Fault detection method, device and equipment of electric drive assembly, medium and vehicle

Abstract

The invention relates to a fault detection method, device and equipment of an electric drive assembly, a medium and a vehicle. The fault detection method for the electric drive assembly comprises the steps that basic information of the electric drive assembly, driving information of a vehicle and vibration and noise signals are obtained; based on whether there is a knocking signal in the vibration and noise signals, preliminarily performing fault classification to obtain an initial fault type; and performing fault analysis based on the basic information, the driving information and the vibration and noise signals by using a fault analysis strategy matched with the initial fault type to obtain a fault analysis result of the electric drive assembly. According to the embodiment of the invention, when the electric drive assembly has a fault, the final fault type and the fault position can be identified in a manner of first determining the initial fault type, then determining the final fault type and positioning the fault, so that the maintenance and repair end can quickly position and repair the fault position, and the maintenance efficiency is improved. And the maintenance and repair cost can be reduced.

Description

Technical Field

[0001] This disclosure relates to the field of vehicle technology, and in particular to a method, apparatus, equipment, medium, and vehicle for fault detection of an electric drive assembly. Background Technology

[0002] The electric drive system is a crucial core system in new energy vehicles. It integrates the power source that drives the vehicle and is responsible for efficiently converting the electrical energy stored in the battery into mechanical kinetic energy on the wheels and the road surface. Therefore, whether the electric drive system malfunctions is one of the key factors in whether the vehicle can drive safely.

[0003] Currently, vehicle fault detection systems can only identify whether the electric drive assembly has a fault, but cannot identify the specific fault type or the specific location of the fault within the electric drive assembly. Therefore, it is necessary for maintenance and repair personnel to disassemble the vehicle and use specialized equipment for testing in order to discover the specific fault type and location. This is inconvenient for the maintenance and repair of the electric drive assembly and increases maintenance and repair costs. Summary of the Invention

[0004] To address the aforementioned technical problems, this disclosure provides a method, apparatus, equipment, medium, and vehicle for fault detection of an electric drive assembly.

[0005] In a first aspect, this disclosure provides a fault detection method for an electric drive assembly, including:

[0006] Acquire basic information about the electric drive assembly, vehicle driving information, and vibration and noise signals;

[0007] Based on the presence of impact signals in the vibration and noise signals, a preliminary fault classification is performed to obtain the initial fault type.

[0008] Using a fault analysis strategy that matches the initial fault type, fault analysis is performed based on basic information, driving information, and vibration and noise signals to obtain the fault analysis results of the electric drive assembly. The fault analysis results include the final fault type and fault location.

[0009] Optionally, a fault analysis strategy matching the initial fault type is used to perform fault analysis based on basic information, driving information, and vibration and noise signals to obtain fault analysis results, including:

[0010] If the initial fault type is non-knock type, then frequency domain analysis is performed on the vibration and noise signals, and based on basic information and driving information, the preset order howling features corresponding to each preset component are extracted.

[0011] For each preset component, fault analysis is performed based on the preset order howling characteristics corresponding to the preset component, thereby obtaining the fault analysis results.

[0012] Optionally, fault analysis is performed based on preset order howling characteristics corresponding to preset components to obtain fault analysis results, including:

[0013] For each preset order howling feature, the sound pressure level value of the preset order howling feature is compared with its corresponding preset threshold. If the sound pressure level value of the preset order howling feature is greater than its corresponding preset threshold, the preset order howling feature is determined as the target howling feature.

[0014] If a preset component corresponds to at least one target howling feature, then the final fault type is determined to be howling, and the fault location includes the preset component.

[0015] Optionally, the method further includes:

[0016] Based on the target whistling characteristics, the faulty component causing the whistling is located in the preset components, and the fault location is determined to include the faulty component.

[0017] Optionally, based on the target whistling characteristics, the faulty component causing the whistling is located in a preset set of components, including:

[0018] In the preset first association relationship, the component corresponding to the target howling feature is queried, and the queried component is identified as the faulty component. The preset first association relationship is the association relationship between the howling feature and the component.

[0019] Optionally, a fault analysis strategy matching the initial fault type is used to perform fault analysis based on basic information, driving information, and vibration and noise signals to obtain fault analysis results, including:

[0020] If the initial fault type is knocking, then the final fault type is determined to be knocking, and the vibration and noise signals are analyzed in the time and frequency domains. Based on the basic information and driving information, knocking features are extracted.

