A Hybrid LSSVR/HMM-Based Prognostic Approach

Prognostics is an important aspect in health management system and condition-based maintenance (CBM). It is a task to predict the future health of systems, which has few researches so far. In this paper, a hybrid approach for failure prognostics is proposed. The approach combines least squares support vector regression (LSSVR) with hidden Markov model (HMM). Features extracted from sensor signals are used to train HMMs, which represent different health levels. LSSVR algorithm is used to predict feature trend. According to the probabilities of each HMM, it can determine the future health state and estimate the remaining useful life (RUL). To evaluate the proposed approach, test was carried out using bearing vibration signals. Simulation results show that the LSSVR-HMMs method is efficient in prognostics. It can forecast long before failures occur and predict the RUL.

[1]  Aitor Arnaiz,et al.  A Bayesian network model integrated in a prognostics and health management system for aircraft line maintenance , 2011 .

[2]  L. Baum,et al.  Statistical Inference for Probabilistic Functions of Finite State Markov Chains , 1966 .

[3]  X.U.E. Guoxin,et al.  Fault prediction of boilers with fuzzy mathematics and RBF neural network , 2005, Proceedings. 2005 International Conference on Communications, Circuits and Systems, 2005..

[4]  Taimoor Khawaja,et al.  A novel Architecture for on-line Failure Prognosis using Probabilistic Least Squares Support Vector Regression Machines , 2009 .

[5]  Johan A. K. Suykens,et al.  Weighted least squares support vector machines: robustness and sparse approximation , 2002, Neurocomputing.

[6]  T. Dabney,et al.  PHM a key enabler for the JSF autonomic logistics support concept , 2004, 2004 IEEE Aerospace Conference Proceedings (IEEE Cat. No.04TH8720).

[7]  K. Shadan,et al.  Available online: , 2012 .

[8]  Kenneth A. Loparo,et al.  A decision fusion algorithm for tool wear condition monitoring in drilling , 2001 .

[9]  Elijah Kannatey-Asibu,et al.  Hidden Markov model-based tool wear monitoring in turning , 2002 .

[10]  P. Baruah,et al.  HMMs for diagnostics and prognostics in machining processes , 2005 .

[11]  Johan A. K. Suykens,et al.  Sparse least squares Support Vector Machine classifiers , 2000, ESANN.

[12]  H. S. Wolff,et al.  iRun: Horizontal and Vertical Shape of a Region-Based Graph Compression , 2022, Sensors.

[13]  Michael G. Pecht,et al.  Sensor Systems for Prognostics and Health Management , 2010, Sensors.

[14]  K. Loparo,et al.  HMM-Based Fault Detection and Diagnosis Scheme for Rolling Element Bearings , 2005 .

[15]  B. Dickson,et al.  Feasibility Study of a Rotorcraft Health and Usage Monitoring System (HUMS): Usage and Structural Life Monitoring Evaluation. Phase 2. , 1996 .

[16]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

[17]  Sheng Liu,et al.  Investigation of steering dynamics ship model identification based on PSO-LSSVR , 2008, 2008 2nd International Symposium on Systems and Control in Aerospace and Astronautics.

[18]  Jong Min Lee,et al.  Diagnosis of mechanical fault signals using continuous hidden Markov model , 2004 .

[19]  Kenneth A. Loparo,et al.  A new bearing fault detection and diagnosis scheme based on hidden Markov modeling of vibration signals , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

[20]  E. C. Baroth,et al.  IVHM (Integrated Vehicle Health Management) techniques for future space vehicles , 2001 .

[21]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[22]  Ming Jian Zuo,et al.  An LSSVR-based algorithm for online system condition prognostics , 2012, Expert Syst. Appl..

[23]  B. L. Ferrell.  Air vehicle prognostics and health management , 2000, 2000 IEEE Aerospace Conference. Proceedings (Cat. No.00TH8484).

[24]  P. Young,et al.  Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.

[25]  Les E. Atlas,et al.  Hidden Markov models for monitoring machining tool-wear , 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).

[26]  Zhi-huan Song,et al.  Online monitoring of nonlinear multiple mode processes based on adaptive local model approach , 2008 .