Sparse Optimistic Based on Lasso-LSQR and Minimum Entropy De-Convolution with FARIMA for the Remaining Useful Life Prediction of Machinery

Wu, Bo; Gao, Yangde; Feng, Songlin; Chanwimalueang, Theerasak

doi:10.3390/e20100747

Submit to this Journal Review for this Journal Edit a Special Issue

Article Menu

...

Open AccessArticle

Sparse Optimistic Based on Lasso-LSQR and Minimum Entropy De-Convolution with FARIMA for the Remaining Useful Life Prediction of Machinery

Bo Wu

^1,2,

Yangde Gao

^1,*

Songlin Feng

^1,2 and

Theerasak Chanwimalueang

Shanghai Advanced Research Institute, Chinese Academy of Sciences, 99, Hai Ke Road, Shanghai 201210, China

University of Chinese Academy of Sciences, Beijing 100049, China

Department of Biomedical Engineering, Faculty of Engineering, Srinakharinwirot University, Nakhon Nayok 26120, Thailand

Author to whom correspondence should be addressed.

Entropy 2018, 20(10), 747; https://doi.org/10.3390/e20100747

Received: 11 September 2018 / Revised: 28 September 2018 / Accepted: 28 September 2018 / Published: 29 September 2018

(This article belongs to the Special Issue Information Theory Applications in Signal Processing)

View Full-Text Download PDF

Browse Figures

Citation Export

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. View Full-Text

Keywords: compressed sensing (CS); Lasso-LSQR; MED; skip-over; FARIMA; RUL prediction

▼ Show Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Share and Cite

MDPI and ACS Style

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 Machinery. Entropy 2018, 20, 747. https://doi.org/10.3390/e20100747

AMA Style

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 Machinery. Entropy. 2018; 20(10):747. https://doi.org/10.3390/e20100747

Chicago/Turabian Style

Wu, Bo, Yangde Gao, Songlin Feng, and Theerasak Chanwimalueang. 2018. "Sparse Optimistic Based on Lasso-LSQR and Minimum Entropy De-Convolution with FARIMA for the Remaining Useful Life Prediction of Machinery" Entropy 20, no. 10: 747. https://doi.org/10.3390/e20100747

Find Other Styles

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Metrics

Abstract views Pdf views Html views

Article Menu

Sparse Optimistic Based on Lasso-LSQR and Minimum Entropy De-Convolution with FARIMA for the Remaining Useful Life Prediction of Machinery

Abstract

Share and Cite

Article Metrics

Article Access Statistics

Article Access Map by Country/Region

Further Information

Guidelines

MDPI Initiatives

Follow MDPI