Voice detection method of power equipment failure based on combined similar diagonalizable blind source separation algorithm

A technology of power equipment and blind source separation, which is used in measuring devices, measuring ultrasonic/acoustic/infrasonic waves, instruments, etc., and can solve the problems of Gaussian background noise and interference that cannot be well processed for source signals.

Inactive Publication Date: 2015-05-13
SHANDONG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, this patent has the following defects: Gaussian noise interference often exists in the complex environment of power transmission and substations, and this patent cannot handle the Gaussian background noise that may remain in the source signal well

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  • Voice detection method of power equipment failure based on combined similar diagonalizable blind source separation algorithm
  • Voice detection method of power equipment failure based on combined similar diagonalizable blind source separation algorithm
  • Voice detection method of power equipment failure based on combined similar diagonalizable blind source separation algorithm

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Experimental program
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Effect test

Embodiment 1

[0063] A fault sound detection method for power equipment based on the joint approximate diagonal blind source separation algorithm, the specific steps include:

[0064] (1) Using a microphone array, that is, a MIC array to collect sound signals from the operation of electrical equipment;

[0065] (2) Adopting a blind source separation algorithm based on joint approximate diagonalization for step (1) adopting the sound signal collected by the microphone array to separate each independent sound source signal;

[0066] (3) Extract the Mel frequency cepstral coefficient MFCC of the independent sound source signal as the sound characteristic parameter, identify the sound signal through the pattern matching algorithm, match the sound template to be tested with all the reference sample templates, and match the reference sample template with the smallest distance It is the result of electrical equipment working sound recognition: if the reference sample template with the smallest mat...

Embodiment 2

[0068] According to the power equipment failure sound detection method described in embodiment 1, the difference is that in step (1), a microphone array, i.e. a MIC array, is used to collect the sound signal of the operation of the electrical equipment, specifically referring to:

[0069] Adopt microphone array, namely MIC array collects the sound signal of electrical equipment operation and writes down as: x(t)=[x 1 (t),x 2 (t),.......,x n (t)], n is a positive integer, where,

[0070] x 1 (t)=a 11 the s 1

[0071] x 2 (t)=a 21 the s 1 +a 22 the s 2

[0072] .

[0073] .(ⅰ)

[0074] .

[0075] x n (t)=a n1 the s 1 +a n2 the s 2 +…+a nm the s m

[0076] In formula (ⅰ), s 1 ,s 2 ,...,s m is an acoustic signal from an independent source, a ij (i=1,2,...,n; j=1,2,...,m) are real coefficients, n=m.

Embodiment 3

[0078] According to the power equipment failure sound detection method described in Embodiment 1, the difference is that in step (2), the blind source separation algorithm based on joint approximate diagonalization is used to separate each independent sound from the sound signal collected by the microphone array in step (1). source signal, the specific steps include:

[0079] a. Centrally process the sound signals collected by the operation of electrical equipment using the microphone array, that is, the MIC array, and obtain the observation vector after de-averaging Calculated by formula (ii):

[0080] x ‾ ( t ) = x ( t ) - 1 n Σ i = 1 n x i (...

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Abstract

The invention discloses a voice detection method of power equipment failure based on a combined similar diagonalizable blind source separation algorithm. The method comprises the specific steps as follows: (1) adopting a microphone array; (2) separating all independent sound source signals from sound signals collected by the microphone array by adopting the combined similar diagonalizable blind source separation algorithm; (3) extracting Mel-MFC (Frequency Cepstral Coefficients) of the independent sound source signals as sound characteristic parameters, and identifying the sound signals through a model matching algorithm, wherein a reference sample template with a minimal matching distance is a result of identifying the operating sound of the power equipment after a sound template to be tested and all reference sample templates are matched. According to the voice detection method provided by the invention, the characteristics of a non-Gaussian source signal can be enhanced, a source signal which is more clear than a Fast ICA (Independent Component Analysis) can be estimated, the similarity coefficients of the separated signal and the source signals are 0.9 or above, the voice frequency audiometry on the separated signals can be carried out, and the signals separated by a JADE algorithm is clear and distinguishable.

Description

technical field [0001] The invention relates to a fault sound detection method of electric equipment based on a joint approximate diagonalization blind source separation algorithm, and belongs to the technical field of electric equipment maintenance. Background technique [0002] The failure of electrical equipment will not only cause damage to the equipment itself, but also cause serious damage to the safe, stable and economical operation of the entire power system. Therefore, it is of great significance to detect whether electrical equipment fails in time. Electrical equipment fault detection has gone through three stages: blackout experiment stage, live test stage and online detection stage. The traditional periodic detection method has the shortcomings of long test period, low test voltage, heavy workload and poor effectiveness, and it is difficult to meet the reliability requirements of the power system, especially with the rapid development of the power industry toward...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01H17/00
Inventor 田岚张康荣王博睿王海果
Owner SHANDONG UNIV
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