Component abnormity diagnosis method and device of wind generating set

Abstract

The invention relates to a component abnormity diagnosis method and device of a wind generating set. The component abnormity diagnosis method of the wind generating set comprises the following steps: converting a first sound signal acquired from a to-be-diagnosed component into a first digital signal, and determining the highest frequency of the first digital signal; generating a second acoustic signal for component abnormality diagnosis based on the first digital signal in response to the highest frequency being within a predetermined frequency range; and in response to the highest frequency being outside the predetermined frequency range, converting the first digital signal into a second digital signal, and generating a second acoustic signal for component abnormality diagnosis based on the second digital signal, the highest frequency of the second digital signal being within the predetermined frequency range. With the adoption of the method and the device, the ultrahigh-frequency acoustic emission signal can be converted into the sound wave which can be heard by human ears, so that the signal abnormal characteristics can be identified by using an economical, practical, light and rapid stethoscope, and the early faults of the blade, the main bearing, the variable pitch bearing and the like can be identified.

Description

Technical Field

[0001] The present disclosure relates to the technical field of wind power generation, and more specifically, to a method and device for diagnosing abnormality of components of a wind power generator set. Background Art

[0002] During the operation of a wind turbine, if the main bearing, pitch bearing, blades and other components fail, it will cause great losses. Conventional detection methods (such as vibration detection, temperature monitoring, grease detection, etc.) cannot detect component abnormalities early. When conventional detection methods are used to detect component abnormalities, the components are usually in the middle or late stage of failure, and the value of repairing and maintaining the components is relatively small.

[0003] Acoustic emission technology can be used to identify the surface waves emitted by components when they are deformed or cracked, and to collect and analyze them at ultra-high speed. It is often used to determine the early damage of components, and then serves the root cause analysis of failures, design optimization, unit life extension, etc., and has high application value. However, acoustic emission technology requires expensive professional equipment, professional testers for on-site deployment, and the use of professional analysis software. The test cycle is long, resulting in little value for large-scale promotion. Therefore, a simple, economical and fast solution is urgently needed. Summary of the invention

[0004] In order to solve the above problems, the present disclosure proposes a method and device for diagnosing abnormality of components of a wind turbine generator set, a computing system and a computer-readable storage medium.

[0005] According to one aspect of the present disclosure, a method for diagnosing component abnormality of a wind turbine generator set is provided, the method comprising: converting a first acoustic signal obtained from a component to be diagnosed into a first digital signal, and determining a maximum frequency of the first digital signal; in response to the maximum frequency being within a predetermined frequency range, generating a second acoustic signal for component abnormality diagnosis based on the first digital signal; in response to the maximum frequency being outside the predetermined frequency range, converting the first digital signal into a second digital signal, and generating a second acoustic signal for component abnormality diagnosis based on the second digital signal, wherein the maximum frequency of the second digital signal is within the predetermined frequency range.

[0006] Optionally, the predetermined frequency range is 20 Hz to 20000 Hz.

[0007] Optionally, the component to be diagnosed includes at least one of a blade, a main bearing and a pitch bearing.

[0008] Optionally, the highest frequency of the first digital signal is a predetermined multiple of the highest frequency of the second digital signal.

[0009] Optionally, the step of converting the first digital signal into a second digital signal includes: performing frequency reduction on a frequency of the first digital signal by the predetermined multiple.

[0010] Optionally, the component abnormality diagnosis method further includes: amplifying or reducing the gain of the second acoustic signal.

[0011] According to another aspect of the present disclosure, a component abnormality diagnosis device for a wind turbine generator set is provided, the component abnormality diagnosis device comprising: a frequency determination unit, for converting a first acoustic signal obtained from a component to be diagnosed into a first digital signal, and determining a maximum frequency of the first digital signal; an acoustic signal generation unit, for performing the following operations: in response to the maximum frequency being within a predetermined frequency range, generating a second acoustic signal for component abnormality diagnosis based on the first digital signal; and in response to the maximum frequency being outside the predetermined frequency range, converting the first digital signal into a second digital signal, and generating a second acoustic signal for component abnormality diagnosis based on the second digital signal, wherein the maximum frequency of the second digital signal is within the predetermined frequency range.

[0012] Optionally, the predetermined frequency range is 20 Hz to 20000 Hz.

[0013] Optionally, the component to be diagnosed includes at least one of a blade, a main bearing and a pitch bearing.

