A method and device for diagnosing faults in a UAV drive system

Abstract

The present invention discloses a method and device for diagnosing a fault in an unmanned aerial vehicle (UAV) drive system. The method relates to the field of fault diagnosis and includes: constructing a T-S fuzzy fault tree based on the component structure of the UAV drive system; obtaining the failure rate of each component of the UAV drive system, determining the failure rate of each bottom event corresponding to each fault severity, and using the T-S fuzzy fault tree to determine a first fuzzy fault set for each intermediate event and top event (including a first fuzzy fault rate for each fault severity); simulating the fault severity of each bottom event to determine a membership function for each bottom event; determining the membership value of each bottom event corresponding to each fault severity based on the membership function of the bottom event, and using the T-S fuzzy fault tree to determine a second fuzzy fault set for each intermediate event and top event (including a second fuzzy fault rate for each fault severity); the two fuzzy fault sets for the top event constitute the fault diagnosis result for the UAV drive system. This invention improves the accuracy of fault diagnosis for UAV drive systems.

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 an unmanned aerial vehicle (UAV) drive system. Background Art

[0002] Multi-rotor drones, a common platform for carrying airborne equipment, rely on their drive systems as a core component. The drive system provides power and payload for the entire drone, determining its flight performance and directly impacting its reliability in performing specific missions in diverse climates. A well-designed drive system enhances drone durability, reduces maintenance requirements and downtime due to potential failures, and thus lowers operating costs. However, any failure in the drive system increases the risk of the drone losing control and crashing. According to relevant literature, the probability of drive system failure exceeds 50%. In recent years, the importance of drone drive systems has garnered considerable attention from scholars.

[0003] The reliability of drone systems is generally studied using methods such as Markov chains, Monte Carlo algorithms, fault tree analysis (FTA), and failure mode and effect analysis (FMEA). These methods analyze the failure mechanisms and various fault states of complex drone systems and provide reliability calculation methods. Alternatively, sensors can be designed or improved to monitor the operating status of the drive system, thereby improving its reliability. Yang Cheng et al. used traditional fault tree models to analyze drone system failures. Song Y et al. used discrete-time Bayesian networks to address uncertainty in complex systems, but their calculations assumed that a single component had only two states: faulty and normal operation. However, the states of individual components in complex systems are diverse, and due to factors such as replacement and maintenance, the states of individual components can vary significantly. Therefore, academic research on calculating the probabilities of multiple fault states in drone drive systems is still insufficient. Summary of the Invention

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

[0005] To achieve the above objectives, the present invention provides a method for diagnosing faults in a UAV drive system, comprising:

[0006] 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 faults of various components of the UAV drive system; each TS fuzzy gate corresponds to an intermediate event or a top event;

[0007] 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;

[0008] Determine, based on the failure rates of the bottom events corresponding to the failure degrees and the TS fuzzy fault tree, a first fuzzy fault set of each intermediate event and a first fuzzy fault set of the top event; the first fuzzy fault set includes a first fuzzy failure rate corresponding to each failure degree;

[0009] Simulate the fault degree of each bottom event and determine the membership function of each bottom event;

[0010] 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;

[0011] 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 failure 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.

[0012] To achieve the above objectives, the present invention also provides 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 above-mentioned UAV drive system fault diagnosis method.

[0013] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects: the present invention constructs a TS fuzzy fault tree based on the component structure of the UAV drive system, and based on the TS fuzzy fault tree, adopts two methods to calculate the failure rate of the drive system: (1) calculating the first fuzzy failure rate of the drive system based on the failure rate of the bottom event; (2) calculating the second fuzzy failure rate of the drive system based on the membership value of each fault degree corresponding to the bottom event. The fuzzy fault of the top event calculated by the above two methods is used as the fault diagnosis result of the drive system, thereby improving the fault diagnosis accuracy of the UAV drive system. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. 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 any creative work.

[0015] Figure 1 This is a flow chart of the UAV drive system fault diagnosis method provided by the present invention.

[0016] Figure 2 Schematic diagram of a simple TS fuzzy fault tree.

[0017] Figure 3 Schematic diagram of the TS fuzzy fault tree of the multi-rotor UAV drive system.

[0018] Figure 4 Schematic diagram of the membership function.

