2D-hidden Markov model feature extraction strategy of rotating machinery fault diagnosis

2006 ◽  
Vol 19 (01) ◽  
pp. 156
Author(s):  
Dapeng YE
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Rotating Machinery ◽  
Machinery Fault Diagnosis

scholarly journals Symbolic Important Point Perceptually and Hidden Markov Model Based Hydraulic Pump Fault Diagnosis Method

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4460 ◽  
Author(s):  
Yunzhao Jia ◽  
Minqiang Xu ◽  
Rixin Wang
Keyword(s):  
Fault Diagnosis ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hydraulic System ◽  
Hidden Markov ◽  
Operating Conditions ◽  
Fault Classification ◽  
Hydraulic Pump ◽  
Diagnosis Method ◽  
Original Time

Hydraulic pump is a driving device of the hydraulic system, always working under harsh operating conditions, its fault diagnosis work is necessary for the smooth running of a hydraulic system. However, it is difficult to collect sufficient status information in practical operating processes. In order to achieve fault diagnosis with poor information, a novel fault diagnosis method that is the based on Symbolic Perceptually Important Point (SPIP) and Hidden Markov Model (HMM) is proposed. Perceptually important point technology is firstly imported into rotating machine fault diagnosis; it is applied to compress the original time-series into PIP series, which can depict the overall movement shape of original time series. The PIP series is transformed into symbolic series that will serve as feature series for HMM, Genetic Algorithm is used to optimize the symbolic space partition scheme. The Hidden Markov Model is then employed for fault classification. An experiment involves four operating conditions is applied to validate the proposed method. The results show that the fault classification accuracy of the proposed method reaches 99.625% when each testing sample only containing 250 points and the signal duration is 0.025 s. The proposed method could achieve good performance under poor information conditions.

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