[0014] Optionally, the highest frequency of the first digital signal is a predetermined multiple of the highest frequency of the second digital signal.

[0015] Optionally, the step of converting the first digital signal into a second digital signal includes: performing frequency reduction on a frequency of the first digital signal by the predetermined multiple.

[0016] Optionally, the acoustic signal generating unit is further configured to amplify or reduce a gain of the second acoustic signal.

[0017] According to another aspect of the present disclosure, a component abnormality diagnosis device for a wind turbine generator set is provided, the component abnormality diagnosis device comprising: an acoustic emission sensor for acquiring a first acoustic signal from a component to be diagnosed; a controller configured to: convert the first acoustic signal into a first digital signal, and determine a maximum frequency of the first digital signal; in response to the maximum frequency being within a predetermined frequency range, generate a second acoustic signal for component abnormality diagnosis based on the first digital signal; in response to the maximum frequency being outside the predetermined frequency range, convert the first digital signal into a second digital signal, and generate a second acoustic signal for component abnormality diagnosis based on the second digital signal, wherein the maximum frequency of the second digital signal is within the predetermined frequency range; and a sound generating device configured to generate sound based on the second acoustic signal.

[0018] According to another aspect of the present disclosure, a computing system is provided that includes at least one computing device and at least one storage device storing instructions, wherein the instructions, when executed by the at least one computing device, prompt the at least one computing device to execute the component abnormality diagnosis method of the wind turbine generator set as described above.

[0019] According to another aspect of the present disclosure, a computer-readable storage medium storing instructions is provided, wherein when the instructions are executed by at least one computing device, the at least one computing device is prompted to execute the component abnormality diagnosis method of the wind turbine generator set as described above.

[0020] By adopting the present disclosure, ultra-high frequency acoustic emission signals can be converted into sound waves audible to the human ear, so that an economical, practical, lightweight and fast stethoscope can be used to identify abnormal signal characteristics for identifying early faults of blades, main bearings, pitch bearings, etc., which is suitable for on-site inspections of wind turbines and avoids problems such as long debugging time and high application complexity caused by the use of high-precision equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The above and / or other objects and advantages of the present disclosure will become more apparent through the following description of embodiments in conjunction with the accompanying drawings, in which:

[0022] Figure 1 is a flow chart showing a component abnormality diagnosis method of a wind turbine generator system according to an exemplary embodiment of the present disclosure;

[0023] Figure 2 is a block diagram showing a component abnormality diagnosis device of a wind turbine generator set according to an exemplary embodiment of the present disclosure;

[0024] Figure 3 A schematic diagram showing an ultra-high frequency electronic stethoscope according to an exemplary embodiment of the present disclosure;

[0025] Figure 4 is a block diagram illustrating a computing system including at least one computing device and at least one storage device storing instructions according to an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION

[0026] Below, in conjunction with the accompanying drawings, a description of a specific embodiment is provided to help the reader obtain a comprehensive understanding of the method, device and / or system described herein. However, after understanding the disclosure of the present application, various changes, modifications and equivalents of the method, device and / or system described herein will be clear. For example, the order of operations described herein is only an example and is not limited to those orders set forth herein, but can be changed as will be clear after understanding the disclosure of the present application, except for operations that must occur in a specific order. In addition, for greater clarity and simplicity, the description of features known in the art may be omitted.

[0027] Figure 1 is a flowchart illustrating a component abnormality diagnosis method of a wind turbine generator system according to an exemplary embodiment of the present disclosure.

[0028] like Figure 1 As shown, in step S101, a first acoustic signal obtained from a component to be diagnosed is converted into a first digital signal, and the highest frequency of the first digital signal is determined. In the example, the component to be diagnosed includes at least one of a blade, a main bearing, and a pitch bearing of a wind turbine. For example, an acoustic emission signal received from a component to be diagnosed can be converted into an electrical signal by an acoustic emission sensor, and the electrical signal can be converted into a digital signal.

[0029] In step S102, it is determined whether the highest frequency of the first digital signal is within a predetermined frequency range. In an example, the predetermined frequency range is 20 Hz to 20000 Hz, which corresponds to the audible frequency range of the human ear.

