Blade fault diagnosis method, apparatus and system, and storage medium

A blade fault diagnosis apparatus, comprising:

• an acquisition module configured to acquire a blade rotation audio collected by an audio collection device during an operation of a wind turbine generator system;
• a preprocessing module configured to preprocess the blade rotation audio based on a wind noise filtering algorithm to obtain a blade rotation audio filtered out of wind noise;
• a dividing module configured to divide the blade rotation audio filtered out of wind noise to obtain audio segments corresponding to blades of the wind turbine generator system respectively;
• a diagnosis module configured to diagnose, based on the audio segments, whether the blades each corresponding to one of the audio segments are faulty,
• an alarm module configured to transmitting blade fault diagnosis result to a server in a central control room, and causing the server to display the blade fault diagnosis result through an interface program, and to transmit an alarm prompt when there is faulty blade,
• wherein the diagnosis module is further configured to:
• process each of the audio segments by a Fourier transform to obtain a second feature value of each of the audio segments, wherein the second feature value is configured to characterize a frequency domain feature of each of the audio segments:
• input the second feature value of each of the audio segments to a blade fault diagnosis model to obtain a first fault diagnosis result of each of the blades, wherein the blade fault diagnosis model is a pre-trained model configured to identify whether a blade corresponding to the audio segment is faulty according to the second feature value of the audio segment:
• count durations of the audio segments corresponding to the blades respectively;
• obtain a second fault diagnosis result by judging whether there is a faulty blade according to whether a difference between durations of every two audio segments exceeds a reset-threshold value;
• and
• judge, by combining the first fault diagnosis result of each of the blades and the second fault diagnosis result, whether there is a faulty blade.

A blade fault diagnosis system, comprising:

• an audio collection device comprising a plurality of audio sensors, wherein at least one of the audio sensors is disposed at a prevailing wind direction position or a lee wind direction position of a tower barrel of a wind turbine generator system;
• a processor disposed inside of the tower barrel and connected to the audio sensors, wherein the processor is configured to:
• receive a blade rotation audio collected by the audio sensors;
• preprocess the blade rotation audio based on a wind noise filtering algorithm to obtain a blade rotation audio filtered out of wind noise;
• divide the blade rotation audio filtered out of wind noise to obtain audio segments corresponding to blades of the wind turbine generator system respectively;
• diagnose, based on the audio segments, whether the blades each corresponding to one of the audio segments are faulty;
• transmit blade fault diagnosis result to a server in a central control room;
• and cause the server to display the blade fault diagnosis result through an interface program, and to transmit an alarm prompt when there is faulty blade,
• wherein the processor is further configured to:
• process each of the audio segments by a Fourier transform to obtain a second feature value of each of the audio segments, wherein the second feature value is configured to characterize a frequency domain feature of each of the audio segments:
• input the second feature value of each of the audio segments to a blade fault diagnosis model to obtain a first fault diagnosis result of each of the blades wherein the blade fault diagnosis model is a pre-trained model configure to identify whether a blade corresponding to the audio segment is faulty according to the second feature value of the audio segment;
• count durations of the audio segments corresponding to the blades respectively;
• obtain a second fault diagnosis result by judging whether there is a faulty blade according to whether a difference between durations of every two audio segments exceeds a preset threshold value;
• and
• judge, by combining the first fault diagnosis result of each of the blades and the second fault diagnosis result, whether there is a faulty blade.

A storage medium having computer program instructions stored thereon, wherein the computer program instructions, when executed by a processor, implement a blade fault diagnosis method, comprising:

• acquiring a blade rotation audio collected by an audio collection device during operation of a wind turbine generator system:
• preprocessing the blade rotation audio based on a wind noise filtering algorithm to obtain a blade rotation audio filtered out of wind noise:
• dividing the blade rotation audio filtered out of wind noise to obtain audio segments corresponding to blades of the wind turbine generator system respectively;
• diagnosing, based on the audio segments, whether the blades each corresponding to one of the audio segments are faulty,
• transmitting blade fault diagnosis result to a server in a central control room;
• and
• causing the server to display the blade fault diagnosis result through an interface program, and to transmit an alarm prompt when there is faulty blade,
• wherein diagnosing, based on the audio segments, whether the blades each corresponding to one of the audio segments are faulty comprises:
• processing each of the audio segments by a Fourier transform to obtain a second feature value of each of the audio segments, wherein the second feature value is configured to characterize a frequency domain feature of each of the audio segments;
• inputting the second feature value of each of the audio segments to a blade fault diagnosis model to obtain a first fault diagnosis result of each of the blades wherein the blade fault diagnosis model is a pre-trained model configured to identify whether a blade corresponding to the audio segment is faulty according to the second feature value of the audio segment;
• counting durations of the audio segments corresponding to the blades respectively;
• obtaining a second fault diagnosis result by judging whether there is a faulty blade according to whether a difference between duration of every two audio segments exceeds a preset threshold value;
• and
• judging, by combining the first fault diagnosis result of each of the blades and the second fault diagnosis result, whether there is a faulty blade.