Journal
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
Volume 67, Issue 8, Pages 1887-1899Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIM.2018.2806984
Keywords
Condition monitoring; fault diagnosis; feature extraction; signal denoising; vibration measurement
Funding
- National Natural Science Foundation of China [51675035]
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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.
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