A model for online failure prognosis subject to two failure modes based on belief rule base and semi-quantitative information

As one of most important aspects in condition-based maintenance (CBM), failure prognosis has attracted an increasing attention with the growing demand for higher operational efficiency and safety in complex engineering systems. Currently there are no effective methods for predicting the failure of a system in real-time by using both expert knowledge and quantitative information (i.e., semi-quantitative information) when degradation failure and shock failure are dependent and competitive. Since belief rule base (BRB) can model the complex system when semi-quantitative information is available, this paper focuses on developing a new BRB based method for online failure prognosis that can deal with this problem. Although it is difficult to obtain accurate and complete quantitative information, some expert knowledge can be collected and represented by a BRB which is an expert system essentially. As such, a new BRB based prognosis model is proposed to predict the system failure in real-time when two failure modes are dependent and competitive. Moreover, a recursive algorithm for online updating the parameters of the failure prognosis model is developed. Equipped with the recursive algorithm, the proposed prognosis model can predict the failure in real-time when two failure modes are dependent and competitive. An experimental case study is examined to demonstrate the implementation and potential applications of the proposed online failure prognosis method.

[1]  Ian Jenkinson,et al.  Inference and learning methodology of belief-rule-based expert system for pipeline leak detection , 2007, Expert Syst. Appl..

[2]  David He,et al.  Hidden semi-Markov model-based methodology for multi-sensor equipment health diagnosis and prognosis , 2007, Eur. J. Oper. Res..

[3]  Wei Huang.  Reliability analysis considering product performance degradation , 2002 .

[4]  Yada Zhu,et al.  Availability optimization of systems subject to competing risk , 2010, Eur. J. Oper. Res..

[5]  Donghua Zhou,et al.  A model for real-time failure prognosis based on hidden Markov model and belief rule base , 2010, Eur. J. Oper. Res..

[6]  Daming Lin,et al.  A review on machinery diagnostics and prognostics implementing condition-based maintenance , 2006 .

[7]  Jian-Bo Yang,et al.  Belief rule-base inference methodology using the evidential reasoning Approach-RIMER , 2006, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[8]  Wenbin Wang.  A two-stage prognosis model in condition based maintenance , 2007, Eur. J. Oper. Res..

[9]  Axel Lehmann.  Joint modeling of degradation and failure time data , 2009 .

[10]  Hoang Pham,et al.  Reliability modeling of multi-state degraded systems with multi-competing failures and random shocks , 2005, IEEE Trans. Reliab..

[11]  Keh-Shew Lu.  Failure Prediction for an On-Line Maintenance System in a Poisson Shock Environment. , 1977 .

[12]  Vincent Couallier.  Some recent results on joint degradation and failure time modeling , 2006 .

[13]  Wenbin Wang,et al.  Condition-based maintenance of dynamic systems using online failure prognosis and belief rule base , 2012, Expert Syst. Appl..

[14]  Jian-Bo Yang,et al.  Online updating belief rule based system for pipeline leak detection under expert intervention , 2009, Expert Syst. Appl..

[15]  Wenbin Wang,et al.  A model to predict the residual life of rolling element bearings given monitored condition information to date , 2002 .

[16]  Bo Henry Lindqvist,et al.  Modeling of dependent competing risks by first passage times of Wiener processes , 2008 .

[17]  Jian-Bo Yang,et al.  Rule and utility based evidential reasoning approach for multiattribute decision analysis under uncertainties , 2001, Eur. J. Oper. Res..

[18]  Maurizio Guida,et al.  A competing risk model for the reliability of cylinder liners in marine Diesel engines , 2009, Reliab. Eng. Syst. Saf..

[19]  Axel Lehmann.  Degradation-Threshold-Shock Models , 2006 .

[20]  Jiang Jiang,et al.  A new BRB based method to establish hidden failure prognosis model by using life data and monitoring observation , 2014, Knowl. Based Syst..

[21]  Jian-Bo Yang,et al.  A sequential learning algorithm for online constructing belief-rule-based systems , 2010, Expert Syst. Appl..

[22]  Jian-Bo Yang,et al.  Online Updating Belief-Rule-Base Using the RIMER Approach , 2011, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[23]  A. H. Christer,et al.  Towards a general condition based maintenance model for a stochastic dynamic system , 2000, J. Oper. Res. Soc..

[24]  Jian-Bo Yang,et al.  Optimization Models for Training Belief-Rule-Based Systems , 2007, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[25]  Vincent Couallier.  A Competing Risks Model for Degradation and Traumatic Failure Times , 2008 .

[26]  Wei Huang,et al.  Reliability analysis of electronic devices with multiple competing failure modes involving performance aging degradation , 2003 .

[27]  Jian-Bo Yang,et al.  New model for system behavior prediction based on belief rule based systems , 2010, Inf. Sci..

[28]  Chang-Hua Hu,et al.  Hidden Behavior Prediction of Complex Systems Based on Hybrid Information , 2013, IEEE Transactions on Cybernetics.

[29]  Vilijandas Bagdonavičius,et al.  Statistical Analysis of Linear Degradation and Failure Time Data with Multiple Failure Modes , 2004, Lifetime data analysis.