Hidden Markov Models for the Prediction of Impending Faults

Reliability and safety are two important concepts in industrial applications. Thus, the development of monitoring tools, which are able to ensure the continuity of service by predicting faults, should improve competitiveness. This paper presents two probabilistic methods based on hidden Markov models (HMMs) for the prediction of impending faults. This paper shows that a prediction of faults is not limited to the estimation of the remaining useful life but is also extended to the estimation of the risk of an imminent appearance of faults in the future. The first method consists in modeling the degradation process of the studied system by a single HMM. A probabilistic model is proposed to predict an imminent appearance of a fault. The second method consists in modeling the degradation states by a set of HMMs. Another probabilistic model is proposed to predict an imminent appearance of a fault. An experimental application is proposed to demonstrate their applicability. The obtained results show their effectiveness to predict the imminent appearance of faults.

[1]  Selin Aviyente,et al.  Prognosis of Gear Failures in DC Starter Motors Using Hidden Markov Models , 2011, IEEE Transactions on Industrial Electronics.

[2]  Behrad Bagheri,et al.  Application of data mining and feature extraction on intelligent fault diagnosis by Artificial Neural Network and k-nearest neighbor , 2010, The XIX International Conference on Electrical Machines - ICEM 2010.

[3]  Bhim Singh,et al.  Investigation of Vibration Signatures for Multiple Fault Diagnosis in Variable Frequency Drives Using Complex Wavelets , 2014, IEEE Transactions on Power Electronics.

[4]  Gianluca Ippoliti,et al.  Electric Motor Fault Detection and Diagnosis by Kernel Density Estimation and Kullback–Leibler Divergence Based on Stator Current Measurements , 2015, IEEE Transactions on Industrial Electronics.

[5]  Steven X. Ding,et al.  A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part II: Fault Diagnosis With Knowledge-Based and Hybrid/Active Approaches , 2015, IEEE Transactions on Industrial Electronics.

[6]  L. Baum,et al.  A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains , 1970 .

[7]  Danwei Wang,et al.  Model-Based Prognosis for Hybrid Systems With Mode-Dependent Degradation Behaviors , 2014, IEEE Transactions on Industrial Electronics.

[8]  Jie Yu.  Multiway discrete hidden Markov model‐based approach for dynamic batch process monitoring and fault classification , 2012 .

[9]  Feng Li,et al.  Survey of signal processing methods and research on vibrating screen fault diagnosis , 2011, 2011 Second International Conference on Mechanic Automation and Control Engineering.

[10]  Liu Dongfeng,et al.  Research on combination of data-driven and probability-based prognostics techniques for equipments , 2014, 2014 Prognostics and System Health Management Conference (PHM-2014 Hunan).

[11]  Steven X. Ding,et al.  A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches , 2015, IEEE Transactions on Industrial Electronics.

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

[13]  Okyay Kaynak,et al.  Improved PLS Focused on Key-Performance-Indicator-Related Fault Diagnosis , 2015, IEEE Transactions on Industrial Electronics.

[14]  Hubert Razik,et al.  Detection and Diagnosis of Faults in Induction Motor Using an Improved Artificial Ant Clustering Technique , 2013, IEEE Transactions on Industrial Electronics.

[15]  Noureddine Zerhouni,et al.  Bearing Health Monitoring Based on Hilbert–Huang Transform, Support Vector Machine, and Regression , 2015, IEEE Transactions on Instrumentation and Measurement.

[16]  Selin Aviyente,et al.  Extended Kalman Filtering for Remaining-Useful-Life Estimation of Bearings , 2015, IEEE Transactions on Industrial Electronics.

[17]  Emily Mower Provost,et al.  Emotion recognition from spontaneous speech using Hidden Markov models with deep belief networks , 2013, 2013 IEEE Workshop on Automatic Speech Recognition and Understanding.

[18]  Selin Aviyente,et al.  The Effect of Failure Prognosis and Mitigation on the Reliability of Permanent-Magnet AC Motor Drives , 2013, IEEE Transactions on Industrial Electronics.

[19]  Faa-Jeng Lin,et al.  Fault-Tolerant Control for Six-Phase PMSM Drive System via Intelligent Complementary Sliding-Mode Control Using TSKFNN-AMF , 2013, IEEE Transactions on Industrial Electronics.

[20]  Linxia Liao,et al.  Discovering Prognostic Features Using Genetic Programming in Remaining Useful Life Prediction , 2014, IEEE Transactions on Industrial Electronics.

[21]  Myeongsu Kang,et al.  An FPGA-Based Multicore System for Real-Time Bearing Fault Diagnosis Using Ultrasampling Rate AE Signals , 2015, IEEE Transactions on Industrial Electronics.

[22]  Qingbo He,et al.  Sparse Signal Reconstruction Based on Time–Frequency Manifold for Rolling Element Bearing Fault Signature Enhancement , 2016, IEEE Transactions on Instrumentation and Measurement.

[23]  Bing Long,et al.  Diagnostics and Prognostics Method for Analog Electronic Circuits , 2013, IEEE Transactions on Industrial Electronics.

[24]  Gérard-André Capolino,et al.  Modern Diagnostics Techniques for Electrical Machines, Power Electronics, and Drives , 2015, IEEE Transactions on Industrial Electronics.

[25]  Raghunathan Rengaswamy,et al.  A review of process fault detection and diagnosis: Part III: Process history based methods , 2003, Comput. Chem. Eng..

[26]  H. Razik,et al.  Fault detection and diagnosis of induction motors based on hidden Markov model , 2012, 2012 XXth International Conference on Electrical Machines.

[27]  Shahin Hedayati Kia,et al.  Gear tooth surface damage fault detection using induction machine electrical signature analysis , 2013, 2013 9th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED).

[28]  Yi Xie,et al.  Resisting Web Proxy-Based HTTP Attacks by Temporal and Spatial Locality Behavior , 2013, IEEE Transactions on Parallel and Distributed Systems.

[29]  Venkat Venkatasubramanian,et al.  Challenges in the industrial applications of fault diagnostic systems , 2000 .

[30]  Steven X. Ding,et al.  A Review on Basic Data-Driven Approaches for Industrial Process Monitoring , 2014, IEEE Transactions on Industrial Electronics.

[31]  Abdesselam Lebaroud,et al.  Classification method for faults diagnosis in reluctance motors using Hidden Markov Models , 2014, 2014 IEEE 23rd International Symposium on Industrial Electronics (ISIE).

[32]  Xiaojun Wu,et al.  Multiple Sequence Alignment with Hidden Markov Models Learned by Random Drift Particle Swarm Optimization , 2014, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[33]  Y. Gritli,et al.  Vibration signature analysis for rotor broken bar diagnosis in double cage induction motor drives , 2013, 4th International Conference on Power Engineering, Energy and Electrical Drives.

[34]  Eric Bechhoefer,et al.  Development of a new acoustic emission based fault diagnosis tool for gearbox , 2013, 2013 IEEE Conference on Prognostics and Health Management (PHM).

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