期刊
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
卷 69, 期 9, 页码 6212-6220出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIM.2020.2967113
关键词
Kernel; Fitting; Signal analysis; Noise measurement; Monitoring; Sensors; Optical fiber filters; Encoder signal analysis (ESA); fault detection; generalized difference filter (GDF); kernel ridge regression (KRR); machinery
资金
- National Natural Science Foundation of China [51875434]
- Natural Science Foundation of Shaanxi Province [2019JM-278]
- National Key Laboratory of Science and Technology on Reliability and Environmental Engineering
Encoder signal analysis (ESA) provides a novel tool for condition monitoring of machinery. Nevertheless, how to enhance the incipient fault signatures using ESA, especially in noisy measurements, remains a challenging issue. In view of this limitation, a new method termed generalized difference filter (GDF) is proposed for weak feature enhancement of encoder signals. To better capture the fault transients, kernel ridge regression is first introduced for signal approximation in high-dimensional feature space. A stochastic error minimization scheme is then proposed to improve approximation accuracy in a data-driven manner. Finally, a fast algorithm for GDF is developed based on the Gaussian kernel. With this method, the denoising and differencing of encoder signals can be treated in a unified framework. The effectiveness of the proposed method is validated by both simulated studies and experimental data.
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