[0021] Based on the characteristics of the knocking, the faulty component causing the knocking in the electric drive assembly is located, and the fault location is determined to include the faulty component.

[0022] Optionally, based on the characteristics of the knocking, the faulty component in the electric drive assembly that caused the knocking can be located, including:

[0023] In the preset second association relationship, the component corresponding to the knocking feature is queried, and the queried component is identified as the faulty component. The preset second association relationship is the association relationship between the knocking feature and the component.

[0024] Secondly, this disclosure provides a fault detection device for an electric drive assembly, comprising:

[0025] The acquisition module is used to acquire basic information about the electric drive assembly, vehicle driving information, and vibration and noise signals.

[0026] The classification module is used to perform preliminary fault classification based on the presence of impact signals in vibration and noise signals to obtain the initial fault type.

[0027] The analysis module is used to perform fault analysis based on basic information, driving information, and vibration and noise signals using a fault analysis strategy that matches the initial fault type. The results of the fault analysis include the final fault type and fault location.

[0028] Thirdly, this disclosure provides an electronic device, including:

[0029] processor;

[0030] Memory, used to store executable instructions;

[0031] The processor is used to read executable instructions from memory and execute the executable instructions to implement the fault detection method of the electric drive assembly described in the first aspect.

[0032] Fourthly, this disclosure provides a computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to implement the fault detection method for the electric drive assembly described in the first aspect.

[0033] Fourthly, this disclosure provides a vehicle, including:

[0034] The fault detection device for the electric drive assembly described in the second aspect;

[0035] The electronic equipment described in the third aspect;

[0036] The computer-readable storage medium described in the fourth aspect.

[0037] The technical solution provided in this disclosure has the following advantages compared with the prior art:

[0038] The fault detection method, apparatus, device, medium, and vehicle of the electric drive assembly disclosed in this embodiment can acquire basic information of the electric drive assembly, vehicle driving information, and vibration and noise signals. Based on the presence of impact signals in the vibration and noise signals, an initial fault type is initially classified. Then, using a fault analysis strategy matched to the initial fault type, fault analysis is performed based on the basic information, driving information, and vibration and noise signals to obtain the fault analysis result of the electric drive assembly. The fault analysis result includes the final fault type and fault location. Therefore, according to the above technical solution, detailed fault detection of the electric drive assembly can be achieved by first initially classifying the fault type, then classifying the final fault type and fault location. Furthermore, because the final fault type and fault location can be identified when a fault occurs in the electric drive assembly, maintenance and repair personnel can quickly locate and repair the fault, which helps reduce maintenance and repair costs. Attached Figure Description

[0039] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and the originals and elements are not necessarily drawn to scale.

[0040] Figure 1 A flowchart illustrating a fault detection method for an electric drive assembly provided in an embodiment of this disclosure is shown.

[0041] Figure 2 A schematic diagram of a vibration signal provided in an embodiment of this disclosure is shown;

[0042] Figure 3 A schematic diagram of a tapping feature provided in an embodiment of this disclosure is shown;

[0043] Figure 4 A schematic diagram of a second noise signal provided in an embodiment of this disclosure is shown;

[0044] Figure 5 A schematic diagram of a 1H1 order howling feature provided in an embodiment of this disclosure is shown;

[0045] Figure 6 This diagram illustrates the structure of a fault detection device for an electric drive assembly according to an embodiment of the present disclosure.

[0046] Figure 7 A schematic diagram of the structure of an electronic device provided in an embodiment of this disclosure is shown. Detailed Implementation

[0047] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0048] It should be understood that the steps described in the method embodiments of this disclosure may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of this disclosure is not limited in this respect.

[0049] The term "comprising" and its variations as used herein are open-ended inclusions, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the description below.

[0050] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are used only to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0051] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0052] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0053] To address the aforementioned problems, this disclosure provides a method, apparatus, device, and medium for fault detection of an electric drive assembly. The fault detection method for an electric drive assembly provided in this disclosure will be described first.

[0054] Figure 1 A flowchart illustrating a fault detection method for an electric drive assembly provided in an embodiment of this disclosure is shown.

[0055] In some embodiments of this disclosure, Figure 1The fault detection method for the electric drive assembly shown is applicable to scenarios where faults are detected in the electric drive assembly of a vehicle. This method can be executed by an electronic device, which can be understood, for example, as a vehicle infotainment system, but is not limited thereto.

