BEARING PERFORMANCE DEGRADATION ASSESSMENT BY ORTHOGONAL LOCAL PRESERVING PROJECTION AND CONTINUOUS HIDDEN MARKOV MODEL

2016 ◽  
Vol 40 (5) ◽  
pp. 1019-1030
Author(s):  
Tao Liu ◽  
Xing Wu ◽  
Yu Guo ◽  
Chang Liu
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Frequency Domain ◽  
Hidden Markov ◽  
Performance Degradation ◽  
Time Frequency ◽  
Rotating Machine ◽  
Accelerated Life ◽  
The Time Domain ◽  
Degradation Degree

Bearing is the key component in rotating machine. It is important to assess the performance degradation degree of bearings for making proactive maintenance and realizing near-zero downtime. A methodology based on orthogonal local preserving projection (OLPP) and continuous hidden Markov model (CHMM) is introduced in bearing performance degradation assessment. Firstly, the time domain, frequency domain and time-frequency domain features are extracted from the vibration signals. Then, the multi-dimensional features are reduced by OLPP. And the selection of the adjacent paragraph parameters in OLPP is optimized adaptively by minimizing the ratio of between-class distance to within-class distance. A CHMM is trained by using the reduced feature in normal condition. At last, the test bearing data are input into the pre-trained CHMM to calculate the log-likelihood of the test data, which can assess the performance degradation of bearings quantitatively. A bearing accelerated life experiment is performed to validate the feasibility and validity of the proposed method.

A model based on hidden graphic evaluation and review technique network to evaluate reliability and lifetime of multi-state systems

2018 ◽  
Vol 233 (3) ◽  
pp. 369-378 ◽  
Author(s):  
Wenjie Dong ◽  
Sifeng Liu ◽  
Zhigeng Fang ◽  
Yingsai Cao ◽  
Ye Ding
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Network Model ◽  
State Transition ◽  
Likelihood Function ◽  
Hidden Markov ◽  
Reliability Assessment ◽  
Assessment Method ◽  
Performance Degradation ◽  
State System

The essence of multi-state system performance degradation is a process of deteriorating state transition. On the basis of hidden Markov model and graphic evaluation and review technique network, this article proposes a new reliability assessment method called hidden graphic evaluation and review technique network model for multi-state system. Specifically, nodes in graphic evaluation and review technique network represent hidden states of a system at different deteriorating times, and they can be expanded through a series of observable sequences. Baum–Welch algorithm in hidden Markov model is introduced to train parameters and when logarithmic likelihood function of the output reaches convergent, we can estimate the most probable output state and obtain the state transition probability eventually. Suppose performance degradation amount between different nodes follows gamma distribution, a method of improved maximum likelihood function is introduced to estimate parameters. According to signal flow graph theory and Mason formula, equivalent transfer function from the initial node to any other nodes can be obtained, then expectation and variance of performance degradation amount can be presented. In the real case study, we compare the reliability assessment method proposed in this article with other two traditional methods, which show the rationality of hidden graphic evaluation and review technique network model.

Bearing performance degradation assessment based on topological representation and hidden Markov model

2020 ◽  
pp. 107754632094663
Author(s):  
Ran Wang ◽  
Jihao Jin ◽  
Xiong Hu ◽  
Jin Chen
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Performance Indicator ◽  
Hidden Markov ◽  
General Structure ◽  
Feature Space ◽  
Performance Degradation ◽  
Assessment Model ◽  
Self Organizing Map ◽  
Topological Representation

Bearing performance degradation assessment is essential to avoid abrupt machinery breakdown. However, background noise, outliers, and other interferences in the monitoring data may restrict the accuracy and stability of bearing performance degradation assessment in practical applications. In this study, a bearing performance degradation assessment method based on the topological representation and hidden Markov model is proposed. To construct a robust and representative feature space, the topological representations, specifically, topological meshes of the original features are obtained by self-organizing map, which can represent the general structure of the original feature space and eliminate outliers and other interferences. Then, the weight vectors of topological meshes are used as degradation features. Finally, the hidden Markov model is adopted as the assessment model to evaluate the bearing performance degradation tendency and detect the initial degradation effectively. To validate the effectiveness and superiority of the proposed method, two experimental datasets are analyzed. Compared with peer methods, the performance indicator curve of the proposed method presents a more smooth and accurate degradation tendency than comparative methods. Moreover, initial degradation can be identified accurately.

