An enhanced cyclostationary method and its application on the incipient fault diagnosis of induction motors

Zuolu Wang, Haiyang Li, Guojin Feng, Dong Zhen, Fengshou Gu, Andrew Ball

Research output: Contribution to journalArticlepeer-review


The cyclostationary analysis techniques have been extensively explored for the purpose of fault detection in rotating machinery. However, there are still huge challenges because of both limited detection frequency range and low fault identification accuracy. This paper proposes an improved cyclostationary method to enhance incipient fault features. Firstly, the continuous wavelet transform is used to accurately locate important frequency bands, and the fault modulation mechanism or fast kurtogram can be adopted to design the optimal wavelet transform scale factor. Secondly, the Teager-Kaiser energy operator is improved to be used in the frequency domain for the weak fault feature enhancement. Finally, fault features are presented in the cyclic frequency domain through spectral coherence and enhanced envelope spectrum. The proposed method is verified through both numerical simulation and experiments, including incipient half-broken rotor bar, and rolling bearing outer race faults in induction motors.

Original languageEnglish
Article number113475
Number of pages13
JournalMeasurement: Journal of the International Measurement Confederation
Early online date10 Sep 2023
Publication statusPublished - 15 Nov 2023

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