Bearing Faults Detection And Identification Using Relational Data Clustering With Composite Differential Evolution Optimization

Bearing faults in machinery are among the most critical faults that require attention by maintenance personnel at the early stages of fault initiation. In many cases, it is difficult to directly and accurately identify the fault type and its extent under varying operating conditions. This work demonstrates a novel procedure for bearing fault detection and identification in an experimental set-up. Three seeded faults, in the rotating machinery supported by the test ball bearing, include inner race fault, outer race fault and one roller fault. The rotor is run at different speeds and with a small level of rotating mass unbalance. Accelerometer-based vibration signals are analyzed for the different bearing faults’ signatures using statistical features, frequency spectra and wavelet coefficients. The composite differential evolution technique is proposed for parameter estimation when the system response is known a-priori. The algorithm is compared to five other differential evolution algorithms using conventional crossover and mutation operators. The objective is to correlate bearing faults to the extracted vibration features. The results of this analysis will be extended for applications in real-time bearing condition monitoring systems.

 

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