Hidden Markov model based rotate vector reducer fault detection using acoustic emissions

2020 ◽  
Vol 32 (2) ◽  
pp. 116
Author(s):  
Jindong Tan ◽  
Wei Liang ◽  
Yinlong Zhang ◽  
Haibo An
Keyword(s):  
Fault Detection ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Acoustic Emissions ◽  
Model Based

Hidden Markov model based rotate vector reducer fault detection using acoustic emissions

2020 ◽  
Vol 32 (2) ◽  
pp. 116
Author(s):  
Haibo An ◽  
Wei Liang ◽  
Yinlong Zhang ◽  
Jindong Tan
Keyword(s):  
Fault Detection ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Acoustic Emissions ◽  
Model Based

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.

Hidden Markov Model-Based Fault Detection Approach for a Multimode Process

2016 ◽  
Vol 55 (16) ◽  
pp. 4613-4621 ◽  
Author(s):  
Fan Wang ◽  
Shuai Tan ◽  
Yawei Yang ◽  
Hongbo Shi
Keyword(s):  
Fault Detection ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Model Based ◽  
Detection Approach

Multiway continuous hidden Markov model-based approach for fault detection and diagnosis

AIChE Journal ◽  
2014 ◽  
Vol 60 (6) ◽  
pp. 2035-2047 ◽  
Author(s):  
Deepthi Sen ◽  
Dilshad Raihan ◽  
M. Chidambaram
Keyword(s):  
Fault Detection ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Fault Detection And Diagnosis ◽  
Model Based ◽  
Detection And Diagnosis
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