Wind turbine generator set bearing mechanical fault diagnosis method considering multi-class objectives

A technology for wind turbines and bearing machinery, applied in mechanical bearing testing, complex mathematical operations, etc., can solve problems such as errors, misidentification of fault samples without training, and impact on equipment reliability, so as to prevent false modals and modal aliasing Effects with low impact and few modalities

Active Publication Date: 2018-06-19
国电投河南新能源有限公司
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Problems solved by technology

In the absence of fault training data, multi-class classifiers such as support vector machines are easy to misidentify non-training fault samples as wrong fault types or even normal states, seriously affecting equipment reliability

Method used

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  • Wind turbine generator set bearing mechanical fault diagnosis method considering multi-class objectives
  • Wind turbine generator set bearing mechanical fault diagnosis method considering multi-class objectives
  • Wind turbine generator set bearing mechanical fault diagnosis method considering multi-class objectives

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Embodiment Construction

[0086] The invention is a method for diagnosing mechanical faults of wind turbine bearings in consideration of multi-category targets, comprising the following steps:

[0087] 2) Wind turbine bearing vibration signal acquisition

[0088] Acceleration sensors are used to collect wind turbine bearing normal state signals, rolling element fault vibration signals, inner ring fault vibration signals and outer ring fault signals, and the above signals are recorded by a 16-channel data recorder. The signal sampling frequency is 12 kHz, and the signal length is 4096 sampling points;

[0089] 2) Wind turbine bearing vibration signal processing

[0090] In order to extract effective fault feature information and take into account the modulation characteristics of the bearing vibration signal, the empirical wavelet transform is used for bearing fault diagnosis. Then, a set of orthogonal filter banks is constructed based on the segmented intervals, so as to decompose the complex bearing...

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Abstract

The invention provides a wind turbine generator set bearing mechanical fault diagnosis method considering multi-class objectives. The method is characterized by comprising the steps of wind turbine generator set bearing vibration signal acquisition, wind turbine generator set bearing vibration signal processing, wind turbine generator set bearing vibration signal feature extraction, wind turbine generator set bearing vibration signal feature selection and recognition of the circuit breaker state through the hierarchical hybrid classifier. The method is scientific and reasonable, high in applicability and high in practical value and can accurately recognize the fault so that the situation that the present method is liable to misrecognize the new fault degree or the new fault type of samplewhich is not included in the training samples as the normal state can be avoided.

Description

[0001] The invention is a mechanical fault diagnosis method for wind turbine bearings in consideration of multi-category targets, which is applied to online diagnosis of mechanical fault states of wind turbine bearings. Background technique [0002] Bearings are key components of rotating machinery, and their failure affects the reliable operation of many types of electrical equipment, including wind turbines. Non-bearing failures such as gearboxes and blades in the mechanical transmission system of wind turbines are mostly caused by bearing failures, and the same is true for other mechanical equipment failures. Therefore, the research on bearing condition monitoring technology and fault diagnosis method is of great significance. The existing bearing fault diagnosis process mainly includes two steps of feature extraction and fault classification. [0003] Commonly used signal processing methods include empirical mode decomposition, wavelet decomposition, ensemble empirical mo...

Claims

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Application Information

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IPC IPC(8): G01M13/04G06F17/14
Inventor 黄南天王斌王达蔡国伟杨冬锋黄大为方立华杨学航刘博张良孔令国王燕涛
Owner 国电投河南新能源有限公司
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