Bearing Damage Detection via Wavelet Packet Decomposition of the Stator Current

2004/04/01 by L. Eren, Levent Eren, M.J. Devaney · 6 citations
Engineering · #Acoustics #Artificial intelligence #Bearing (navigation) #Computer science #Current (fluid) #Electrical engineering #Electronic engineering #Engineering #Engineering Diagnostics and Reliability #Gear and Bearing Dynamics Analysis #Machine Fault Diagnosis Techniques #Materials science #Network packet #Physics #Stator #Vibration #Wavelet #Wavelet packet decomposition #Wavelet transform

paper · doi:10.1109/tim.2004.823323

openalex publication_date 2004/04/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/22

Abstract

Bearing faults are one of the major causes of motor failures. The bearing defects induce vibration, resulting in the modulation of the stator current. In this paper, the stator current is analyzed via wavelet packet decomposition to detect bearing defects. The proposed method enables the analysis of frequency bands that can accommodate the rotational speed dependence of the bearing defect frequencies. The wavelet packet decomposition also provides a better treatment of nonstationary stator current than currently used Fourier techniques.

Cited by

• Early Bearing Fault Diagnosis of Rotating Machinery by 1D Self-Organized\n Operational Neural Networks
• 1D Convolutional Neural Networks and Applications: A Survey
• An Enhanced Diagnostic Scheme for Bearing Condition Monitoring
• Visibility Graph Feature Model of Vibration Signals: A Novel Bearing Fault Diagnosis Approach. [europepmc]
• Bearing Fault Diagnosis Based on the Switchable Normalization SSGAN with 1-D Representation of Vibration Signals as Input. [europepmc]
• A Novel Detection Scheme for Motor Bearing Structure Defects in a High-Speed Train Using Stator Current. [europepmc]

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