[0019] Figure 5 This is a diagram of the internal structure of a computer device. DETAILED DESCRIPTION

[0020] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0021] The purpose of the present invention is to provide a method and device for diagnosing faults in a drone drive system. The method uses the TS fuzzy fault tree modeling method to analyze the structural composition and working principle of a multi-rotor drone drive system, enumerate the causes of the faults and their corresponding possibilities, characterize the failure probability of each component with fuzzy numbers, and characterize the correlation between each component or between the component and the drive system with TS fuzzy gates. This method realizes the failure rate calculation of a multivariable system under the conditions of fuzzy fault mechanisms and diverse fault states. By recording and accumulating daily drive system fault data, the failure degree of the drive system can be more accurately predicted.

[0022] 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.

[0023] like Figure 1 As shown, the UAV drive system fault diagnosis method provided by the present invention includes the following steps S1 to S6.

[0024] S1: Construct a TS fuzzy fault tree based on the component structure of the UAV drive system.

[0025] Considering the uncertainty of the relationships between events in a drive system, this paper uses TS fuzzy gates and fuzzy numbers to describe the relationships between events and the severity of faults, thereby constructing a new fault tree—the TS fuzzy fault tree. The TS fuzzy fault tree, composed of a series of if-then rules, can be used to describe static event relationships, thus allowing the construction of TS fuzzy gates using the TS fuzzy fault tree.

[0026] The TS fuzzy fault tree consists of a top event, multiple intermediate events, multiple bottom events, and multiple TS fuzzy gates. The top event is a fault in the drone's propulsion system, and the bottom events are faults in various components of the drone's propulsion system. Each TS fuzzy gate corresponds to an intermediate event or a top event.

[0027] Each TS fuzzy gate includes multiple rules, each of which specifies the fuzzy probability of each failure degree of the super-level event corresponding to the TS fuzzy gate, as well as the failure degree of the subordinate events of the super-level event. The super-level event corresponding to the TS fuzzy gate is an intermediate event or a top event. The subordinate events of the super-level event are bottom events and / or intermediate events.

[0028] Figure 2 is a simple TS fuzzy fault tree, where 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 Bottom event x 2 and x The fuzzy failure rate of 3 is calculated.

[0029] In traditional fault trees, failure probabilities are derived from historical maintenance data. However, due to the lack of historical data and the diverse environments of drive systems, accurate failure probabilities cannot be determined. This invention introduces fuzzy logic into the fault tree, converting failure data derived from expert systems and experience into fuzzy failure rates, thereby calculating the failure probability of the drive system. Similarly, the fault states of various drive system components can also be characterized using fuzzy numbers.

[0030] The present invention expresses the failure rate and failure degree of each component of the drive system with fuzzy numbers, and divides the failure degree into three levels: no failure, slight failure, and severe failure, which are expressed by fuzzy numbers 0, 0.5, and 1 respectively.

[0031] The following assumptions are made: ①The rule of 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; ③The failure probability of the bottom event is recorded as , k n For the n The bottom event k Fault status, the value is selected 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. The first fault degree of the bottom event; ⑤ The 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.

[0032] 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 the eight TS fuzzy gates. The numbers and names of all bottom events are shown in Table 1.

[0033] Table 1 Bottom event numbers and names

[0034]

[0035] 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 propeller failure state is more obvious during daily use and can be replaced in time, the failure 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 x2 The value corresponding to each rule is its fault degree, y The value of 1 corresponding to each rule is its fuzzy probability, and the same applies to Tables 3 to 5. In addition, the number of rules in the 8 TS fuzzy gates is appropriately selected according to the actual situation. The value of the fault degree can be 0 or 1, or 0, 0.5, or 1 or more values.

[0036] Table 2 The first TS fuzzy gate

[0037]

[0038] Table 3 The second TS fuzzy gate

[0039]

[0040] Table 4 The third TS fuzzy gate

[0041]

[0042] Table 5 The 8th TS fuzzy gate

[0043]

[0044] 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.

[0045] Specifically, the number of failures and total operating hours of each component of the drone's drive system within a set time period are obtained. For any component of the drone's drive system, the failure rate of the component is determined based on the ratio of the number of failures to the total operating hours of the component within the set time period.

[0046] 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.

[0047] According to the expert system and empirical data, the life of components in 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.

[0048] Table 6 Failure rate of bottom events

[0049]

[0050] S3: Determine a first fuzzy fault set for each intermediate event and a first fuzzy fault set for the top event based on the failure rates of each bottom event corresponding to each failure severity and the TS fuzzy fault tree. The first fuzzy fault set includes a first fuzzy failure rate corresponding to each failure severity.

[0051] Step S3 analyzes the failure rate of the drive system based on the fuzzy failure rate of the components. , then the failure rate of the bottom event is , The fuzzy failure rate is .

