Online Degradation Assessment and Adaptive Fault Detection Using Modified Hidden Markov Model

2010 ◽  
Vol 132 (2) ◽  
Author(s):  
Seungchul Lee ◽  
Lin Li ◽  
Jun Ni
Keyword(s):  
Tool Wear ◽  
Markov Model ◽  
Hidden Markov Model ◽  
State Space ◽  
Manufacturing Industry ◽  
Failure Modes ◽  
Hidden Markov ◽  
Statistical Process ◽  
New States ◽  
Novel Method

Online condition monitoring and diagnosis systems play an important role in the modern manufacturing industry. This paper presents a novel method to diagnose the degradation processes of multiple failure modes using a modified hidden Markov model (MHMM) with variable state space. The proposed MHMM is combined with statistical process control to quickly detect the occurrence of an unknown fault. This method allows the state space of a hidden Markov model to be adjusted and updated with the identification of new states. Hence, the online degradation assessment and adaptive fault diagnosis can be simultaneously obtained. Experimental results in a turning process illustrate that the tool wear state can be successfully detected, and previously unknown tool wear processes can be identified at the early stages using the MHMM.

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.

Sequence-based protein structure prediction using a reduced state-space hidden Markov model

2007 ◽  
Vol 37 (9) ◽  
pp. 1211-1224 ◽  
Author(s):  
Christos Lampros ◽  
Costas Papaloukas ◽  
Themis P. Exarchos ◽  
Yorgos Goletsis ◽  
Dimitrios I. Fotiadis
Keyword(s):  
Protein Structure ◽  
Markov Model ◽  
Hidden Markov Model ◽  
State Space ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Hidden Markov ◽  
Reduced State

Improving the protein fold recognition accuracy of a reduced state-space hidden Markov model

2009 ◽  
Vol 39 (10) ◽  
pp. 907-914 ◽  
Author(s):  
Christos Lampros ◽  
Costas Papaloukas ◽  
Kostas Exarchos ◽  
Dimitrios I. Fotiadis ◽  
Dimitrios Tsalikakis
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
State Space ◽  
Recognition Accuracy ◽  
Hidden Markov ◽  
Fold Recognition ◽  
Protein Fold ◽  
Protein Fold Recognition ◽  
Reduced State

scholarly journals Blind bandwidth extension of audio signals based on non-linear prediction and hidden Markov model

2014 ◽  
Vol 3 ◽  
Author(s):  
Xin Liu ◽  
Changchun Bao
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
State Space ◽  
Linear Prediction ◽  
Hidden Markov ◽  
Audio Signals ◽  
Bandwidth Extension ◽  
Fine Spectrum ◽  
Non Linear ◽  
Audio Codec

The bandwidth limitation of wideband (WB) audio systems degrades the subjective quality and naturalness of audio signals. In this paper, a new method for blind bandwidth extension of WB audio signals is proposed based on non-linear prediction and hidden Markov model (HMM). The high-frequency (HF) components in the band of 7–14 kHz are artificially restored only from the low-frequency information of the WB audio. State-space reconstruction is used to convert the fine spectrum of WB audio to a multi-dimensional space, and a non-linear prediction based on nearest-neighbor mapping is employed in the state space to restore the fine spectrum of the HF components. The spectral envelope of the resulting HF components is estimated based on an HMM according to the features extracted from the WB audio. In addition, the proposed method and the reference methods are applied to the ITU-T G.722.1 WB audio codec for comparison with the ITU-T G.722.1C super WB audio codec. Objective quality evaluation results indicate that the proposed method is preferred over the reference bandwidth extension methods. Subjective listening results show that the proposed method has a comparable audio quality with G.722.1C and improves the extension performance compared with the reference methods.

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