[0056] like Figure 1 As shown, the fault detection method for this electric drive assembly may include the following steps.

[0057] S110: Acquire basic information about the electric drive assembly, vehicle driving information, and vibration and noise signals.

[0058] Specifically, the basic information of the electric drive assembly consists of the most fundamental and critical characteristic data (i.e., design parameters) used to describe the electric drive assembly. This information helps in the management and maintenance of the electric drive assembly. For example, it may include motor parameters and / or mechanical transmission parameters, including gear parameters (such as the number of gears, the number of rollers, the inner and outer diameters, the contact angle, etc.), but is not limited to these.

[0059] In some embodiments, basic information about the electric drive assembly can be extracted from its instruction manual and stored locally beforehand, so that it can be retrieved locally when fault detection of the electric drive assembly is required. Of course, in other embodiments, a first request can be sent to a cloud platform to obtain basic information about the electric drive assembly from the cloud platform. However, this is not the only possible approach.

[0060] Specifically, vehicle driving information refers to data used to describe various states and performance of the vehicle during operation, which is crucial for vehicle management and maintenance. For example, it may include vehicle speed, electric drive speed, and / or electric drive torque, but is not limited to these.

[0061] In some embodiments, vehicle driving information can be read via the CAN bus, but this is not the only possibility.

[0062] Specifically, the sensors include vibration sensors and noise sensors, and correspondingly, the vibration and noise signals include vibration signals and noise signals.

[0063] In some embodiments, the vibration and noise signals include a first noise signal, a second noise signal, and a vibration signal. The first noise signal is detected by an in-vehicle noise sensor, the second noise signal is detected by a near-field noise sensor disposed on the electric drive assembly, and the vibration signal is detected by a vibration sensor disposed on the electric drive assembly, but is not limited thereto.

[0064] S120. Based on whether there is a knocking signal in the vibration and noise signals, the initial fault type is obtained by preliminary fault classification.

[0065] Specifically, the knocking signal is the vibration and noise signal generated by a knocking event, such as the impact of internal components of a motor, gear collision, piston knocking, etc.

[0066] Specifically, if there is a knocking signal in the vibration and noise signals, the initial fault type is determined to be knocking; otherwise, the initial fault type is determined to be non-knocking.

[0067] In some embodiments, detecting whether a knocking signal exists in the vibration and noise signals includes: detecting whether a brief, rhythmic pulse waveform exists in the time-domain waveforms of the vibration and noise signals; if so, it is determined that a knocking signal exists in the vibration and noise signals; otherwise, it is determined that no knocking signal exists in the vibration and noise signals.

[0068] Specifically, "brief" here refers to the duration of each pulse being less than or equal to a first preset threshold, and "rhythmic" here refers to the time interval between two adjacent pulses being the same or similar.

[0069] For example, the vibration signal and noise signal include a first noise signal, a second noise signal and a vibration signal. If at least one of the three—the first noise signal, the second noise signal and the vibration signal—has a brief, rhythmic pulse waveform in its time domain waveform, then it is determined that there is a knocking signal in the vibration and noise signal; otherwise, it is determined that there is no knocking signal in the vibration and noise signal.

[0070] In other embodiments, detecting whether a knocking signal exists in the vibration and noise signal includes: inputting the vibration and noise signal into a pre-trained first neural network model, and obtaining the result output by the first neural network model regarding whether a knocking signal exists in the vibration and noise signal.

[0071] S130. Using a fault analysis strategy that matches the initial fault type, fault analysis is performed based on basic information, driving information, and vibration and noise signals to obtain the fault analysis results of the electric drive assembly. The fault analysis results include the final fault type and fault location.

[0072] Specifically, the fault analysis strategies differ for impact-related and non-impact-related faults. The fault analysis results for the electric drive assembly include two types: when the electric drive assembly has no fault, the fault analysis result is "healthy"; when the electric drive assembly has a fault, the fault analysis result includes the final fault type and fault location. The final fault type refers to the ultimately identified fault type of the electric drive assembly, and the fault location refers to the ultimately identified faulty component or part within the electric drive assembly.

[0073] In some embodiments, S130 includes: S131, if the initial fault type is non-knock type, then perform frequency domain analysis on the vibration and noise signals, and extract the preset order howling features corresponding to each preset component based on basic information and driving information.

