 1 to 8 represent 8 TS fuzzy gates. The numbers and names of all bottom events are shown in Table 1.

[0027] Table 1 Bottom event numbers and names

[0028] According to the operation mode of the multi-rotor UAV drive system, the bottom event , intermediate events And top events The fault degree is 0, 0.5, 1, and the membership function , . Since the fault state of the propeller is more obvious during daily use and can be replaced in time, the fault probability of the propeller is not considered. Based on the empirical data, 8 TS fuzzy gates are obtained. Due to space limitations, only the first TS fuzzy gate, the second TS fuzzy gate, the third TS fuzzy gate and the eighth TS fuzzy gate are listed here, as shown in Tables 2 to 5. In Table 2, x 1 and x 2 The value corresponding to each rule is its fault degree, y The value of 1 corresponding to each rule is its fuzzy possibility, and the same is true for Tables 3 to 5. In addition, the number of rules in the 8 TS fuzzy gates is appropriately selected according to the actual situation, and the value of the fault degree can be 0 or 1, or 0, 0.5 or 1 or more values.

[0029] Table 2 The first TS fuzzy gate

[0030] Table 3 The second TS fuzzy gate

[0031] Table 4 The third TS fuzzy gate

[0032] Table 5 The 8th TS fuzzy gate

[0033] S2: Obtain the failure rate of each component of the UAV drive system within a set time period, and determine the failure rate of each bottom event corresponding to each failure degree.

[0034] Specifically, the number of failures and the total working time of each component of the UAV drive system within a set time period are obtained. For any component of the UAV drive system, the failure rate of the component is determined according to the ratio of the number of failures of the component within the set time period to the total working time.

[0035] The fuzzy failure rate of each component of the UAV drive system can generally be expressed by the failure rate, and the failure rate distribution density function of each component is made to obey the exponential distribution. : ,in, λ is the failure rate of the component.

[0036] According to the expert system and empirical data, the life of components of the UAV drive system is usually greater than 5 years. Therefore, the present invention counts the components and the number of failures that occur in the drive system in 5 years to calculate the failure probability, as shown in Table 6.

[0037] Table 6 Failure rate of bottom events

[0038] S3: According to the fault rates of each bottom event corresponding to each fault degree and the TS fuzzy fault tree, determine the first fuzzy fault set of each intermediate event and the first fuzzy fault set of the top event. The first fuzzy fault set includes the first fuzzy fault rates corresponding to each fault degree.

[0039] Step S3 analyzes the failure rate of the drive system according to the fuzzy failure rate of the components. , then the failure rate of the bottom event is , The fuzzy failure rate is .

[0040] Let the failure rate of each component be the fuzzy failure rate of the bottom event, then The first execution of the rule for .

[0041] Then the first fuzzy failure rate of the superior event is .in, For the q n The first fuzzy failure rate of the superordinate event, For the q n The vague possibility of a superordinate event.

[0042] Specifically, for any upper-level event in the TS fuzzy fault tree, the first fuzzy fault set of the upper-level event is determined according to the first fuzzy fault set of each lower-level event of the upper-level event and the TS fuzzy gate corresponding to the upper-level event. The first fuzzy fault rate of each fault degree corresponding to the bottom event is the fault rate of each fault degree corresponding to the bottom event.

[0043] In this embodiment, the following formula is used to determine the parent event: A The corresponding fault level is c The first fuzzy failure rate.

[0044] .

[0045] .

[0046] in, For the superior event A The corresponding TS fuzzy gate The first execution degree of a rule, A n For the superior event A The number of subordinate events, For the superior event A The corresponding TS fuzzy gate The rules stipulate i The failure degree of each subordinate event, for The first fuzzy failure rate, For the superior event A The corresponding fault level is c The first fuzzy failure rate, A r For the superior event A The number of rules corresponding to the TS fuzzy gate, For the superior event A The corresponding TS fuzzy gate Superior event specified by the rule A The degree of failure is c The vague possibility.

[0047] In order to better understand the technical solution of the present invention, it is assumed that the failure rate when the failure degree is 0.5 is equal to the failure rate when the failure degree is 1. Figure 3 Intermediate events in TS fuzzy fault tree y 1 is the calculation formula of the first fuzzy failure rate corresponding to the failure degree of 0.5.

