Multiway discrete hidden Markov model-based approach for dynamic batch process monitoring and fault classification

AIChE Journal ◽  
2011 ◽  
Vol 58 (9) ◽  
pp. 2714-2725 ◽  
Author(s):  
Jie Yu
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Process Monitoring ◽  
Hidden Markov ◽  
Batch Process ◽  
Fault Classification ◽  
Model Based ◽  
Batch Process Monitoring

A hybrid hidden Markov model towards fault detection of rotating components

2016 ◽  
Vol 23 (19) ◽  
pp. 3175-3195 ◽  
Author(s):  
Ayan Sadhu ◽  
Guru Prakash ◽  
Sriram Narasimhan
Keyword(s):  
Fault Detection ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Time Scale ◽  
Hidden Markov ◽  
Energy Operator ◽  
Fault Classification ◽  
Multiple Faults ◽  
Condition Indicators ◽  
Model Based

A robust hybrid hidden Markov model-based fault detection method is proposed to perform multi-state fault classification of rotating components. The approach presented in this paper enhances the performance of the standard hidden Markov model (HMM) for fault detection by performing a series of pre-processing steps. First, the de-noised time-scale signatures are extracted using wavelet packet decomposition of the vibration data. Subsequently, the Teager Kaiser energy operator is employed to demodulate the time-scale components of the raw vibration signatures, following which the condition indicators are calculated. Out of several possible condition indicators, only relevant features are selected using a decision tree. This pre-processing improves the sensitivity of condition indicators under multiple faults. A Gaussian mixing model-based hidden Markov model (HMM) is then employed for fault detection. The proposed hybrid HMM is an improvement over traditional HMM in that it achieves better separation of the feature space leading to more robust state estimation under multiple fault states and measurement noise scenarios. A simulation employing modulated signals and two experimental validation studies are presented to demonstrate the performance of the proposed method.

Gyro motor fault classification model based on a coupled hidden Markov model with a minimum intra-class distance algorithm

2019 ◽  
Vol 234 (5) ◽  
pp. 646-661 ◽  
Author(s):  
Lei Dong ◽  
Wei-min Li ◽  
Ching-Hsin Wang ◽  
Kuo-Ping Lin
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Correct Diagnosis ◽  
Feature Reduction ◽  
Classification Model ◽  
Fault Classification ◽  
Statistical Features ◽  
Model Based ◽  
Diagnosis Rate

In this study, we developed a fault classification model that combines a coupled hidden Markov model based on multi-channel information fusion with a minimum intra-class distance algorithm. This model relies on statistical features in the current time domain, which are the easiest features to extract for clustering. First, an algorithm is used to select and sequence the statistical features with the minimum intra-class distance in order to form feature vectors, which in turn enhance inter-class discrimination and feature reduction. Following reduction, the coupled hidden Markov model is used to perform classification. The coupled hidden Markov model was shown to reflect the coupling relationships between and among channels. We evaluated the efficacy of the proposed scheme by applying it to the diagnosis of faults in a gyro motor in three groups of experiments. Our results were compared with those obtained using a single-chain hidden Markov model and other intelligent fault diagnosis methods. The proposed scheme outperformed the other methods in terms of correct diagnosis rate, fluctuations in correct diagnosis rate, and excellent robustness against the effects of interference.

An unsupervised Hidden Markov Model-based system for the detection and classification of blue whale vocalizations off Chile

Bioacoustics ◽  
2019 ◽  
Vol 29 (2) ◽  
pp. 140-167 ◽  
Author(s):  
Susannah J. Buchan ◽  
Rodrigo Mahú ◽  
Jorge Wuth ◽  
Naysa Balcazar-Cabrera ◽  
Laura Gutierrez ◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Blue Whale ◽  
Model Based
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