Support vector machine mechanical fault diagnosis method based on voting mechanism

A technology of support vector machine and mechanical failure, applied in computer components, data processing applications, speech analysis, etc., to achieve the effect of good applicability and high frequency resolution

Active Publication Date: 2018-11-02
STATE GRID CORP OF CHINA +2
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The technology of mechanical fault diagnosis based on GIS equipme...

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  • Support vector machine mechanical fault diagnosis method based on voting mechanism
  • Support vector machine mechanical fault diagnosis method based on voting mechanism
  • Support vector machine mechanical fault diagnosis method based on voting mechanism

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Embodiment Construction

[0050] A support vector machine mechanical fault diagnosis method based on a voting mechanism, specifically comprising the following steps:

[0051] Step 1: Based on the dual-channel audio collector, the main microphone is facing the GIS or other electrical equipment in the substation to collect operating sound signals, and the secondary microphone is used to collect ambient noise;

[0052] Step 2: Use the sound data collected in the auxiliary microphone as the noise reference signal, and filter out the operating sound signal collected by the main microphone and the ambient noise in the noise mixed signal based on the principle of adaptive noise cancellation, to obtain the noise-filtered sound signal ;

[0053] Step 3: Based on the power frequency harmonic structure analysis algorithm, the multi-resolution bandpass filter bank spectrum analysis algorithm and the Hilbert-Huang transform algorithm, respectively extract the sound signal characteristics after filtering out the noi...

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Abstract

The invention discloses a support vector machine mechanical fault diagnosis method based on a voting mechanism. The method comprises three different feature analysis algorithms: power frequency harmonic surface feature analysis algorithm, a multi-resolution band-pass filter tank spectrum analysis method and Hilbert-huang conversion feature analysis method; the feature extraction is performed on asound signal emitted under the mechanical equipment fault operation state, and then classification identification is performed by utilizing the support vector machine algorithm. The adopted three-feature analysis algorithms have good applicability for the mechanical noise and like unstable nonlinear signal, and the time frequency feature of the signal can be self-adaptively described in multiple aspects; the classification algorithm adopts a support vector machine multi-classifier based on the voting mechanism; the classifier decomposes the multi-classifier into multiple second-class classifiers by adopting a one versus rest method, and the classification method can achieve high identification rate and good algorithm robustness based on the voting mechanism and the confidence optimal criterion judgment method.

Description

technical field [0001] The invention belongs to the technical field of mechanical fault diagnosis, in particular to a support vector machine mechanical fault diagnosis method. Background technique [0002] Gas Insulated Switchgear (GIS) has been more and more widely used due to its high operational reliability. GIS has good insulation properties and is widely used in high-voltage transmission systems. With the rapid development of my country's electric power industry, GIS tends to develop with large capacity and high voltage. As an important equipment in the power system, once GIS fails, it will affect the normal power supply of the power system, resulting in huge economic losses and adverse social impact. Therefore, the operational reliability of GIS is very important. [0003] The failure rate of GIS is generally only 20%-40% of conventional equipment, but there are also defects that are not easy to find, there are few testing methods, and it is difficult to judge. Ope...

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Application Information

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IPC IPC(8): G06K9/62G06Q10/06G06Q50/06G10L15/02G10L21/0216G10L25/18
CPCG06Q10/0635G06Q50/06G10L15/02G10L21/0216G10L25/18G06F18/2411G06F18/254
Inventor 牛博齐卫东王森吴经锋李毅詹海峰任双赞丁彬张晓兰
Owner STATE GRID CORP OF CHINA
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