[0048] .

[0049] but y The calculation formula of the first fuzzy failure rate corresponding to the failure degree of 1 is: . y 2 The calculation formula of the first fuzzy failure rate corresponding to the failure degree of 0.5 is: . y The calculation formula of the first fuzzy failure rate corresponding to the failure degree of 1 is: .

[0050] Similarly, the first fuzzy failure rate of all intermediate events and bottom events can be calculated, as shown in Table 7.

[0051] Table 7 The first fuzzy failure rate of intermediate events and top events

[0052] By comparing the data in Table 7, it can be seen that the fuzzy probability of the intermediate event and the bottom event are roughly the same in magnitude, and the probability of the bottom event is lower than that of the intermediate event, which is in line with reality. By analyzing the first fuzzy failure rate of the top event, the intermediate event and the bottom event, it can be found that when each component is in the initial state, the probability of critical damage is low. And for a relatively simple system such as the UAV drive system, it is applicable to use the TS fuzzy fault tree to derive the fuzzy failure rate of the top event from the fuzzy failure rate of the bottom event.

[0053] S4: Simulate the fault degree of each bottom event and determine the membership function of each bottom event.

[0054] Common membership functions include rectangular, trapezoidal, normal, etc. The present invention uses a trapezoidal membership function. The trapezoidal membership function F is expressed as: ;in, is the center of the fuzzy number support set, is the left support radius, is the right support radius, Left fuzzy area, is the right fuzzy area, such as Figure 4 shown.

[0055] Depend on Figure 4 The equation for the fuzzy number can be obtained as follows.

[0056] .

[0057] in, is the fuzzy number of failure rate and failure degree.

[0058] Specifically, the simulated fault degree value of the bottom event is used as the membership function F 0, with Figure 3 TS fuzzy fault tree x 1. x 2 and x Taking 3 as an example, the following three membership functions can be obtained.

[0059] .

[0060] .

[0061] .

[0062] in, for x The membership value is 1, for x The membership value of 2, for x The membership value of 3, F The value of is 0, 0.5 or 1.

[0063] S5: For any bottom event, determine the membership value of each fault degree corresponding to the bottom event based on the membership function of the bottom event.

[0064] Specifically, x 1 as an example, using the above x The membership function of 1 can be calculated x The membership values ​​corresponding to the fault degrees of 0, 0.5 and 1 are 1 / 3, 2 / 3 and 0 respectively. The membership values ​​of all bottom events are shown in Table 8. The membership values ​​of 0, 0.5 and 1 represent three fault degrees. The values ​​of the membership columns corresponding to each row are the membership values ​​of the three fault degrees. The fault state is used to calculate the degree to which the component belongs to 0, 0.5 and 1 in the current state.

[0065] Table 8 Fault degree and membership value of bottom events

[0066] S6: According to the membership values ​​of each bottom event corresponding to each fault degree and the TS fuzzy fault tree, determine the second fuzzy fault set of each intermediate event and the second fuzzy fault set of the top event. The second fuzzy fault set includes the second fuzzy fault rate corresponding to each fault degree. The first fuzzy fault set and the second fuzzy fault set of the top event are used as the fault diagnosis results of the UAV drive system.

[0067] Step S6 analyzes the failure rate of the drive system according to the degree of failure of the components. ,make , the membership function is used to quantify the fault degree of the bottom event, and the fuzzy membership degrees of fault degrees 0, 0.5, and 1 are , , .

[0068] No. The initial second execution of the rule for .

[0069] Normalize the above formula to get The second execution degree of the rule for .

[0070] Then the second fuzzy fault rate of the superior event is .in, For the q n The second fuzzy failure rate of the superordinate event, For the q n The vague possibility of a superordinate event.

[0071] Specifically, for any upper-level event in the TS fuzzy fault tree, the second fuzzy fault set of the upper-level event is determined according to the second fuzzy fault sets of each lower-level event of the upper-level event and the TS fuzzy gate corresponding to the upper-level event. The second fuzzy fault rate of each fault degree corresponding to the bottom event is the membership value of each fault degree corresponding to the bottom event.

[0072] In this embodiment, the following formula is used to determine the parent event: A The corresponding fault level is c The second fuzzy failure rate.

