Improved cyclostationary analysis method based on TKEO and its application on the faults diagnosis of induction motors

Zuolu Wang, Jie Yang, Haiyang Li, Dong Zhen, Fengshou Gu, Andrew Ball

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Cyclostationary analysis has been strongly recognized as an effective demodulation tool in identifying fault features of rotating machinery based on vibration signature analysis. This study improves two current mature cyclostationary approaches, cyclic modulation spectrum (CMS) and fast spectral correlation (Fast-SC), combined with the novel frequency-domain application of Teager Kaiser energy operator (TKEO). They can enhance fault feature identification with the lower computational burden. Firstly, the raw vibration signal is transformed into the time-frequency domain through the short-time Fourier transform (STFT) to realize the conversion of the multi-carrier signal to a multiple signal-carrier signal. Secondly, the TKEO is utilized to enhance the fault feature by taking full advantage of demodulating the mono-component. Finally, the spectral coherence and enhanced envelope spectrum (EES) are calculated to effectively exhibit fault features. The superiority of the proposed methods is successfully validated by the simulation study and diagnosing the broken rotor bar (BRB) and bearing outer race faults of induction motors (IMs) under various operating conditions.
Original languageEnglish
JournalISA Transactions
Early online date27 Oct 2021
DOIs
Publication statusE-pub ahead of print - 27 Oct 2021

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