Online diagnosis of accidental faults for real-time embedded systems using a hidden Markov model

This article proposes an approach for the online analysis of accidental faults for real-time embedded systems using hidden Markov models (HMMs). By introducing reasonable and appropriate abstraction of complex systems, HMMs are used to describe the healthy or faulty states of system’s hardware components. They are parametrized to statistically simulate the real system’s behavior. As it is not easy to obtain rich accidental fault data from a system, the Baum–Welch algorithm cannot be employed here to train the parameters in HMMs. Inspired by the principles of fault tree analysis and the maximum entropy in Bayesian probability theory, we propose to compute the failure propagation distribution to estimate the parameters in HMMs and to adapt the parameters using a backward algorithm. The parameterized HMMs are then used to online diagnose accidental faults using a vote algorithm integrated with a low-pass filter. We design a specific test bed to analyze the sensitivity, specificity, precision, accuracy and F1-score measures by generating a large amount of test cases. The test results show that the proposed approach is robust, efficient and accurate.

[1]  L. Baum,et al.  An inequality and associated maximization technique in statistical estimation of probabilistic functions of a Markov process , 1972 .

[2]  Rolf Isermann,et al.  Model-based fault-detection and diagnosis - status and applications , 2004, Annu. Rev. Control..

[3]  Janos Gertler,et al.  Fault detection and diagnosis in engineering systems , 1998 .

[4]  Raghunathan Rengaswamy,et al.  A review of process fault detection and diagnosis: Part I: Quantitative model-based methods , 2003, Comput. Chem. Eng..

[5]  Alex Bateman,et al.  An introduction to hidden Markov models. , 2007, Current protocols in bioinformatics.

[6]  Rolf Isermann,et al.  Trends in the Application of Model Based Fault Detection and Diagnosis of Technical Processes , 1996 .

[7]  Shin Nakajima,et al.  Efficient online analysis of accidental fault localization for dynamic systems using hidden Markov model , 2013, SpringSim.

[8]  Andrew J. Viterbi,et al.  Error bounds for convolutional codes and an asymptotically optimum decoding algorithm , 1967, IEEE Trans. Inf. Theory.

[9]  Brian Randell,et al.  Fundamental Concepts of Dependability , 2000 .

[10]  P. J. Green,et al.  Density Estimation for Statistics and Data Analysis , 1987 .

[11]  Shin Nakajima,et al.  Co-analysis of SysML and Simulink Models for Cyber-Physical Systems Design , 2012, 2012 IEEE International Conference on Embedded and Real-Time Computing Systems and Applications.

[12]  R. Palmer,et al.  Introduction to the theory of neural computation , 1994, The advanced book program.

[13]  Rolf Isermann,et al.  Trends in the Application of Model Based Fault Detection and Diagnosis of Technical Processes , 1996 .

[14]  Padhraic J. Smyth,et al.  Hidden Markov models for fault detection in dynamic systems , 1993 .

[15]  Bharat B. Madan,et al.  A method for modeling and quantifying the security attributes of intrusion tolerant systems , 2004, Perform. Evaluation.

[16]  Ming Liang,et al.  Detection and diagnosis of bearing and cutting tool faults using hidden Markov models , 2011 .

[17]  Carl E. Landwehr,et al.  Basic concepts and taxonomy of dependable and secure computing , 2004, IEEE Transactions on Dependable and Secure Computing.

[18]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[19]  John Bell,et al.  A review of methods for the assessment of prediction errors in conservation presence/absence models , 1997, Environmental Conservation.

[20]  Edmund M. Clarke,et al.  Statistical Model Checking for Cyber-Physical Systems , 2011, ATVA.

[21]  Krishna R. Pattipati,et al.  A hidden Markov model-based algorithm for fault diagnosis with partial and imperfect tests , 2000, IEEE Trans. Syst. Man Cybern. Part C.

[22]  Edward A. Lee.  Cyber Physical Systems: Design Challenges , 2008, 2008 11th IEEE International Symposium on Object and Component-Oriented Real-Time Distributed Computing (ISORC).

[23]  Anders Krogh,et al.  Introduction to the theory of neural computation , 1994, The advanced book program.

[24]  Noureddine Zerhouni,et al.  A Data-Driven Failure Prognostics Method Based on Mixture of Gaussians Hidden Markov Models , 2012, IEEE Transactions on Reliability.

[25]  M. Rosenblatt.  A CENTRAL LIMIT THEOREM AND A STRONG MIXING CONDITION. , 1956, Proceedings of the National Academy of Sciences of the United States of America.

[26]  Shin Nakajima,et al.  Hidden Markov model based automated fault localization for integration testing , 2013, 2013 IEEE 4th International Conference on Software Engineering and Service Science.