[0052] Let the failure rate of each component be the fuzzy failure rate of the bottom event, then The first execution level of a rule for .

[0053] Then the first fuzzy failure rate of the upper-level event is .in, For the q n The first fuzzy failure rate of the upper-level event, For the q n The vague possibility of a higher-level event.

[0054] Specifically, for any upper-level event in the TS fuzzy fault tree, the first fuzzy fault set of the upper-level event is determined based on the first fuzzy fault sets of each of the lower-level events of the upper-level event and the TS fuzzy gate corresponding to the upper-level event. The first fuzzy failure rate of each fault severity corresponding to the bottom event is the failure rate of each fault severity corresponding to the bottom event.

[0055] In this embodiment, the following formula is used to determine the upper level event A The corresponding fault degree is c The first fuzzy failure rate.

[0056] .

[0057] .

[0058] in, For higher-level events A The corresponding TS fuzzy gate The first execution degree of a rule, A n For higher-level events A The number of subordinate events, For higher-level events A The corresponding TS fuzzy gate The rules stipulatei The failure degree of each subordinate event, for The first fuzzy failure rate, For higher-level events A The corresponding fault degree is c The first fuzzy failure rate, A r For higher-level events A The number of rules corresponding to the TS fuzzy gate, For higher-level events A The corresponding TS fuzzy gate A higher-level event specified by the rules A The degree of failure is c The vague possibility.

[0059] In order to better understand the technical solution of the present invention, it is assumed that the failure rate when the fault degree is 0.5 is equal to the failure rate when the fault 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.

[0060] .

[0061] but y The calculation formula of the first fuzzy failure rate corresponding to the failure degree of 1 is: . y 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: .

[0062] Similarly, the first fuzzy failure rate of all intermediate events and bottom events can be calculated, as shown in Table 7.

[0063] Table 7 First fuzzy failure rate of intermediate events and top events

[0064]

[0065] Comparing the data in Table 7 shows that the fuzzy probability of intermediate and bottom events is roughly the same order of magnitude, and the probability of the bottom event is lower than that of the intermediate event, which is consistent with reality. Analysis of the first fuzzy failure rates of top, intermediate, and bottom events reveals that the probability of critical damage is low when components are in their initial states. Furthermore, for relatively simple systems like drone drive systems, using the TS fuzzy fault tree to derive the fuzzy failure rate of the top event from the fuzzy failure rate of the bottom event is applicable.

[0066] S4: Simulate the fault degree of each bottom event and determine the membership function of each bottom event.

[0067] 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.

[0068] Depend on Figure 4 The equation for fuzzy number can be obtained as follows.

[0069] .

[0070] in, is the fuzzy number of failure rate and failure degree.

[0071] 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.

[0072] .

[0073] .

[0074] .

[0075] in, for x The membership value is 1, for x The membership value of 2, for x The membership value is 3, F The value of is 0, 0.5 or 1.

[0076] 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.

[0077] Specifically, x 1 as an example, using the above x The membership function of 1 can be calculated xThe 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.

[0078] Table 8 Fault degree and membership value of bottom events

[0079]

[0080] S6: Based on the membership values ​​of the bottom events corresponding to the respective fault severity levels and the TS fuzzy fault tree, determine a second fuzzy fault set for each intermediate event and a second fuzzy fault set for the top event. The second fuzzy fault set includes a second fuzzy failure rate corresponding to each fault severity level. The first and second fuzzy fault sets of the top events serve as the fault diagnosis results for the drone drive system.

[0081] Step S6 analyzes the failure rate of the drive system according to the degree of failure of the components. ,make , the fault degree of the bottom event is quantified using the membership function, and the fuzzy membership degrees of fault degrees 0, 0.5, and 1 are 、 、 .

[0082] No. The initial second execution of the rule for .

[0083] Normalize the above formula to get The second execution level of the rule for .

[0084] Then the second fuzzy failure rate of the upper-level event is .in, For the q n The second fuzzy failure rate of the upper-level event, For the q n The vague possibility of a higher-level event.

[0085] Specifically, for any upper-level event in the TS fuzzy fault tree, the second fuzzy fault set of the upper-level event is determined based on the second fuzzy fault sets of each of the lower-level events of the upper-level event and the TS fuzzy gate corresponding to the upper-level event. The second fuzzy fault rate of each fault severity corresponding to the bottom event is the membership value of each fault severity corresponding to the bottom event.

[0086] In this embodiment, the following formula is used to determine the upper level event A The corresponding fault degree is c The second fuzzy failure rate.