[0073] .

[0074] .

[0075] in, For the superior event A The corresponding TS fuzzy gate The second execution degree of the rule, A n For the superior event A The number of subordinate events, For the superior event A The corresponding TS fuzzy gate The rules stipulate iThe failure degree of each subordinate event, for The second fuzzy failure rate, A r For the superior event A The number of rules corresponding to the TS fuzzy gate, For the superior event A The corresponding fault level is c The second fuzzy failure rate, For the superior event A The corresponding TS fuzzy gate Superior event specified by the rule A The degree of failure is c The vague possibility.

[0076] In order to better understand the technical solution of the present invention, the following is introduced Figure 3 Intermediate events in TS fuzzy fault tree y 1 corresponds to the specific calculation formula of the second fuzzy failure rate of 0.

[0077] .

[0078] Similarly, the second fuzzy failure rate of all intermediate events and bottom events can be calculated, as shown in Table 9.

[0079] Table 9 Second fuzzy failure rate of intermediate events and top events

[0080] Through the above calculation, the top event y 8 The second fuzzy failure rates when the failure degrees are 0, 0.5, and 1 are: 9.8%, 7.4%, and 82.8%.

[0081] The present invention uses the first fuzzy failure rate and the second fuzzy failure rate of the top event to characterize the failure of the drive system, which can be used for users to judge the degree of system failure.

[0082] Aiming at the situation that the components of the UAV drive system are highly correlated and have different reliability in different fault states, the present invention applies the fuzzy fault tree analysis method to the fault diagnosis of the drive system. Based on the fuzzy fault tree analysis method of the TS model and the analysis of the UAV drive system model, a TS fuzzy fault tree of the drive system is constructed, and reliability analysis is performed. Two methods are used in the calculation, namely, calculating the first fuzzy failure rate of the drive system according to the fuzzy failure rate of the components and calculating the second fuzzy failure rate of the drive system according to the degree of component failure. The calculation results show that both calculation methods have good adaptability to the fault diagnosis of the UAV drive system. Compared with the traditional fault tree that subdivides the degree of fault, the fault rate assessment of the present invention is more accurate.

[0083] In one embodiment, a computer device is provided. The computer device may be a database, and its internal structure diagram may be as follows: Figure 5 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store pending transactions. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for diagnosing faults in a drone drive system is implemented.

[0084] In one embodiment, a computer device is also provided, including a memory and a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps in the above-mentioned UAV drive system fault diagnosis method when executing the computer program.

[0085] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned UAV drive system fault diagnosis method are implemented.

[0086] In one embodiment, a computer program product is provided, including a computer program, which, when executed by a processor, implements the steps in the above-mentioned drone drive system fault diagnosis method.

[0087] It should be noted that the object information (including but not limited to object device information, object personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the object or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards.

[0088] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0089] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0090] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for diagnosing a fault in a UAV drive system, characterized in that: The UAV drive system fault diagnosis method comprises: According to the component structure of the UAV drive system, a TS fuzzy fault tree is constructed; the TS fuzzy fault tree includes a top event, multiple intermediate events, multiple bottom events and multiple TS fuzzy gates; wherein the top event is a UAV drive system fault, and the multiple bottom events are component faults of the UAV drive system; each TS fuzzy gate corresponds to an intermediate event or a top event; Obtain the failure rate of each component of the UAV drive system within a set time period, and determine the failure rate of each bottom event corresponding to each failure degree; According to the failure rates of the bottom events corresponding to the failure degrees and the TS fuzzy fault tree, determining the first fuzzy failure set of the intermediate events and the first fuzzy failure set of the top events; the first fuzzy failure set includes the first fuzzy failure rates corresponding to the failure degrees; Simulate the fault degree of each bottom event and determine the membership function of each bottom event; For any bottom event, determining the membership value of each fault degree corresponding to the bottom event based on the membership function of the bottom event; According to the membership value of each bottom event corresponding to each fault degree and the TS fuzzy fault tree, the second fuzzy fault set of each intermediate event and the second fuzzy fault set of the top event are determined; the second fuzzy fault set includes the second fuzzy fault rate corresponding to each fault degree; the first fuzzy fault set and the second fuzzy fault set of the top event are used as the fault diagnosis results of the UAV drive system.

