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.
Highlights
Rotating machinery is widely used in industry; once a fault of the rotating machinery is found, such fault could cause the breakdown of the machine and lead to further severe losses [1,2,3]
We found that the lasso-LSQR can restore the most time–frequency information of the original signal, and the edge of signal matching is better than that of the least absolute shrinkage and selection operator (Lasso) and alternating direction method of multipliers (ADMM) algorithms
Skip-over can performance for non-dimensional quantity, which is sensitive to the occurrence of fault in the performance for non-dimensional quantity, which of is vibration sensitive bearing, to the occurrence of fault in that the evaluate the incipient and serious failure operation and the results show vibration signals
Summary
Rotating machinery is widely used in industry; once a fault of the rotating machinery is found, such fault could cause the breakdown of the machine and lead to further severe losses [1,2,3]. The parallel FISTA algorithm combined with the CS theory has been applied in the field of construction of vibration bearing Such method shows that the time–frequency perspective of the reconstruction signal can effectively detect a bearing fault. Sample Entropy (SampEn) and Approximate Entropy (ApEn) approaches were applied to examine nonlinear characteristics of vibration bearing signals Both the methods have become popular for practical applications. We propose a new indicator regarding the Skip-over, which is considered as a non-dimensional quantity This quantity is known to be sensitive to the fault of the vibration signals, so it is able to reflect the deteriorating state of the vibration signals and is suitable for analyzing the characteristics of the RUL prediction.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
