Journal
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
Volume 62, Issue 1, Pages 647-656Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIE.2014.2327917
Keywords
Data driven; feature extraction; monitoring; prognostics
Categories
Funding
- Laboratory of Excellence Dedicated to Smart Systems Integrated into Physical Structures (Labex) ACTION [ANR-11-LABX-01-01]
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The performance of data-driven prognostics approaches is closely dependent on the form and trend of extracted features. Indeed, features that clearly reflect the machine degradation should lead to accurate prognostics, which is the global objective of this paper. This paper contributes a new approach for feature extraction/selection: The extraction is based on trigonometric functions and cumulative transformation, and the selection is performed by evaluating feature fitness using monotonicity and trendability characteristics. The proposition is applied to the time-frequency analysis of nonstationary signals using a discrete wavelet transform. The main idea is to map raw vibration data into monotonic features with early trends, which can be easily predicted. To show that, selected features are used to build a model with a data-driven approach, namely, the summation wavelet-extreme learning machine, that enables good balance between model accuracy and complexity. For validation and generalization purposes, the vibration data from two real applications of prognostics and health management challenges are used: 1) cutting tools from a computer numerical control machine (2010); and 2) bearings from the platform PRONOSTIA (2012). The performance of the proposed approach is thoroughly compared with the classical approach by performing feature fitness analysis, cutting-tool wear estimation, and bearings' long-term prediction tasks, which validates the proposition.
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