[0087] .

[0088] .

[0089] in, For higher-level events A The corresponding TS fuzzy gate The second execution degree of the rule, A n For higher-level events A The number of subordinate events, For higher-level events 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 higher-level events A The number of rules corresponding to the TS fuzzy gate, For higher-level events A The corresponding fault degree is c The second fuzzy failure rate, For higher-level events A The corresponding TS fuzzy gate A higher-level event specified by the rules A The degree of failure is c The vague possibility.

[0090] In order to better understand the technical solution of the present invention, the following Figure 3 Intermediate events in TS fuzzy fault tree y The specific calculation formula of the second fuzzy failure rate corresponding to the failure degree of 0 is 1.

[0091] .

[0092] Similarly, the second fuzzy failure rate of all intermediate events and bottom events can be calculated, as shown in Table 9.

[0093] Table 9 Second fuzzy failure rate of intermediate events and top events

[0094]

[0095] Through the above calculation, the top event y8 The second fuzzy failure rates when the failure degrees are 0, 0.5, and 1 are: 9.8%, 7.4%, and 82.8%.

[0096] 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.

[0097] 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 a reliability analysis is performed. Two methods are used in the calculation, namely, calculating the first fuzzy failure rate of the drive system based on the fuzzy failure rate of the components and calculating the second fuzzy failure rate of the drive system based on 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 failure rate assessment of the present invention is more accurate.

[0098] 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, abbreviated as I / O) and a communication interface. The processor, memory and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. 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 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.

[0099] 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. When the processor executes the computer program, the steps in the above-mentioned method for diagnosing faults in a drone drive system are implemented.

[0100] 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 drone drive system fault diagnosis method are implemented.

[0101] In one embodiment, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the steps in the above-mentioned method for diagnosing faults in a drone drive system.

[0102] 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.

[0103] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. 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 above-mentioned embodiments. In particular, any reference to memory, database, or other media 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), magnetic 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. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processors (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), data processing logic devices based on quantum computing, and the like.

[0104] The technical features of the above embodiments can 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.

[0105] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only intended to help understand the method and core concept of the present invention. At the same time, those skilled in the art will find that the specific implementation methods and application scopes may vary based on the concept of the present invention. In summary, the contents of this specification should not be construed as limiting the present invention.

Claims

1. A method for diagnosing faults in a UAV drive system, characterized in that: The UAV drive system fault diagnosis method includes: 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 faults of various components 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; Determine, based on the failure rates of the bottom events corresponding to the failure degrees and the TS fuzzy fault tree, a first fuzzy fault set of each intermediate event and a first fuzzy fault set of the top event; the first fuzzy fault set includes a first fuzzy failure rate corresponding to each failure degree; 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 failure 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 UAV drive system fault diagnosis method 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 hours of each component of the drone 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 to the total operating time within a set time period.

3. The UAV drive system fault diagnosis method according to claim 1, characterized in that: Each TS fuzzy gate includes multiple rules, each rule is 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 drive system according to claim 3, wherein: Determining the first fuzzy fault set of each intermediate event and the first fuzzy fault set of the top event according to the failure rate of each bottom event corresponding to each fault degree and the TS fuzzy fault tree 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 based on 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 failure rate of each fault degree corresponding to the bottom event is the failure rate of each fault degree corresponding to the bottom event.

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

6. The method for diagnosing a fault in a UAV drive system according to claim 3, wherein: Determining the second fuzzy fault set of each intermediate event and the second fuzzy fault set of the top event according to the membership value of each bottom event corresponding to each fault degree and the TS fuzzy fault tree specifically includes: For any upper-level event in the TS fuzzy fault tree, the second fuzzy fault set of the upper-level event is determined based on 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 drive system according to claim 6, wherein: Use the following formula to determine the parent event A The corresponding fault degree is c The second fuzzy failure rate: ； ； in, For higher-level events A The corresponding TS fuzzy gate The second execution degree of the rule, A n For higher-level events A The number of subordinate events, For higher-level events 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 higher-level events A The number of rules corresponding to the TS fuzzy gate, For higher-level events A The corresponding fault degree is c The second fuzzy failure rate, For higher-level events A The corresponding TS fuzzy gate A higher-level event specified by the rules A The degree of failure is c The vague possibility.

8. The UAV drive system fault diagnosis method 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, signal transmission 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 drive system according to claim 1, wherein: 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 method for diagnosing faults in a drone drive system according to any one of claims 1 to 9.

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