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Wind turbine generator blade audio fault detection method based on classification

A fault detection and wind turbine technology, applied in wind turbines, engines, mechanical equipment, etc., can solve the problems of affecting detection accuracy, long deployment time, and high detection costs, achieving rapid deployment, efficient monitoring, and ensuring reliable operation. Effect

Pending Publication Date: 2022-04-15
XIAN XIANGXUN TECH
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  • Summary
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] In order to solve the technical problems of the existing wind power blade monitoring technology, such as high detection cost, difficult installation, complex structure, long deployment time, and easy to be affected by the environment and affect the detection accuracy, the present invention provides a classification-based wind turbine blade audio Fault detection method

Method used

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  • Wind turbine generator blade audio fault detection method based on classification
  • Wind turbine generator blade audio fault detection method based on classification
  • Wind turbine generator blade audio fault detection method based on classification

Examples

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Embodiment 1

[0095] Taking audio detection of blades of a wind turbine as an example, this embodiment provides a method for detecting audio faults of wind turbine blades based on binary classification, including the following steps:

[0096] Step 1: Data acquisition and organization

[0097] Step 1.1: Acquisition of open source audio data set, download the standard speech classification task data set, which contains 10 kinds of speech, namely air conditioner, car horn, children playing, dog barking, drilling, engine idling, gun shooting, handheld Rock drills, police sirens, street music, each recording is about 4s, used for the training of the pre-training model, extract the two types of data with the largest number to divide normal data and abnormal data, or divide the 10 types of data into two types of data, respectively Labeled as normal data and abnormal data.

[0098]Step 1.2: Collection and sorting of blade audio data sets in the wind field. The sound pickup equipment installed at t...

Embodiment 2

[0143] Taking the blade audio detection of a certain wind power generator as an example, this embodiment provides a multi-classification-based audio fault detection method for wind turbine blades, including the following steps:

[0144] Step 1: Data acquisition and organization

[0145] Step 1.1: Acquisition of open source audio data set, download the standard speech classification task data set, which contains 10 kinds of speech, namely air conditioner, car horn, children playing, dog barking, drilling, engine idling, gun shooting, handheld Rock drills, police sirens, street music, each recording is about 4s, used for the training of the pre-training model, extract the largest number of M-type data and divide it into audio category data with identification, or divide 10 types of data into M-type data , respectively marked and differentiated. Among them, M is an integer greater than 2, and detects and warns of significant abnormalities such as blade breakage, cracking, and li...

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Abstract

The invention provides a classification-based wind turbine generator blade audio fault detection method, and solves the problems of high detection cost, difficulty in installation, complex structure, long deployment time and easiness in environmental influence in the existing wind turbine blade monitoring technology. The method comprises the following steps: 1) acquiring an open source audio data set and a leaf audio data set, wherein the leaf audio data set comprises a training set and a test set; 2) extracting a Mel frequency spectrum, a Mel frequency cepstral coefficient and a chrominance characteristic of each piece of audio data in the training set; 3) constructing an audio fault detection classification network model, sending the open source audio data set into the network model for pre-training, then sending the Mel frequency spectrum, Mel frequency cepstral coefficient and chromaticity characteristics of the audio data in the training set into the model for classification detection training, and testing the audio fault detection classification network model by using the test set; and 4) inputting a to-be-detected blade audio into the audio fault detection classification network model to obtain a classification detection result of the to-be-detected blade audio.

Description

technical field [0001] The invention belongs to the field of wind power generation, and in particular relates to a classification-based audio fault detection method for wind turbine blades. Background technique [0002] With the demand for new energy development, wind energy has attracted much attention, and wind power generation has become one of the most effective ways to transform and use wind energy. The continuous development of large-scale equipment manufacturing industry and wind power technology has made wind turbines continue to develop in the direction of large-scale and ocean-oriented, and the number of installations has increased year by year. Blades are an indispensable core component of wind turbine equipment. Monitoring them can greatly reduce the incidence of failures and accidents, ensure the stable operation and safety of wind turbines, and avoid unnecessary economic losses. Therefore, how to monitor the operating status and fault category of blades is an ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): F03D17/00G06K9/62
CPCF03D17/00G06F18/24G06F18/214Y02P70/50
Inventor 吴娇雷红涛李刚张苑任毅
Owner XIAN XIANGXUN TECH
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