[0030] If it is determined in step S102 that the highest frequency of the first digital signal is within the predetermined frequency range, then in step S103, a second acoustic signal for component abnormality diagnosis is generated based on the first digital signal. If it is determined in step S102 that the highest frequency of the first digital signal is not within the predetermined frequency range, then in step S104, the first digital signal is converted into a second digital signal, and a second acoustic signal for component abnormality diagnosis is generated based on the second digital signal, wherein the highest frequency of the second digital signal is within the predetermined frequency range. In the example, the highest frequency of the first digital signal is a predetermined multiple (e.g., 20 times) of the highest frequency of the second digital signal. In the example, the step of converting the first digital signal into the second digital signal includes performing frequency reduction on the frequency of the first digital signal by a predetermined multiple.

[0031] Generally, the frequency of acoustic emission signals is above 200 kHz, while the frequency range recognizable by the human ear is from 20 Hz to 20,000 Hz. In the present disclosure, the frequency of the acoustic emission signal is reduced to the audible range of the human ear by means of high-frequency slow playback, and a conventional stethoscope (a conventional stethoscope can convert vibrations into sounds, for example, a medical stethoscope or a stethoscope for fault detection purposes) is used to listen to the frequency-reduced acoustic signal to identify whether there is damage or a fault in the components of the wind turbine generator.

[0032] To convert high-frequency signals outside the audible range of the human ear into low-frequency signals audible to the human ear, frequency reduction is required. Apply the scaling property of the Fourier transform:

[0033]

[0034] When, for example, a = 20, that is, after slow playback by 20 times, the corresponding Fourier spectrum changes from F(ω) to The corresponding frequency is reduced to 1 / 20 of the original frequency, so that the process of changing from inaudible ultrasonic signals to audible sound signals can be achieved. It should be clear that the high-frequency slow playback ratio is not limited to 1:20, but can be comprehensively set according to the audible range of the human ear and the sampling frequency.

[0035] In addition, in the example, after the second acoustic signal is generated, the gain of the second acoustic signal can be amplified or reduced. By amplifying or reducing the gain of the second acoustic signal, the sound loudness can be made suitable for the audible range of the human ear.

[0036] By adopting the method for diagnosing abnormal components of a wind turbine generator according to an exemplary embodiment of the present disclosure, ultra-high-frequency acoustic emission signals can be converted into audible sound waves, so that economic, practical, portable, and fast stethoscopes can be used to identify signal abnormal characteristics for identifying early faults of components such as blades, main bearings, and pitch bearings of wind turbine generators.

[0037] Figure 2 is a block diagram showing a device for diagnosing abnormal components of a wind turbine generator according to an exemplary embodiment of the present disclosure.

[0038] As Figure 2As shown, a component abnormality diagnosis device 200 of a wind turbine generator set according to an exemplary embodiment of the present disclosure includes: a frequency determination unit 201, which is used to convert a first acoustic signal obtained from a component to be diagnosed into a first digital signal, and determine the highest frequency of the first digital signal; an acoustic signal generation unit 202, which is used to perform the following operations: in response to the highest frequency of the first digital signal being within a predetermined frequency range, generating a second acoustic signal for component abnormality diagnosis based on the first digital signal; and in response to the highest frequency of the first digital signal being outside the predetermined frequency range, converting the first digital signal into a second digital signal, and generating a second acoustic signal for component abnormality diagnosis based on the second digital signal, wherein the highest frequency of the second digital signal is within the predetermined frequency range. In the example, the predetermined frequency range is 20 Hz to 20000 Hz. In the example, the component to be diagnosed includes at least one of a blade, a main bearing, and a pitch bearing of the wind turbine generator set. In the example, the highest frequency of the first digital signal is a predetermined multiple (e.g., 20 times) of the highest frequency of the second digital signal. In the example, the step of converting the first digital signal into the second digital signal includes: performing frequency reduction on the frequency of the first digital signal by a predetermined multiple. In the example, the acoustic signal generating unit 202 is further configured to amplify or reduce the gain of the second acoustic signal.

[0039] Combination of the above Figure 1 The specific operations shown are respectively Figure 2 The above operation is performed by corresponding units in the component abnormality diagnosis device 200 of the wind turbine generator set shown in the figure, and the specific operation details will not be repeated here.

[0040] By adopting the component abnormality diagnosis device of the wind turbine generator set according to the exemplary embodiment of the present disclosure, the ultra-high frequency acoustic emission signal can be converted into sound waves audible to the human ear, so that an economical, practical, lightweight and fast stethoscope can be used to identify the abnormal characteristics of the signal, so as to identify the early faults of the components such as the blades, main bearings, and pitch bearings of the wind turbine generator set.

