Fault detection of rolling element bearings using optimal segmentation of vibrating signals
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Change detection and diagnosis are important research directions and activities in the field of system engineering and conditional maintenance of equipments and industrial processes. The paper promotes a new method for change detection and optimal segmentation of vibrating data obtained during operation of rolling element bearings (REB). After a description of the bearing faults and dynamic simulation of REB, the paper makes a review of the change detection and segmentation approaches, that could be used in REB fault detection and diagnosis. A new approach for change detection and optimal segmentation of vibrating signals, aiming to determine the change points in signals generated by the faults, produced during REB operating, is presented; the efficiency of the segmentation method is proven using Monte Carlo simulations for different signal models, including models with changes in the mean, in FIR, and AR model parameters, frequently used in processing vibrating signals. In the final part, the paper analyses some experimental results obtained using this approach and data from the Case Western Reserve University Bearing Data Center.
Fault detection and diagnosis, Rolling element bearings, Optimal segmentation, Monte Carlo simulation, Vibrating signals, Case study