Publication: Sparse optimistic based on lasso-lsqr and minimum entropy de-convolution with FARIMA for the remaining useful life prediction of machinerys
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Issued Date
2018
Resource Type
File Type
application/pdf
ISSN
10994300
Other identifier(s)
2-s2.0-85055702737
Rights Holder(s)
Scopus
Bibliographic Citation
Entropy. Vol 20, No.10 (2018)
Suggested Citation
Wu B., Gao Y., Feng S., Chanwimalueang T. Sparse optimistic based on lasso-lsqr and minimum entropy de-convolution with FARIMA for the remaining useful life prediction of machinerys. Entropy. Vol 20, No.10 (2018). doi:10.3390/e20100781 Retrieved from: https://hdl.handle.net/20.500.14740/5834
Author(s)
Abstract
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.