Noise diagnosis method for electrical equipment break-down arc

Abstract

The invention discloses a noise diagnosis method for an electrical equipment break-down arc. The method comprises the following steps that A, an arc sound signal of the electrical equipment is collected, and pre-emphasis processing, FIR digital filtering and framing are carried out on the arc sound signal; B, short-time energy and short-time zero-crossing rate of each frame of the arc sound signalare calculated, short-time average energy and short-time average zero-crossing rate are determined, and an arc sound signal abnormal interval is detected by adopting a dual threshold determinate method; C, filtering is carried out on the arc sound signal abnormal interval by adopting M FIR filters, an energy value of each frequency domain sub-band is calculated to form an M-dimensional feature vector; D, a primary linear kernel model is established by using sample set data and an optimal parameter of a kernel function, an optimal linear kernel model is obtained by modifying the parameter repeatedly, the M-dimensional feature vector is diagnosed by using the optimal linear kernel model to determine whether or not a running state of the electrical equipment is normal. The noise diagnosis method for the electrical equipment break-down arc has the advantages that on-line detection and real-time alarming of the running state of the electrical equipment are achieved, and the damage and probability of the break-down arc are reduced.

Description

technical field [0001] The invention relates to the technical field of fault diagnosis of electrical equipment, in particular to a noise diagnosis method for fault arcs of electrical equipment. Background technique [0002] The application of acoustic analysis to the physical characteristics, generation mechanism and detection methods of arc faults at home and abroad began in the mid-1980s. After rapid development in the 1990s, it has been successfully applied in many fields. In the diagnosis of the operating status of electrical equipment, since the sound wave signal in the frequency band audible to the human ear from 20Hz to 20kHz is greatly disturbed by the outside world, it is not only time-consuming for the staff on the operating site to use the traditional method to initially judge whether the electrical equipment is faulty by the senses. It is laborious, and the accuracy mainly depends on the professionalism of the inspectors. Therefore, it is of great significance t...

AI Technical Summary

Problems solved by technology

In the diagnosis of the operating status of electrical equipment, since the sound wave signal in the frequency band audible to the human ear from 20Hz to 20kHz is greatly disturbed by the outside world, it is not only time-consuming for the staff on the operating site to use the traditional method to initially judge whether the electrical equipment is faulty by the senses. Laborious, and the accuracy mainly depends on the professionalism of the inspectors

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