The Research of DHMM in Diagnosing Malfunction for the Closing down of Rotary Machine

2012 ◽  
Vol 546-547 ◽  
pp. 182-187
Author(s):  
Wei Hua Liu ◽  
Yu Bin Gao
Keyword(s):  
Fault Diagnosis ◽  
Markov Models ◽  
Vibration Spectrum ◽  
Hidden Markov ◽  
Quantization Method ◽  
Scalar Quantization ◽  
Rotary Machine ◽  
Diagnosis Method ◽  
Run Up ◽  
Dynamical Pattern

This paper proposes the basic concept of dynamical pattern recognition and introduces the implementation theories based on probability statistics .Based on the theory of the Discrete Hidden Markov Models (DHMM), the scalar quantization method of vibration spectrum vector of rotary machine is proposed, and then the fault diagnosis method based on DHMM is designed. The experiment of run-up process of rotor machine is made to verify the effect of the new fault diagnosis method.

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.

scholarly journals Spectral Kurtosis of Choi–Williams Distribution and Hidden Markov Model for Gearbox Fault Diagnosis

Symmetry ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 285 ◽  
Author(s):  
Yufei Li ◽  
Wanqing Song ◽  
Fei Wu ◽  
Enrico Zio ◽  
Yujin Zhang
Keyword(s):  
Fault Diagnosis ◽  
Markov Models ◽  
Hidden Markov ◽  
Characteristic Parameters ◽  
Characteristic Sequence ◽  
Vibration Signals ◽  
Spectral Kurtosis ◽  
Product Function ◽  
Multicomponent Signals ◽  
Gearbox Vibration

A combination of spectral kurtosis (SK), based on Choi–Williams distribution (CWD) and hidden Markov models (HMM), accurately identifies initial gearbox failures and diagnoses fault types of gearboxes. First, using the LMD algorithm, five types of gearbox vibration signals are collected and decomposed into several product function (PF) components and the multicomponent signals are decomposed into single-component signals. Then, the kurtosis value of each component is calculated, and the component with the largest kurtosis value is selected for the CWD-SK analysis. According to the calculated CWD-SK value, the characteristics of the initial failure of the gearbox are extracted. This method not only avoids the difficulty of selecting the window function, but also provides original eigenvalues for fault feature classification. In the end, from the CWD-SK characteristic parameters at each characteristic frequency, the characteristic sequence based on CWD-SK is obtained with HMM training and diagnosis. The experimental results show that this method can effectively identify the initial fault characteristics of the gearbox, and also accurately classify the fault characteristics of different degrees.

scholarly journals A Refined Composite Multivariate Multiscale Fluctuation Dispersion Entropy and Its Application to Multivariate Signal of Rotating Machinery

Entropy ◽  
2021 ◽  
Vol 23 (1) ◽  
pp. 128
Author(s):  
Chenbo Xi ◽  
Guangyou Yang ◽  
Lang Liu ◽  
Hongyuan Jiang ◽  
Xuehai Chen
Keyword(s):  
Fault Diagnosis ◽  
Single Channel ◽  
Vibration Signal ◽  
Rotating Machinery ◽  
Support Vector ◽  
Data Set ◽  
Rotary Machine ◽  
Gear Fault ◽  
Diagnosis Method ◽  
Low Sensitivity

In the fault monitoring of rotating machinery, the vibration signal of the bearing and gear in a complex operating environment has poor stationarity and high noise. How to accurately and efficiently identify various fault categories is a major challenge in rotary fault diagnosis. Most of the existing methods only analyze the single channel vibration signal and do not comprehensively consider the multi-channel vibration signal. Therefore, this paper presents Refined Composite Multivariate Multiscale Fluctuation Dispersion Entropy (RCMMFDE), a method which extracts the recognition information of multi-channel signals with different scale factors, and the refined composite analysis ensures the recognition stability. The simulation results show that this method has the characteristics of low sensitivity to signal length and strong anti-noise ability. At the same time, combined with Joint Mutual Information Maximisation (JMIM) and support vector machine (SVM), RCMMFDE-JMIM-SVM fault diagnosis method has been proposed. This method uses RCMMFDE to extract the state characteristics of the multiple vibration signals of the rotary machine, and then uses the JMIM method to extract the sensitive characteristics. Finally, different states of the rotary machine are classified by SVM. The validity of the method is verified by the composite gear fault data set and bearing fault data set. The diagnostic accuracy of the method is 99.25% and 100.00%. The experimental results show that RCMMFDE-JMIM-SVM can effectively recognize multiple signals.

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