[0041] Figure 3 A schematic diagram showing an ultra-high frequency electronic stethoscope according to an exemplary embodiment of the present disclosure.

[0042] like Figure 3As shown, the ultra-high frequency electronic stethoscope according to an exemplary embodiment of the present disclosure includes: an acoustic emission sensor 302 configured to obtain a first acoustic signal from a component to be diagnosed; a controller 304 configured to: convert the first acoustic signal into a first digital signal and determine the highest frequency of the first digital signal; in response to the highest frequency of the first digital signal being within a predetermined frequency range, generate a second acoustic signal for diagnosing component abnormalities based on the first digital signal; in response to the highest frequency of the first digital signal being outside the predetermined frequency range, convert the first digital signal into a second digital signal and generate a second acoustic signal for diagnosing component abnormalities based on the second digital signal, wherein the highest frequency of the second digital signal is within the predetermined frequency range; and a sound generating device 305 configured to generate sound based on the second acoustic signal. Additionally, the ultra-high frequency electronic stethoscope according to an exemplary embodiment of the present disclosure may further include a magnetic suction base 303.

[0043] In the example as Figure 3 shown, the coupling agent 301 is a filler for filling the minute gaps between the contact surfaces to reduce the acoustic impedance difference between the acoustic emission sensor 302 and the detection surface; the acoustic emission sensor 302 is a sensing device for converting an acoustic emission signal into an electrical signal; the magnetic suction base 303 is a fixing device for fixing the acoustic emission sensor 302 so that it reliably adheres to the detection surface; the controller 304 may be an acquisition and calculation device connected to the acoustic emission sensor 302 through a wire, providing a driving power supply for the acoustic emission sensor 302, while performing ultra-high frequency data acquisition, converting the electrical signal into a digital signal, and performing a composite operation to convert the ultra-high frequency acoustic emission signal into a digital signal audible to humans, and then performing digital-to-analog conversion to convert it into an audible sound signal, which is output through the sound generating device 305; the sound generating device 305 is a signal output device, and the sound generating device 305 can be used to identify whether the audible sound signal contains an abnormal characteristic signal by listening to the sound manually.

[0044] In the example, the ultra-high frequency electronic stethoscope according to the exemplary embodiment of the present disclosure may have the following functions, for example: an ultra-high frequency data acquisition function for converting an ultra-high frequency acoustic emission signal into a digital signal; an ultra-high speed data analysis function for calculating the basic characteristic signal of the acoustic emission signal, so as to facilitate recording the development trend of the fault; a graphic display function for viewing the real-time waveform, spectrum characteristics, time domain, frequency domain, and impact domain characteristic parameters; a high-frequency slow-motion function for converting the ultra-high frequency signal into an audible sound signal; a sound adjustment function for amplifying or reducing the gain of the converted slow-motion signal so that the sound loudness is adjusted to the audible range of the human ear; a historical data recording function for saving historical waveform data and characteristic signals for tracing subsequent fault problems; a typical fault sound slow-motion function for providing a variety of abnormal types of sounds of typical components for audition and on-site evaluation of the unit status based on the sound; a data export function for exporting historical waveforms and characteristic signals for manual analysis; a label function for distinguishing different objects under test, suitable for multiple uses of one object, and expanding the application scope of the ultra-high frequency electronic stethoscope.

