Increasing mapping based hidden Markov model for dynamic process monitoring and diagnosis

2014 ◽  
Vol 41 (2) ◽  
pp. 744-751 ◽  
Author(s):  
Zefang Li ◽  
Huajing Fang ◽  
Lisha Xia
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Process Monitoring ◽  
Dynamic Process ◽  
Hidden Markov ◽  
Monitoring And Diagnosis ◽  
Increasing Mapping

Adaptive Anomaly Detection Using a Hidden Markov Model

2010 ◽  
Author(s):  
Seungchul Lee ◽  
Lin Li ◽  
Jun Ni
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Manufacturing Industry ◽  
Failure Modes ◽  
Hidden Markov ◽  
Turning Process ◽  
Statistical Process ◽  
Monitoring And Diagnosis ◽  
New States ◽  
Modern Manufacturing

Online condition monitoring and diagnosis systems are very important in the modern manufacturing industry. We present a new method to assess the degradation processes of multiple failure modes using the Hidden Markov Model (HMM). The HMM is combined with statistical process control (SPC) to detect the occurrence of unknown faults. This method allows an HMM to adjust and update the state space with the identification of new states. Hence, the online degradation assessment and adaptive fault diagnosis can be simultaneously obtained. The turning process are used to illustrate that previously unknown tool wear processes can be successfully detected at the early stages using the HMM.

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