[0074] Specifically, the preset components are the parts of the electric drive assembly to be tested for faults. These components are part of the electric drive assembly, performing specific functional modules relatively independently, and are composed of multiple parts. For example, the preset components include motors, reducers, controllers, and / or bearings, but are not limited to these.

[0075] Specifically, each preset component has its own corresponding preset order. Accordingly, for each preset component, the whistling characteristics of its corresponding preset order (i.e., preset order whistling characteristics) are extracted. The preset order whistling characteristics are used to characterize the correlation between sound pressure level and electric drive speed. For example, for a motor, its corresponding 2P / 6P / 12P / 18P (P refers to the number of pole pairs) whistling characteristics are extracted; for a reducer, its corresponding 1H1 / 1H2 / 1H3 / 2H1 / 2H2 / 2H4 (i.e., first-stage gear first harmonic / first-stage gear second harmonic / first-stage gear third harmonic / second-stage gear first harmonic / second-stage gear second harmonic / second-stage gear fourth harmonic) whistling characteristics are extracted; for a controller, its corresponding 3P / 6P (P refers to the number of pole pairs) whistling characteristics are extracted; and for a bearing, its corresponding harmonic order whistling characteristics are extracted, but this is not limited to these.

[0076] In some examples, S131 includes: if the initial fault type is non-knock type, then the vibration and noise signals are filtered and FFT analyzed in sequence, and based on the basic information and driving information, the preset order howling features corresponding to each preset component are extracted.

[0077] In some other examples, S131 includes: if the initial fault type is non-knock type, then inputting vibration and noise signals, basic information and driving information into a pre-trained second neural network model, and obtaining the preset order howling features corresponding to each preset component output by the second neural network model.

[0078] S132. For each preset component, perform fault analysis based on the preset order howling characteristics corresponding to the preset component to obtain the fault analysis results.

[0079] Specifically, for each preset component, fault analysis is performed based on the preset order howling characteristics corresponding to the preset component to obtain the fault analysis result of the preset component. When the preset component does not have a fault, the fault analysis result of the preset component is "no fault". When the preset component has a fault, the fault analysis result of the preset component is "the preset component has a fault and the fault type is howling". The fault analysis results of each preset component are summarized to obtain the fault analysis result of the electric drive assembly.

[0080] In some examples, S132 includes: for each preset order howling feature, comparing the sound pressure level value of the preset order howling feature with a fixed threshold; if the sound pressure level value of the preset order howling feature is greater than the fixed threshold, then the preset order howling feature is determined to be a target howling feature; and if a preset component corresponds to at least one target howling feature, then the final fault type is determined to be howling, and the fault location includes the preset component. In this way, each preset order howling feature corresponds to the same fixed threshold, which helps reduce the computational complexity of detecting whether a preset component is howling.

[0081] In other examples, S132 includes: for each preset order howling feature, comparing the sound pressure level value of the preset order howling feature with its corresponding preset threshold, and if the sound pressure level value of the preset order howling feature is greater than its corresponding preset threshold, then determining the preset order howling feature as a target howling feature, and if the preset component corresponds to at least one target howling feature, then determining the final fault type as howling, and determining the fault location including the preset component.

[0082] Specifically, each preset order of howling characteristic of the same preset component has its own corresponding preset threshold. For example, as shown in Table 1, the preset thresholds corresponding to the 2P / 6P / 12P / 18P howling characteristics of the motor are 90 / 90 / 90 / 85, respectively; the preset thresholds corresponding to the 1H1 / 1H2 / 1H3 / 2H1 / 2H2 / 2H4 howling characteristics of the reducer are 85 / 75 / 70 / 95 / 90 / 95, respectively. If the bearing has a harmonic howling characteristic, a fault occurs; if there is no harmonic howling characteristic, no fault occurs (which can be understood as the preset threshold being 0), but it is not limited to this.

[0083] Table 1

[0084]

[0085] Accordingly, when detecting whether a preset component has malfunctioned, for each preset order howling feature of the preset component, the sound pressure level value of the preset order howling feature is compared with its corresponding preset threshold. If the sound pressure level value of the preset order howling feature is greater than its corresponding preset threshold, the preset order howling feature is determined to be the target howling feature. If the preset component corresponds to at least one target howling feature, the fault analysis result of the preset component is determined to be that the preset component has malfunctioned and the fault type is howling (i.e., the final fault type is howling and the fault location includes the preset component).

