Please use this identifier to cite or link to this item: https://ir.swu.ac.th/jspui/handle/123456789/12746
ชื่อเรื่อง: Sparse optimistic based on lasso-lsqr and minimum entropy de-convolution with FARIMA for the remaining useful life prediction of machinerys
ผู้แต่ง: Wu B.
Gao Y.
Feng S.
Chanwimalueang T.
วันที่เผยแพร่: 2018
บทคัดย่อ: To reduce the maintenance cost and safeguard machinery operation, remaining useful life (RUL) prediction is very important for long term health monitoring. In this paper, we introduce a novel hybrid method to deal with the RUL prediction for health management. Firstly, the sparse reconstruction algorithm of the optimized Lasso and the Least Square QR-factorization (Lasso-LSQR) is applied to compressed sensing (CS), which can realize the sparse optimization for long term health monitoring data. After the sparse signal is reconstructed, the minimum entropy de-convolution (MED) is used to identify the fault characteristics and to obtain significant fault information from the machinery operation. Health indicators with Skip-over, sample entropy and approximate entropy are then performed to track the degradation of the machinery process. The performance analysis of the Skip-over is superior to other indicators. Finally, Fractal Autoregressive Integrated Moving Average model (FARIMA) is employed to predict the Skip-over using the R/S method. The analysis results evidence that the novel hybrid method yields a good performance, and such method can achieve highly accurate RUL prediction and safeguard machinery operation for long term monitoring. © 2018 by the authors.
URI: https://ir.swu.ac.th/jspui/handle/123456789/12746
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85055702737&doi=10.3390%2fe20100781&partnerID=40&md5=bd47eef9707652363ebe226244928a36
ISSN: 10994300
Appears in Collections:Scopus 1983-2021

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