A Switching Hidden Semi-Markov Model for Degradation Process and Its Application to Time-Varying Tool Wear Monitoring

Hidden semi-Markov model (HSMM) has been widely used in equipment condition monitoring. However, the HSMM is usually modeled in fixed working mode. It is incompetent to monitor the condition when the working mode is varying in the equipment's lifetime. In this article, taking time-varying working mode into account, we propose a novel switching HSMM (SHSMM) to represent the equipment's degradation process. The reciprocal of duration is modeled and utilized to quantize the influence of working mode on the degradation process. Compared to traditional HSMM and time-varying HMM, the proposed SHSMM has a more generalized form and a more powerful ability to describe the degradation process with time-varying working mode. The proposed SHSMM is then applied to tool wear monitoring with time-varying cutting mode. Experimental results show that, via the proposed SHSMM, the monitoring confidence increases and the estimation of remaining useful life has a great improvement.

[1]  Xiang Li,et al.  A Physically Segmented Hidden Markov Model Approach for Continuous Tool Condition Monitoring: Diagnostics and Prognostics , 2012, IEEE Transactions on Industrial Informatics.

[2]  Yaojie Lu,et al.  Robust Diagnosis of Operating Mode Based on Time-Varying Hidden Markov Models , 2016, IEEE Transactions on Industrial Electronics.

[3]  Gary D. Bernard,et al.  Multilevel Classification of Milling Tool Wear with Confidence Estimation , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Svetha Venkatesh,et al.  Efficient duration and hierarchical modeling for human activity recognition , 2009, Artif. Intell..

[5]  John A. Quinn,et al.  Factorial Switching Linear Dynamical Systems Applied to Physiological Condition Monitoring , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Shudong Sun,et al.  A Hidden Semi-Markov Model with Duration-Dependent State Transition Probabilities for Prognostics , 2014 .

[7]  Tao Mei,et al.  Online Condition Monitoring in Micromilling: A Force Waveform Shape Analysis Approach , 2015, IEEE Transactions on Industrial Electronics.

[8]  Bo-Suk Yang,et al.  Machine performance degradation assessment and remaining useful life prediction using proportional hazard model and support vector machine , 2012, WCE 2010.

[9]  Kunpeng Zhu,et al.  Online Tool Wear Monitoring Via Hidden Semi-Markov Model With Dependent Durations , 2018, IEEE Transactions on Industrial Informatics.

[10]  Wojciech Pieczynski,et al.  Unsupervised segmentation of hidden semi-Markov non-stationary chains , 2006, Signal Process..

[11]  Geok Soon Hong,et al.  Multi-category micro-milling tool wear monitoring with continuous hidden Markov models , 2009 .

[12]  Ying Peng,et al.  A prognosis method using age-dependent hidden semi-Markov model for equipment health prediction , 2011 .

[13]  Jianbin Qiu,et al.  Descriptor reduced-order sliding mode observers design for switched systems with sensor and actuator faults , 2017, Autom..

[14]  W. Wang,et al.  Plant residual time modelling based on observed variables in oil samples , 2009, J. Oper. Res. Soc..

[15]  Wenyuan Lv,et al.  A novel method using adaptive hidden semi-Markov model for multi-sensor monitoring equipment health prognosis , 2015 .

[16]  Gérard-André Capolino,et al.  A Diagnostic Space Vector-Based Index for Rotor Electrical Fault Detection in Wound-Rotor Induction Machines Under Speed Transient , 2017, IEEE Transactions on Industrial Electronics.

[17]  Donghua Zhou,et al.  Remaining useful life estimation - A review on the statistical data driven approaches , 2011, Eur. J. Oper. Res..

[18]  Bo-Suk Yang,et al.  Support vector machine in machine condition monitoring and fault diagnosis , 2007 .

[19]  Shunzheng Yu,et al.  Hidden semi-Markov models , 2010, Artif. Intell..

[20]  Marcello Braglia,et al.  Diffusion theory applied to tool-life stochastic modeling under a progressive wear process , 2014 .

[21]  Svetha Venkatesh,et al.  Activity recognition and abnormality detection with the switching hidden semi-Markov model , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[22]  Tony L. Schmitz,et al.  Tool life prediction using Bayesian updating. Part 1: Milling tool life model using a discrete grid method , 2014 .

[23]  Huijun Gao,et al.  Data-Based Techniques Focused on Modern Industry: An Overview , 2015, IEEE Transactions on Industrial Electronics.

[24]  Hamid Reza Karimi,et al.  Fault detection for discrete-time Markov jump linear systems with partially known transition probabilities , 2010, Int. J. Control.

[25]  Radoslaw Zimroz,et al.  A new feature for monitoring the condition of gearboxes in non-stationary operating conditions , 2009 .

[26]  Sankalita Saha,et al.  Metrics for Offline Evaluation of Prognostic Performance , 2021, International Journal of Prognostics and Health Management.

[27]  Dongfeng Shi,et al.  Industrial Applications of Online Machining Process Monitoring System , 2007, IEEE/ASME Transactions on Mechatronics.

[28]  Donghua Zhou,et al.  A Wiener-process-based degradation model with a recursive filter algorithm for remaining useful life estimation , 2013 .

[29]  Zhiqiang Ge,et al.  HMM-Driven Robust Probabilistic Principal Component Analyzer for Dynamic Process Fault Classification , 2015, IEEE Transactions on Industrial Electronics.

[30]  Xiang Li,et al.  Continuous health condition monitoring: A single Hidden Semi-Markov Model approach , 2011, 2011 IEEE Conference on Prognostics and Health Management.

[31]  Xin Zhou,et al.  Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data , 2016 .

[32]  Yaguo Lei,et al.  An Improved Exponential Model for Predicting Remaining Useful Life of Rolling Element Bearings , 2015, IEEE Transactions on Industrial Electronics.

[33]  Ning Li,et al.  Hidden semi-Markov model-based method for tool wear estimation in milling process , 2017 .

[34]  Belkacem Ould Bouamama,et al.  Remaining Useful Life Prediction and Uncertainty Quantification of Proton Exchange Membrane Fuel Cell Under Variable Load , 2016, IEEE Transactions on Industrial Electronics.

[35]  Chee Khiang Pang,et al.  Gamma process with recursive MLE for wear PDF prediction in precognitive maintenance under aperiodic monitoring , 2015 .

[36]  Krzysztof Jemielniak,et al.  Advanced monitoring of machining operations , 2010 .

[37]  Liangcai Zeng,et al.  Diagnosis and Prognosis of Degradation Process via Hidden Semi-Markov Model , 2018, IEEE/ASME Transactions on Mechatronics.

[38]  Xiang Li,et al.  Multimodal Hidden Markov Model-Based Approach for Tool Wear Monitoring , 2014, IEEE Transactions on Industrial Electronics.