Intelligent Diagnosis method for Multi-flaws of Roller Bearing by Time-Frequency Waveform Distribution and Extreme Learning Machine
Rotating machine, Fault diagnosis, Bearing multi-flaws, Time-frequency waveform distribution
This paper proposed a method for intelligent condition diagnosis of rotating machinery using the time-frequency waveform distribution (TFWD) and extreme learning machine (ELM). The precisely diagnosing method of single flaw and multi-flaws in a roller bearing are proposed as follows: First, the measured data is processed by time-frequency analysis. Second, in order to extraction the feature of signals, waveform distributions in the time domain and frequency domain are calculated separately. Third, extreme learning machine is introduced to distinguish the signal state by the time-frequency waveform distributions in frequency domain. Moreover, if the state of single is abnormal, the time-frequency waveform distributions in time domain are used to identify the flaw is signal flaw or multi-flaws. The efficacy of these novel methods are confirmed by the results of the condition diagnosis for roller bearings with single flaw and multi-flaws at the speed of 600 rpm, 900 rpm and 1200 rpm.
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