[0086] Of course, if the preset component corresponds to zero target howling features, then the fault analysis result of the preset component is determined to be no fault.

[0087] Of course, the fault analysis results of the electric drive assembly can also include fault severity parameters. Accordingly, when a preset component fails, the difference between the sound pressure level of the target howling feature and the corresponding preset threshold can be calculated for the target howling feature, and the fault severity parameters can be determined to include this difference.

[0088] It is understood that when a preset component exhibits a whistling sound, the sound pressure level characteristics of its corresponding different order whistling features may differ. Therefore, in this embodiment of the present disclosure, when detecting whether a preset component has malfunctioned, the sound pressure level value of each preset order whistling feature of the preset component is compared with its corresponding preset threshold. This can more accurately determine whether the preset component exhibits the preset order whistling sound, thereby improving the accuracy of fault detection of the preset component.

[0089] It is also understood that, in the embodiments of this disclosure, by setting up a preset component for each preset component, the preset component is detected to determine whether it is faulty, based on the preset order whistling characteristics corresponding to the preset component. This enables fine-grained (at the component level) fault detection in the electric drive assembly, thereby making the fault location in the electric drive assembly more accurate. This is beneficial for maintenance and repair personnel to quickly locate the fault location and carry out timely repairs.

[0090] Optionally, the method further includes: locating the faulty component causing the whistling in a preset component based on the target whistling characteristics, and determining that the fault location includes the faulty component.

[0091] It is understandable that different components within the same part may exhibit different levels of whistling characteristics. Therefore, based on the target whistling characteristics, the component causing the whistling can be further located within a preset part. This allows for more granular (at the component level) fault detection in the electric drive assembly, resulting in more accurate fault location within the electric drive assembly. This facilitates rapid fault location and timely repair by maintenance and repair personnel.

[0092] Further optionally, based on the target whistling feature, the faulty component causing the whistling is located in the preset components, including: querying the component corresponding to the target whistling feature in the preset first association relationship, and determining the queried component as the faulty component, wherein the preset first association relationship is the association relationship between the whistling feature and the component.

[0093] Specifically, the specific content of the preset first association relationship can be set by those skilled in the art according to the actual situation, such as being calibrated in advance through relevant calibration tests, but is not limited to this.

[0094] In one example, the preset first association can be pre-stored locally, so that it can be read from locally when fault detection of the electric drive assembly is required. Of course, in other examples, a second request can be sent to the cloud platform to obtain the preset first association returned by the cloud platform. But it is not limited to this.

[0095] Understandably, determining faulty components by querying the pre-defined first association makes locating faulty components simple and convenient, which helps reduce the difficulty of implementation.

[0096] Of course, in another example, based on the target squealing features, the faulty component causing the squealing is located in the preset components, including: inputting the target squealing features and the basic information of the preset components to which the target squealing features belong into a pre-trained third neural network model, and obtaining the faulty component output by the third neural network model.

[0097] In some other embodiments, S130 includes: S133, if the initial fault type is knocking, then determine the final fault type as knocking, and perform time-domain and frequency-domain analysis on the vibration and noise signals, and extract knocking features based on basic information and driving information.

[0098] Specifically, striking characteristics include striking order (i.e., the order in which the striking event occurs), but are not limited to this.

[0099] In some examples, S133 includes: if the initial fault type is a knocking type, then the vibration and noise signals are sequentially filtered and subjected to wavelet analysis, and knocking features are extracted based on basic information and driving information.

[0100] In some other examples, S133 includes: if the initial fault type is a knocking type, then inputting the vibration and noise signals, basic information and driving information into a pre-trained fourth neural network model, and obtaining the knocking features output by the fourth neural network model.

[0101] S134. Based on the knocking characteristics, locate the faulty component in the electric drive assembly that caused the knocking, and determine the fault location including the faulty component.

[0102] Optionally, S134 includes: querying the component corresponding to the striking feature in a preset second association relationship, and identifying the queried component as a faulty component, wherein the preset second association relationship is the association relationship between the striking feature and the component.

[0103] Specifically, the specific content of the preset second association relationship can be set by those skilled in the art according to the actual situation, such as by pre-calibrating through calibration tests, but is not limited thereto. For example, the preset second association relationship includes the knocking characteristics corresponding to the components in the reducer, the knocking characteristics corresponding to the components in the differential, and / or the knocking characteristics corresponding to the moving parts such as half shafts, but is not limited thereto.

