Fault detection and diagnosis of bearing based on local wave time-frequency feature analysis

Incipient fault information detection of mechanical equipment is a kind of technical support for efficient operation of current automation equipment. Due to the abruptness and transience of mechanical fault, the traditional signal processing methods based on Fourier transform cannot meet the demands...

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Main Authors: Xiao, Qijun, Luo, Zhonghui, Wu, Junlan
Format: Conference Proceeding
Language:English
Subjects:
Automation
Demand
Fault detection
Faults
Feature extraction
local wave
Noise reduction
Roller bearings
Rolling bearings
Signal processing
Time-frequency
Time-frequency analysis
Wavelet analysis
Wavelet transforms
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creator Xiao, Qijun
Luo, Zhonghui
Wu, Junlan
description Incipient fault information detection of mechanical equipment is a kind of technical support for efficient operation of current automation equipment. Due to the abruptness and transience of mechanical fault, the traditional signal processing methods based on Fourier transform cannot meet the demands of such kind of transient signals. In this paper, local wave time-frequency analysis techniques are explored, mainly including Signal Denoising, Signal Singularity Detection, Empirical Mode Decomposition (EMD), and the methods for extracting the features of transient signals are also explored, of which the effectiveness is verified by taking the rolling bearing fault as an example.
doi_str_mv 10.1109/ICNC.2015.7378095
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subjects Automation
Demand
Fault detection
Faults
Feature extraction
local wave
Noise reduction
Roller bearings
Rolling bearings
Signal processing
Time-frequency
Time-frequency analysis
Wavelet analysis
Wavelet transforms
title Fault detection and diagnosis of bearing based on local wave time-frequency feature analysis
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