Vibration-Based Intelligent Fault Diagnosis for Roller Bearings in Low-Speed Rotating Machinery

This paper proposes a new signal feature extraction and fault diagnosis method for fault diagnosis of low-speed machinery. Statistic filter (SF) and wavelet package transform (WPT) are combined with moving-peak-hold method (M-PH) to extract features of a fault signal, and special bearing diagnostic...

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Bibliographic Details
Published in:IEEE transactions on instrumentation and measurement 2018-08, Vol.67 (8), p.1887-1899
Main Authors: Song, Liuyang, Wang, Huaqing, Chen, Peng
Format: Article
Language:English
Subjects:
Condition monitoring
Decision trees
Diagnostic systems
Fault detection
Fault diagnosis
Feature extraction
Frequency-domain analysis
Low speed
Machinery
Parameter identification
Parameter sensitivity
Roller bearings
Rolling bearings
Rotating machinery
signal denoising
Signal processing
Training
vibration measurement
Vibrations
Wavelet analysis
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Summary:This paper proposes a new signal feature extraction and fault diagnosis method for fault diagnosis of low-speed machinery. Statistic filter (SF) and wavelet package transform (WPT) are combined with moving-peak-hold method (M-PH) to extract features of a fault signal, and special bearing diagnostic symptom parameters (SSPs) in a frequency domain that are sensitive to bearing fault diagnosis are defined to recognize fault types. The SF is first used to adaptively cancel noises, and then fault detection is performed by exploiting the optimum symptom parameters in a time domain to identify a normal or fault state. For precise diagnosis, the SSPs are calculated after the signals are processed by M-PH and WPT. A decision tree is used to structure intelligent diagnosis rules in each step until the states are fully and automatically detected. The efficacy of this method was confirmed by applying it to an experimental low-speed rotation machine.
ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2018.2806984