[0104] In one example, the preset second association can be pre-stored locally, so that it can be read from locally when fault detection of the electric drive assembly is required. Of course, in other examples, a third request can be sent to the cloud platform to obtain the preset second association returned by the cloud platform. But it is not limited to this.

[0105] Understandably, determining the faulty component by querying a pre-defined second association makes locating the faulty component simple and convenient, which helps to reduce the difficulty of implementation.

[0106] Of course, in other examples, S134 includes: inputting the tapping features into a pre-trained fifth neural network model to obtain the faulty component output by the fifth neural network model.

[0107] It is also understandable that different components typically exhibit different levels of characteristics when struck. Therefore, the striking characteristics can be used to further pinpoint the component that is being struck. This allows for more granular (component-level) fault detection in the electric drive assembly, resulting in more accurate fault location within the electric drive assembly. This facilitates rapid fault location and timely repair by maintenance and repair teams.

[0108] In some other embodiments, S130 includes the above-described S131-S134.

[0109] In some other embodiments, S130 includes: inputting the initial fault type, basic information, driving information, and vibration and noise signals into a pre-trained sixth neural network model, and obtaining the fault analysis results of the electric drive assembly output by the sixth neural network model.

[0110] This embodiment of the invention enables detailed fault detection of the electric drive assembly by first identifying the initial fault type, then identifying the final fault type and fault location. Furthermore, since the final fault type and fault location can be identified when a fault occurs in the electric drive assembly, the maintenance and repair end can quickly locate and repair the fault, which helps to reduce maintenance and repair costs.

[0111] In another embodiment of this disclosure, the method further includes: outputting fault analysis results.

[0112] Specifically, fault analysis results can be output through voice broadcasts, display screens, etc., but are not limited to these methods.

[0113] It can be understood that by outputting fault analysis results, users can perform timely maintenance and repairs on the electric drive assembly, ensuring its normal operation and extending its lifespan.

[0114] The following describes in detail the fault detection method for the electric drive assembly provided in this disclosure embodiment with reference to two specific examples. The premise of these two examples is that the vibration and noise signals include a first noise signal, a second noise signal, and a vibration signal. The first noise signal is detected by an in-vehicle noise sensor, the second noise signal is detected by a near-field noise sensor installed on the electric drive assembly, and the vibration signal is detected by a vibration sensor installed on the electric drive assembly.

[0115] Example 1:

[0116] like Figure 2 As shown, a clear impact signal exists in the time-domain waveform of the vibration signal. Figure 2 (The text has been marked with a dashed box). After sequentially performing filtering and wavelet analysis on the vibration signal, and based on basic and driving information, the knocking features were extracted, indicating an abnormal state, such as... Figure 3 As shown. Further analysis reveals that the knocking feature is a 0.33-order feature. By querying the preset second correlation, it can be confirmed that the knocking originated from an abnormality in a tooth of the intermediate shaft, at which point an alarm can be issued.

[0117] Example 2:

[0118] like Figure 4 As shown, there is no obvious knocking signal in the time-domain waveform of the second noise signal. After performing filtering and FFT analysis on the second noise signal in sequence, the 1H1 order characteristic whistling is extracted based on basic information and driving information, as follows: Figure 5 As shown, the sound pressure level of the whistling is below 85dB (i.e., the preset threshold), so the whistling is a normal whistling and the electric drive is in a healthy state. At this time, an alarm can be issued.

[0119] In summary, the embodiments of this disclosure can detect faults in the electric drive assembly by monitoring vibration and noise signals, vehicle driving information, and other basic information about the electric drive assembly. When a fault is detected, the vehicle will issue an active safety warning to ensure driving and user safety. Simultaneously, maintenance and repair personnel can quickly locate and repair components in poor condition based on the fault analysis results.

[0120] Figure 6 A schematic diagram of a fault detection device for an electric drive assembly provided in an embodiment of this disclosure is shown. This fault detection device for the electric drive assembly can be understood as the aforementioned electronic device or a functional module within the aforementioned electronic device. Figure 6 As shown, the fault detection device 600 of the electric drive assembly includes:

[0121] The acquisition module 610 is used to acquire basic information of the electric drive assembly, vehicle driving information, and vibration and noise signals;

[0122] Classification module 620 is used to perform preliminary fault classification to obtain an initial fault type based on whether there is a knocking signal in the vibration and noise signals;

[0123] Analysis module 630 is used to perform fault analysis based on the basic information, the driving information, and the vibration and noise signals using a fault analysis strategy that matches the initial fault type, and to obtain the fault analysis result of the electric drive assembly. The fault analysis result includes the final fault type and fault location.

