SAFO: Serial Amplitude Modulation–Frequency Modulation Operator for Multichannel Multicomponent Signal Decomposition and Its Application to Rolling Bearing Fault Diagnosis
The amplitude modulation-frequency modulation operator (AFO) is an extension of the operator-based signal decomposition method, which uses the amplitude modulation-frequency modulation (AM-FM) signals as the basis functions. In this article, the single-channel AFO is extended to the multichannel case with the serial AFO (SAFO) proposed for multichannel signal decomposition. The proposed SAFO approach uses a concatenation technique to concatenate the multichannel signals into a single-channel signal and then the 1-D signal decomposition method AFO is used to deal with the obtained signal for obtaining several components. Ultimately, the final components are obtained by de-concatenating the 1-D components into multichannel status. SAFO preserves the original essence of AFO and its advantages. Then, a new fault diagnosis method for the rolling bearing is established based on the proposed SAFO for multichannel signal decomposition, the effective weighted sparse kurtosis (EWSK) indicator for the selection of modes containing rich feature information and the square envelope spectrum (SES) for fault information demodulation. Subsequently, the comparison of simulations and measured data is made and the analysis results demonstrate that the SAFO method is superior to the compared methods in the extraction of prominent and feature-rich fault features and fault diagnosis.
Authors
- Qing Ni (ORCID: https://orcid.org/0000-0001-7537-3934)
- Haiyang Pan (ORCID: https://orcid.org/0000-0001-9868-8154)
- Jinyu Tong (ORCID: https://orcid.org/0000-0001-9734-2928)
- Ke Feng (ORCID: https://orcid.org/0000-0003-2338-5161)
- Jinde Zheng (ORCID: https://orcid.org/0000-0001-5735-4916)
- Guoliang Peng (ORCID: https://orcid.org/0000-0001-5496-9122)
Institutions
- University of Technology Sydney (AU)
- University of British Columbia (CA)
- Anhui University of Technology (CN)
Publication Details
- Journal
- IEEE Sensors Journal
- Published
- 2024-12-20
- DOI
- https://doi.org/10.1109/jsen.2024.3517693
- Citations
- 1
- Primary Topic
- Machine Fault Diagnosis Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.33
Funders
- National Natural Science Foundation of China
- State Key Laboratory of Mechanical Transmissions