期刊
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
卷 116, 期 -, 页码 370-391出版社
ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ymssp.2018.06.033
关键词
Fault detection and diagnosis; Rolling element bearings; Optimal segmentation; Monte Carlo simulation; Vibrating signals; Case study
资金
- Ministry of Research and Innovation and Executive Agency for Higher Education, Research, Development and Innovation Funding (UEFISCDI) under the 2018 Core Program [9N/16-03-2018, PN 0301, PN-II-PT-PCCA-2013-4-0044, 224-2014]
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. (C) 2018 Elsevier Ltd. All rights reserved.
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