Scaling-basis chirplet extracting transform and its application in bearing fault diagnosis
Junzhu Zhang, Yating Hou, Liming Wang
TL;DR
This paper introduces a new time-frequency analysis method for accurately diagnosing bearing faults in machinery.
Contribution
The novel 'extraction operator' in the scaling-basis chirplet extracting transform (SBCET) improves time-frequency energy representation for non-stationary signals.
Findings
SBCET provides high-resolution time-frequency representations for signals with close frequency intervals.
The method performs well in intense background noise environments.
Numerical and experimental results confirm the effectiveness of SBCET.
Abstract
In this paper, we propose a new time-frequency analysis (TFA) method, namely scaling-basis chirplet extracting transform (SBCET). Based on the time-frequency representation (TFR) results obtained by scaling-basis chirplet transform (SBCT), the method introduces a new “extraction operator” to extract the time-frequency (TF) energy associated with the signal to portray the TF energy distribution information of the signal with high accuracy. SBCET can also obtain a TFR with concentrated energy and high resolution for non-stationary signals with close frequency intervals and intense background noise. The effectiveness and superiority are proved by numerical signal processing and experimental verification.
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Taxonomy
TopicsMachine Fault Diagnosis Techniques · Fault Detection and Control Systems · Ultrasonics and Acoustic Wave Propagation