A multi-modal hidden Markov model based approach for continuous health assessment in machinery systems

In this paper1, a multi-modal approach based on the single hidden Markov model (HMM) with continuous output is introduced for continuous health condition monitoring in machinery systems. Comparing with existing approaches such as single HMM-based approach, artificial neural networks (ANN) approach, auto-regressive moving average with exogenous inputs (ARMAX), the proposed approach improves the performance of health condition monitoring (HCM) by using multiple HMM models in parallel. Each model emphasizes on different regiments, and outputs of all models are integrated as the ultimate output. The integration of HMM outputs are conducted by either a parametric or a semi-nonparametric hindsight method. The proposed approach is applied to tool wear prediction of a CNC-milling machine, and results are compared with an existing HMM-based approach.

[1]  Frank L. Lewis,et al.  Intelligent Diagnosis and Prognosis of Tool Wear Using Dominant Feature Identification , 2009, IEEE Transactions on Industrial Informatics.

[2]  Geok Soon Hong,et al.  A COMPARATIVE STUDY OF FEATURE SELECTION FOR HIDDEN MARKOV MODEL-BASED MICRO-MILLING TOOL WEAR MONITORING , 2008 .

[3]  Yoke San Wong,et al.  Feature Extraction and Selection in Tool Condition Monitoring System , 2002, Australian Joint Conference on Artificial Intelligence.

[4]  Trevor Hastie,et al.  The Elements of Statistical Learning , 2001 .

[5]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[6]  Stuart J. Russell,et al.  Dynamic bayesian networks: representation, inference and learning , 2002 .

[7]  Xiang Li,et al.  Continuous health assessment using a single hidden Markov model , 2010, 2010 11th International Conference on Control Automation Robotics & Vision.

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

[9]  Zoubin Ghahramani,et al.  Learning Dynamic Bayesian Networks , 1997, Summer School on Neural Networks.

[10]  R. Tibshirani.  Regression Shrinkage and Selection via the Lasso , 1996 .

[11]  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).

[12]  Joseph Mathew,et al.  Rotating machinery prognostics. State of the art, challenges and opportunities , 2009 .

[13]  David He,et al.  A segmental hidden semi-Markov model (HSMM)-based diagnostics and prognostics framework and methodology , 2007 .

[14]  M. R. Osborne,et al.  On the LASSO and its Dual , 2000 .

[15]  David G. Stork,et al.  Pattern Classification , 1973 .

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

[17]  D. Mount.  Bioinformatics: Sequence and Genome Analysis , 2001 .