EPEOgram: An Optimal Frequency Band Identification Method for Rolling Bearing Compound Fault Diagnosis

Spectral coherence (SCoh) is an effective tool for revealing the second-order cyclostationary of bearing faults. Identifying the optimal demodulation bands (ODBs) is key to accurately diagnosing bearing failures. However, most methods focus on the demodulation of a single fault information band, neglecting the discrete resonant frequency bands or multiple informative frequency bands, which may lead to the omission of critical fault information. In addition, the reliance on prior fault characteristic frequencies limits the practical application of complex fault diagnosis. To solve such issues, an ODBs identification method namely equal-proportional expansion optimization-gram (EPEOgram) is proposed for rolling bearing compound faults. First, the original SCoh is reconstructed to automatically identify the potential cyclic frequencies (PCFs) in each spectral frequency slice (SFS) based on the proposed autocorrelation cycle ratio (ACR) calibration algorithm. Next, the PCFs are used as fault-type labels to construct a weight metric to evaluate the abundance of the fault information contained in each SFS, and then to locate the initial center frequency band for optimization. Finally, an equal-proportional expansion frequency band optimization structure is developed to generate the EPEOgram for identifying the ODBs in each initial center band, and the ODBs with similar PCFs are clustered to construct ODBs cluster weighted envelope spectrum (OCWES) for bearing fault detection. This method does not require prior fault information and can accurately and quickly identify the ODBs for the discrete information frequency bands. The simulation and experimental results indicate that the proposed method can effectively identify multiple fault information frequency bands with rich fault information and less noise interference. In addition, comparison with state-of-the-art methods highlights the superiority of the proposed methodology.

Authors

Institutions

Publication Details

Journal
IEEE Transactions on Instrumentation and Measurement
Published
2025-01-01
DOI
https://doi.org/10.1109/tim.2025.3551858
Citations
4
Primary Topic
Machine Fault Diagnosis Techniques
Type
article
Field-Weighted Citation Impact
4.86

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

EPEOgram: An Optimal Frequency Band Identification Method for Rolling Bearing Compound Fault Diagnosis

Shiqian Chen, Zaigang Chen, Junsheng Xin
4 citations
IEEE Transactions on Instrumentation and Measurement
Machine Fault Diagnosis Techniques
4.86
article

EPEOgram: An Optimal Frequency Band Identification Method for Rolling Bearing Compound Fault Diagnosis

Shiqian Chen, Zaigang Chen, Junsheng Xin
article en
4 citations

Abstract

Spectral coherence (SCoh) is an effective tool for revealing the second-order cyclostationary of bearing faults. Identifying the optimal demodulation bands (ODBs) is key to accurately diagnosing bearing failures. However, most methods focus on the demodulation of a single fault information band, neglecting the discrete resonant frequency bands or multiple informative frequency bands, which may lead to the omission of critical fault information. In addition, the reliance on prior fault characteristic frequencies limits the practical application of complex fault diagnosis. To solve such issues, an ODBs identification method namely equal-proportional expansion optimization-gram (EPEOgram) is proposed for rolling bearing compound faults. First, the original SCoh is reconstructed to automatically identify the potential cyclic frequencies (PCFs) in each spectral frequency slice (SFS) based on the proposed autocorrelation cycle ratio (ACR) calibration algorithm. Next, the PCFs are used as fault-type labels to construct a weight metric to evaluate the abundance of the fault information contained in each SFS, and then to locate the initial center frequency band for optimization. Finally, an equal-proportional expansion frequency band optimization structure is developed to generate the EPEOgram for identifying the ODBs in each initial center band, and the ODBs with similar PCFs are clustered to construct ODBs cluster weighted envelope spectrum (OCWES) for bearing fault detection. This method does not require prior fault information and can accurately and quickly identify the ODBs for the discrete information frequency bands. The simulation and experimental results indicate that the proposed method can effectively identify multiple fault information frequency bands with rich fault information and less noise interference. In addition, comparison with state-of-the-art methods highlights the superiority of the proposed methodology.

IEEE Transactions on Instrumentation and MeasurementVol. 74
Southwest Jiaotong University (CN)
National Natural Science Foundation of China, National Key Research and Development Program of China
Climate action
Openalex Percentile: Top 6%
Machine Fault Diagnosis Techniques
4.86
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.