[0045] In the example, the operation process of the ultra-high frequency electronic stethoscope according to the exemplary embodiment of the present disclosure is as follows: apply coupling agent 301 to discharge the air between the acoustic emission sensor and the object to be measured to facilitate the transmission of sound waves and reduce attenuation, so as to make the measurement result more accurate and stable; install the acoustic emission sensor 302, and effectively fix the acoustic emission sensor 302 with a magnetic suction base 303; enable the controller 304 to collect the acoustic emission signal, for example, the sampling frequency can be 500kHz, and the collection time is 0.1s; the controller 304 calculates the time domain characteristics (including effective value, maximum value, steepness, kurtosis, crest factor, waveform factor, etc.), frequency domain characteristics (including center frequency, peak frequency, amplitude, etc.), impact domain characteristics (including amplitude, energy, count, duration, mean square error, average signal level, threshold, rise time, peak count, average frequency, reverberation frequency, start frequency, signal strength, absolute energy, etc.); save the original waveform, time domain characteristics, frequency domain characteristics, and impact domain characteristics of the acoustic emission signal; compare historical records to see if the data characteristics are abnormal; enable the playback function of the control host, and slow down the output signal frequency by the sound device 305 at a specific ratio (for example, a ratio of 1:20) to reduce the frequency range audible to the human ear (for example, reduce the acoustic emission frequency bandwidth from 0 to 250 kHz to 0 to 12.5 kHz), so that the slow-play signal can be manually evaluated based on experience to determine whether there is damage or fault in the component to be diagnosed. It should be understood that the acquisition frequency of the acoustic emission signal is not limited to 500 kHz, and the acquisition time is not limited to 0.1 s, but the acquisition parameters can be changed according to the different objects under test.

[0046] By adopting the ultra-high frequency electronic stethoscope according to the exemplary embodiment of the present disclosure, the acoustic emission characteristic parameters can be directly displayed, and a high-frequency slow playback function can be provided to evaluate the abnormal status of the components of the wind turbine generator set through an economical, practical, convenient and fast stethoscope.

[0047] Figure 4 is a block diagram illustrating a computing system including at least one computing device and at least one storage device storing instructions according to an exemplary embodiment of the present disclosure.

[0048] like Figure 4 As shown, a computing system 400 provided according to an exemplary embodiment of the present invention includes a computing device 401 and a storage device 402, wherein the storage device 402 stores computer executable instructions. When the computer executable instructions are executed by the computing device 401, the component abnormality diagnosis method of the wind turbine generator set described in any of the aforementioned embodiments is executed.

[0049] The computing device 401 is deployed in a server or client, and may also be deployed on a node device in a distributed network environment. In addition, the computing device 401 may be a PC computer, a tablet device, a personal digital assistant, a smart phone, a web application or other device capable of executing the above-mentioned instruction set. Here, the computing device is not necessarily a single computing device, but may also be any device or circuit capable of executing the above-mentioned instruction (or instruction set) individually or jointly. The computing device may also be a part of an integrated control system or a system manager, or may be configured as a portable electronic device interconnected with an interface locally or remotely (e.g., via wireless transmission). In the computing device, the processor includes a central processing unit (CPU), a graphics processing unit (GPU), a programmable logic device, a dedicated processor system, a microcontroller or a microprocessor. As an example and not limitation, the processor also includes an analog processor, a digital processor, a microprocessor, a multi-core processor, a processor array, a network processor, etc.

[0050] According to another aspect of the present disclosure, a computer-readable storage medium storing instructions is provided, and when the instructions are executed by at least one computing device, the at least one computing device is prompted to execute the component abnormality diagnosis method of the wind turbine generator set described in any of the aforementioned embodiments. The computer-readable storage medium includes magnetic media such as floppy disks and tapes, optical media (including compact disk (CD) ROM and DVD ROM), magneto-optical media such as floppy disks, hardware devices such as ROM, RAM and flash memory designed to store and execute program commands. The instructions may include language codes executable by a computer using an interpreter and machine language codes generated by a compiler.

[0051] By adopting the present disclosure, ultra-high frequency acoustic emission signals can be converted into sound waves audible to the human ear, so that an economical, practical, lightweight and fast stethoscope can be used to identify abnormal signal characteristics for identifying early faults of blades, main bearings, pitch bearings, etc., which is suitable for on-site inspections of wind turbines and avoids problems such as long debugging time and high application complexity caused by the use of high-precision equipment.

[0052] The processes, methods or algorithms disclosed herein may be transmitted to or implemented by a processing device, a controller or a computer, which may include any existing programmable electronic control unit or a dedicated electronic control unit. Similarly, the processes, methods or algorithms may be stored in a variety of forms as data and instructions that can be executed by a controller or a computer, including but not limited to information being permanently stored on a non-writable storage medium (such as a ROM device) and information being variably stored on a writable storage medium (such as a floppy disk, a tape, a CD, a RAM device, and other magnetic and optical media). The processes, methods or algorithms may also be implemented in a software executable object. Optionally, the processes, methods or algorithms may be implemented in whole or in part using suitable hardware components (such as an ASIC, an FPGA, a state machine, a controller or other hardware components or devices) or a combination of hardware components, software components and firmware components.

