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A Hybrid LSSVR/HMM-Based Prognostic Approach
Department of Automation, Tsinghua University, Beijing 100084, China
Aerospace System Engineering Shanghai, Shanghai 201109, China
CAD Research Center, Tongji University, Shanghai 200092, China
* Author to whom correspondence should be addressed.
Received: 20 March 2013; in revised form: 11 April 2013 / Accepted: 19 April 2013 / Published: 26 April 2013
Abstract: In a health management system, prognostics, which is an engineering discipline that predicts a system’s future health, is an important aspect yet there is currently limited research in this field. In this paper, a hybrid approach for prognostics is proposed. The approach combines the least squares support vector regression (LSSVR) with the hidden Markov model (HMM). Features extracted from sensor signals are used to train HMMs, which represent different health levels. A LSSVR algorithm is used to predict the feature trends. The LSSVR training and prediction algorithms are modified by adding new data and deleting old data and the probabilities of the predicted features for each HMM are calculated based on forward or backward algorithms. Based on these probabilities, one can determine a system’s future health state and estimate the remaining useful life (RUL). To evaluate the proposed approach, a test was carried out using bearing vibration signals. Simulation results show that the LSSVR/HMM approach can forecast faults long before they occur and can predict the RUL. Therefore, the LSSVR/HMM approach is very promising in the field of prognostics.
Keywords: prognostics; least squares support vector regression; hidden Markov model; remaining useful life
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Cite This Article
MDPI and ACS Style
Liu, Z.; Li, Q.; Liu, X.; Mu, C. A Hybrid LSSVR/HMM-Based Prognostic Approach. Sensors 2013, 13, 5542-5560.
Liu Z, Li Q, Liu X, Mu C. A Hybrid LSSVR/HMM-Based Prognostic Approach. Sensors. 2013; 13(5):5542-5560.
Liu, Zhijuan; Li, Qing; Liu, Xianhui; Mu, Chundi. 2013. "A Hybrid LSSVR/HMM-Based Prognostic Approach." Sensors 13, no. 5: 5542-5560.