Circuit breaker fault diagnosis method for sound and vibration signal fusion processing

Abstract

The invention discloses a circuit breaker fault diagnosis method for identifying a circuit breaker fault diagnosis based on sound and vibration signal fusion processing. There are four states: stuck fault, base loose fault, and refusal to move fault. (2) Step 2, use the db3 wavelet base to decompose the acoustic and vibration signals into three layers, and reconstruct the signals on the eight nodes in the third layer; perform the Hilbert transform on the reconstructed signals of the eight nodes , to obtain the modulus envelope. (3) The envelope signal is divided into 3 sections according to the principle of before opening, during opening and after opening. According to the principle of equal energy, it is divided into 3 sections before opening, 9 sections during opening, and after opening It is divided into 3 sections, and the energy entropy of the wavelet packet square difference of each state of the circuit breaker is extracted. (4) Input the feature vector matrix of each signal into the support vector machine and use the "one vs one" voting strategy to obtain the basic probability distribution. (5) Using improved evidence theory to fuse multi-sensor information to identify circuit breaker fault types.

Description

[0001] The invention relates to a fault diagnosis method of a circuit breaker based on sound-vibration signal fusion processing, which is used for fault diagnosis and operation state monitoring of a high-voltage circuit breaker in a power system. technical background [0002] Circuit breakers are essential protection and control equipment for power systems, and fault diagnosis using external information reflected in their operation has always been one of the hot research issues. At present, circuit breaker fault diagnosis is mostly based on the analysis of vibration signal characteristics, using empirical mode decomposition to extract the energy entropy of wavelet packet square difference of vibration signal, which is used as the input vector of RBF neural network for fault diagnosis. The circuit breaker is accompanied by severe vibration and sound during the opening and closing process. There are also literatures that propose a combined vibration and sound diagnostic method. A...

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Problems solved by technology

The circuit breaker is accompanied by severe vibration and sound during the opening and closing process. There are also literatures that propose a combined vibration and sound diagnostic method. After the sound signal is separated from the blind source and the vibration is improved, the empirical mode decomposition is carried out, and the two-dimensional spectral entropy input support vector is extracted. machine for fault diagnosis, but did not fuse the two signals

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