[0124] Optionally, the analysis module 630 includes:

[0125] The first extraction submodule is used to perform frequency domain analysis on the vibration and noise signals if the initial fault type is non-knock type, and extract the preset order howling features corresponding to each preset component based on the basic information and the driving information.

[0126] The analysis submodule is used to perform fault analysis on each of the preset components based on the preset order howling characteristics corresponding to the preset components, thereby obtaining the fault analysis results.

[0127] Optionally, the first analysis submodule is specifically used to compare the sound pressure level value of each preset order howling feature with its corresponding preset threshold, and if the sound pressure level value of the preset order howling feature is greater than its corresponding preset threshold, then the preset order howling feature is determined to be the target howling feature.

[0128] If the preset component corresponds to at least one target howling feature, then the final fault type is determined to be howling, and the fault location includes the preset component.

[0129] Optionally, the device further includes:

[0130] The first determining module is used to locate the faulty component causing the whistling in the preset components based on the target whistling characteristics, and to determine that the faulty location includes the faulty component.

[0131] Optionally, the first determining module is specifically used to query the component corresponding to the target howling feature in a preset first association relationship, and determine the queried component as the faulty component, wherein the preset first association relationship is the association relationship between the howling feature and the component.

[0132] Optionally, the analysis module 630 includes:

[0133] The second extraction submodule is used to determine the final fault type as knocking if the initial fault type is knocking, and to perform time-domain and frequency-domain analysis on the vibration and noise signals, and extract knocking features based on the basic information and the driving information.

[0134] The second determining module is used to locate the faulty component causing the knocking in the electric drive assembly based on the knocking characteristics, and to determine that the fault location includes the faulty component.

[0135] Optionally, the second determining module is specifically used to query the component corresponding to the striking feature in a preset second association relationship, and determine the queried component as the faulty component, wherein the preset second association relationship is the association relationship between the striking feature and the component.

[0136] Optionally, the device further includes:

[0137] The output module is used to output the fault analysis results.

[0138] The apparatus provided in this embodiment can execute the methods of any of the above embodiments, and its execution method and beneficial effects are similar, so they will not be described again here.

[0139] Figure 7 A schematic diagram of the structure of an electronic device provided in an embodiment of this disclosure is shown.

[0140] like Figure 7 As shown, the electronic device may include a controller 701 and a memory 702 storing computer program instructions.

[0141] Specifically, the controller 701 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0142] Memory 702 may include a large-capacity storage for information or instructions. For example, and not limitingly, memory 702 may include a hard disk drive (HDD), a floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or a Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 702 may include removable or non-removable (or fixed) media. Where appropriate, memory 702 may be internal or external to the integrated gateway device. In a particular embodiment, memory 702 is a non-volatile solid-state memory. In a particular embodiment, memory 702 includes read-only memory (ROM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (Electrically Programmable ROM, EPROM), an electrically erasable programmable PROM (EEPROM), an electrically alterable ROM (EAROM), or flash memory, or a combination of two or more of these.

[0143] The controller 701 reads and executes computer program instructions stored in the memory 702 to perform the steps of the fault detection method for the electric drive assembly provided in this embodiment of the disclosure.

[0144] In one example, the electronic device may also include a transceiver 703 and a bus 704. Wherein, as... Figure 7 As shown, the controller 701, memory 702 and transceiver 703 are connected via bus 704 and communicate with each other.

[0145] Bus 704 includes hardware, software, or both. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industrial Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a MicroChannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses, or a combination of two or more of these. Where appropriate, bus 704 may include one or more buses. Although specific buses are described and illustrated in the embodiments of this application, this application considers any suitable bus or interconnection.

[0146] This disclosure also provides a computer-readable storage medium that can store a computer program. When the computer program is executed by a processor, the processor enables the processor to implement the fault detection method for the electric drive assembly provided in this disclosure.

[0147] The aforementioned storage medium may, for example, include a memory 702 containing computer program instructions, which can be executed by a controller 701 of an electronic device to complete the fault detection method for the electric drive assembly provided in this embodiment. Optionally, the storage medium may be a non-transitory computer-readable storage medium, such as a ROM, random access memory (RAM), compact disc read-only memory (CD-ROM), magnetic tape, floppy disk, and optical data storage device.