2. The method for diagnosing a fault in a UAV driving system according to claim 1, characterized in that: Obtain the failure rate of each component of the drone drive system within a set time period, including: Obtain the number of failures and total working time of each component of the UAV drive system within a set time period; For any component of the UAV drive system, the failure rate of the component is determined based on the ratio of the number of failures of the component within a set time period to the total working time.

3. The method for diagnosing a fault in a UAV driving system according to claim 1, characterized in that: Each TS fuzzy gate includes a plurality of rules, each rule being used to specify the fuzzy probability of each fault degree of the upper-level event corresponding to the TS fuzzy gate, and the fault degree of the lower-level event of the upper-level event; The upper-level event corresponding to the TS fuzzy gate is an intermediate event or a top event; The subordinate events of the superior event are bottom events and / or intermediate events.

4. The method for diagnosing a fault in a UAV driving system according to claim 3, characterized in that: According to the failure rate of each bottom event corresponding to each failure degree and the TS fuzzy fault tree, determining the first fuzzy fault set of each intermediate event and the first fuzzy fault set of the top event specifically includes: For any upper-level event in the TS fuzzy fault tree, the first fuzzy fault set of the upper-level event is determined according to the first fuzzy fault set of each lower-level event of the upper-level event and the TS fuzzy gate corresponding to the upper-level event; wherein the first fuzzy fault rate of each fault degree corresponding to the bottom event is the fault rate of each fault degree corresponding to the bottom event.

5. The method for diagnosing a fault in a UAV driving system according to claim 4, characterized in that: The following formula is used to determine the parent event A The corresponding fault level is c The first fuzzy failure rate: ； ； in, For the superior event A The corresponding TS fuzzy gate The first execution degree of a rule, A n For the superior event A The number of subordinate events, For the superior event A The corresponding TS fuzzy gate The rules stipulate i The failure degree of each subordinate event, for The first fuzzy failure rate, For the superior event A The corresponding fault level is c The first fuzzy failure rate, A r For the superior event A The number of rules corresponding to the TS fuzzy gate, For the superior event A The corresponding TS fuzzy gate Superior event specified by the rule A The degree of failure is c The vague possibility.

6. The method for diagnosing a fault in a UAV driving system according to claim 3, characterized in that: According to the membership values ​​of the bottom events corresponding to the fault degrees and the TS fuzzy fault tree, the second fuzzy fault set of each intermediate event and the second fuzzy fault set of the top event are determined, specifically including: For any upper-level event in the TS fuzzy fault tree, the second fuzzy fault set of the upper-level event is determined according to the second fuzzy fault sets of each lower-level event of the upper-level event and the TS fuzzy gate corresponding to the upper-level event; wherein the second fuzzy fault rate of each fault degree corresponding to the bottom event is the membership value of each fault degree corresponding to the bottom event.

7. The method for diagnosing a fault in a UAV driving system according to claim 6, characterized in that: The following formula is used to determine the parent event A The corresponding fault level is c The second fuzzy failure rate: ； ； in, For the superior event A The corresponding TS fuzzy gate The second execution degree of the rule, A n For the superior event A The number of subordinate events, For the superior event A The corresponding TS fuzzy gate The rules stipulate i The failure degree of each subordinate event, for The second fuzzy failure rate, A r For the superior event A The number of rules corresponding to the TS fuzzy gate, For the superior event A The corresponding fault level is c The second fuzzy failure rate, For the superior event A The corresponding TS fuzzy gate Superior event specified by the rule A The degree of failure is c The vague possibility.

8. The method for diagnosing a fault in a UAV driving system according to claim 1, characterized in that: The multiple bottom events are: foreign matter, coil short circuit and open circuit, wear, corrosion, shaft deformation, metal-oxide semiconductor field effect transistor damage, battery-free circuit damage, microcontroller damage, half-bridge chip damage, transmission signal too far, remote control device failure, resistor short circuit and open circuit, capacitor short circuit and open circuit and power supply damage.

9. The method for diagnosing a fault in a UAV driving system according to claim 1, characterized in that: The failure levels include 0, 0.5, and 1.

10. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the UAV drive system fault diagnosis method according to any one of claims 1 to 9.

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