[0053] Although the present disclosure includes specific examples, it will be apparent to those of ordinary skill in the art that various changes in form and detail may be made in these examples without departing from the spirit and scope of the claims and their equivalents. The examples described herein will be considered to be descriptive only and not for limiting purposes. The description of the features or aspects in each example will be considered to be applicable to similar features or aspects in other examples. Suitable results may be obtained if the described techniques are performed in a different order, and / or if the components in the described systems, architectures, devices, or circuits are combined in different ways and / or replaced or supplemented with other components or their equivalents in the described systems, architectures, devices, or circuits. Therefore, the scope of the present disclosure is not limited by specific embodiments, but by the claims and their equivalents, and all variations within the scope of the claims and their equivalents will be interpreted as included in the present disclosure.

Claims

1. A method for diagnosing abnormality of components of a wind turbine generator set, It is characterized in that The component abnormality diagnosis method comprises: converting a first acoustic signal acquired from a component to be diagnosed into a first digital signal, and determining a highest frequency of the first digital signal; In response to the highest frequency being within a predetermined frequency range, generating a second acoustic signal for component abnormality diagnosis based on the first digital signal; In response to the highest frequency being outside the predetermined frequency range, the first digital signal is converted into a second digital signal, and a second acoustic signal for component abnormality diagnosis is generated based on the second digital signal, wherein the highest frequency of the second digital signal is within the predetermined frequency range.

2. The method for diagnosing abnormality of components of a wind turbine generator set according to claim 1, It is characterized in that The predetermined frequency range is from 20 Hz to 20000 Hz.

3. The method for diagnosing abnormality of components of a wind turbine generator set according to claim 1, It is characterized in that The component to be diagnosed includes at least one of a blade, a main bearing and a pitch bearing.

4. The method for diagnosing abnormality of components of a wind turbine generator set according to claim 1, It is characterized in that The highest frequency of the first digital signal is a predetermined multiple of the highest frequency of the second digital signal.

5. The method for diagnosing abnormality of components of a wind turbine generator set according to claim 4, It is characterized in that The step of converting the first digital signal into a second digital signal includes performing frequency reduction on a frequency of the first digital signal by the predetermined multiple.

6. The method for diagnosing abnormality of components of a wind turbine generator set according to claim 1, It is characterized in that The component abnormality diagnosis method further includes: amplifying or reducing the gain of the second acoustic signal.

7. A device for diagnosing abnormality of components of a wind turbine generator set, It is characterized in that The component abnormality diagnosis device comprises: a frequency determination unit, configured to convert a first acoustic signal obtained from a component to be diagnosed into a first digital signal, and determine a maximum frequency of the first digital signal; The acoustic signal generating unit is used to perform the following operations: In response to the highest frequency being within a predetermined frequency range, generating a second acoustic signal for component abnormality diagnosis based on the first digital signal; and In response to the highest frequency being outside the predetermined frequency range, the first digital signal is converted into a second digital signal, and a second acoustic signal for component abnormality diagnosis is generated based on the second digital signal, wherein the highest frequency of the second digital signal is within the predetermined frequency range.

8. A device for diagnosing abnormality of components of a wind turbine generator set, It is characterized in that The component abnormality diagnosis device comprises: An acoustic emission sensor, used to obtain a first acoustic signal from a component to be diagnosed; a controller configured to: convert the first acoustic signal into a first digital signal, and determine a maximum frequency of the first digital signal; In response to the highest frequency being within a predetermined frequency range, generating a second acoustic signal for component abnormality diagnosis based on the first digital signal; In response to the highest frequency being outside the predetermined frequency range, converting the first digital signal into a second digital signal, and generating a second acoustic signal for component abnormality diagnosis based on the second digital signal, wherein the highest frequency of the second digital signal is within the predetermined frequency range; and The sound generating device is configured to generate sound based on the second sound signal.

9. A computing system comprising at least one computing device and at least one storage device storing instructions, It is characterized in that When the instructions are executed by the at least one computing device, the at least one computing device is prompted to execute the component abnormality diagnosis method of a wind turbine generator set according to any one of claims 1-6.

10. A computer-readable storage medium storing instructions, It is characterized in that When the instruction is executed by at least one computing device, the at least one computing device is prompted to execute the component abnormality diagnosis method of a wind turbine generator set according to any one of claims 1-6.

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