[0148] This disclosure also provides a vehicle, including at least one of the following:

[0149] The fault detection device for the electric drive assembly provided in the embodiments of this disclosure;

[0150] The electronic device provided in the embodiments of this disclosure;

[0151] The computer-readable storage medium provided in the embodiments of this disclosure.

[0152] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the term "comprising" is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus.

[0153] The above description is merely a specific embodiment of this disclosure, enabling those skilled in the art to understand or implement it. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this disclosure. Therefore, this disclosure is not to be limited to the embodiments described herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A fault detection method for an electric drive assembly, characterized in that, include: Acquire basic information about the electric drive assembly, vehicle driving information, and vibration and noise signals; Based on whether there is a knocking signal in the vibration and noise signals, a preliminary fault classification is performed to obtain the initial fault type. Using a fault analysis strategy that matches the initial fault type, fault analysis is performed based on the basic information, the driving information, and the vibration and noise signals to obtain the fault analysis results of the electric drive assembly. The fault analysis results include the final fault type and fault location.

2. The method according to claim 1, characterized in that, The fault analysis strategy, which matches the initial fault type, performs fault analysis based on the basic information, the driving information, and the vibration and noise signals to obtain fault analysis results, including: If the initial fault type is non-knock type, then frequency domain analysis is performed on the vibration and noise signals, and based on the basic information and the driving information, the preset order howling features corresponding to each preset component are extracted. For each of the preset components, a fault analysis is performed based on the preset order howling characteristics corresponding to the preset component, thereby obtaining the fault analysis result.

3. The method according to claim 2, characterized in that, The fault analysis based on the preset order howling characteristics corresponding to the preset component, thereby obtaining the fault analysis result, includes: For each of the preset order howling features, the sound pressure level value of the preset order howling feature is compared with its corresponding preset threshold. If the sound pressure level value of the preset order howling feature is greater than its corresponding preset threshold, then the preset order howling feature is determined to be the target howling feature. If the preset component corresponds to at least one target howling feature, then the final fault type is determined to be howling, and the fault location includes the preset component.

4. The method according to claim 3, characterized in that, The step of locating the faulty component causing the whistling sound within the preset components based on the target whistling characteristics includes: In the preset first association relationship, the component corresponding to the target howling feature is queried, and the queried component is identified as the faulty component. The preset first association relationship is the association relationship between the howling feature and the component.

5. The method according to claim 1, characterized in that, The fault analysis strategy, which matches the initial fault type, performs fault analysis based on the basic information, the driving information, and the vibration and noise signals to obtain fault analysis results, including: If the initial fault type is knocking, then the final fault type is determined to be knocking, and the vibration and noise signals are analyzed in the time and frequency domains, and knocking features are extracted based on the basic information and the driving information; Based on the knocking characteristics, the faulty component causing the knocking in the electric drive assembly is located, and the fault location is determined to include the faulty component.

6. The method according to claim 5, characterized in that, The method of locating the faulty component in the electric drive assembly that caused the knocking based on the knocking characteristics includes: In the preset second association relationship, the component corresponding to the knocking feature is queried, and the queried component is identified as the faulty component. The preset second association relationship is the association relationship between the knocking feature and the component.

7. A fault detection device for an electric drive assembly, characterized in that, include: The acquisition module is used to acquire basic information about the electric drive assembly, vehicle driving information, and vibration and noise signals. The classification module is used to perform preliminary fault classification based on whether there is a knocking signal in the vibration and noise signals to obtain an initial fault type. The analysis module is used to perform fault analysis based on the basic information, the driving information, and the vibration and noise signals using a fault analysis strategy that matches the initial fault type, and to obtain the fault analysis results of the electric drive assembly. The fault analysis results include the final fault type and fault location.

8. An electronic device, characterized in that, include: processor; Memory, used to store executable instructions; The processor is configured to read the executable instructions from the memory and execute the executable instructions to implement the fault detection method for the electric drive assembly according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The storage medium stores a computer program that, when executed by a processor, causes the processor to implement a fault detection method for the electric drive assembly according to any one of claims 1-6.

10. A vehicle, characterized in that, Includes at least one of the following: The fault detection device for the electric drive assembly as described in claim 7; The electronic device according to claim 8; The computer-readable storage medium of claim 9.

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