Monitoring of bearing fatigue life based on hidden Markov model

2021 ◽  
pp. 1-10
Author(s):  
Jie Hu ◽  
Sier Deng
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Probability Model ◽  
Degradation Process ◽  
Performance Degradation ◽  
Production Performance ◽  
Statistical Probability ◽  
State Duration ◽  
Intelligent Methods

With the increase in the intelligence of the production process and the increase in reliability requirements, the monitoring of the bearing life status after the event has been unable to meet the needs of industrial production. Performance degradation assessment and life monitoring have attracted more attention as intelligent methods based on condition maintenance. Hidden Markov model is a statistical probability model based on time series, which is very suitable for modeling the performance degradation process of equipment. Therefore, this paper proposes a life monitoring algorithm based on hidden Markov model. First, the continuous wavelet transform is introduced to obtain the optimal value of the shape factor or the stretch factor. Secondly, a hidden Markov model of multi-channel information fusion is proposed. The algorithm significantly improves the effectiveness and robustness of life monitoring. The hidden Markov model explicitly expresses the state duration distribution, making the model more suitable for life monitoring.

scholarly journals Fault Diagnosis and Prognosis of Bearing Based on Hidden Markov Model with Multi-Features

2020 ◽  
Vol 5 (1) ◽  
pp. 71-84 ◽  
Author(s):  
Weiguo Zhao ◽  
Tiancong Shi ◽  
Liying Wang
Keyword(s):  
Fault Diagnosis ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Wavelet Packet ◽  
Diagnosis And Prognosis ◽  
Pca Method ◽  
The Time Domain ◽  
Low Dimensional ◽  
Fault Diagnosis And Prognosis

AbstractA new approach to achieve fault diagnosis and prognosis of bearing based on hidden Markov model (HMM) with multi-features is proposed. Firstly, the time domain, frequency domain, and wavelet packet decomposition are utilized to extract the condition features of bearing vibration signals, and the PCA method is merged into multi-features to reduce their dimensionality. Then the low-dimensional features are processed to obtain the scalar probabilities of each bearing condition, which are multiplied to generate the observed values of HMM. The results reveal that the established approach can well diagnose fault conditions and achieve the remaining life estimation of bearing.

Real Time Monitoring of Cutting Chatter Based on Fuzzy Hidden Markov Models

2006 ◽  
Vol 532-533 ◽  
pp. 1160-1163
Author(s):  
Chun Liang Zhang ◽  
Li Ping Chen
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Real Time ◽  
Metal Cutting ◽  
Hidden Markov ◽  
Cnc Machine ◽  
Real Time Monitoring ◽  
Time Frequency ◽  
Cutting Chatter ◽  
Cutting Processes

The full automation of machine tools has gained substantial importance in manufacturing industries in recent years, as machining technology has progressed from manually operated production machines to highly advanced and sophisticated CNC machine tool. Whereas manufacturing technology has moved to the stage of automation, there is still an unsolved problem in metal cutting processes: cutting chatter. Due to its complexity, thus cutting chatter is still the primary problem in metal cutting processes. According to the characteristic of cutting chatter, a real time monitoring technique of cutting chatter based on fuzzy hidden Markov model (FHMM) was presented. Hidden Markov model (HMM) is a state-of-the-art technique for speech recognition because of its elegant mathematical structure and the availability of computer implementation of these models. In this paper, the fuzzy EM algorithm was used to the Baum-Welch algorithm in the HMM method, and the strategy of time frequency feature extraction to non-stability signal was described. The experimental results show that the proposed method is feasible and effective for the monitoring of cutting chatter in the metal cutting processes.

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