Time varying and condition adaptive hidden Markov model for tool wear state estimation and remaining useful life prediction in micro-milling

2019 ◽  
Vol 131 ◽  
pp. 689-702 ◽  
Author(s):  
Weijian Li ◽  
Tongshun Liu
Keyword(s):  
Tool Wear ◽  
Markov Model ◽  
Hidden Markov Model ◽  
State Estimation ◽  
Life Prediction ◽  
Hidden Markov ◽  
Remaining Useful Life ◽  
Micro Milling ◽  
Time Varying ◽  
Useful Life

Remaining Useful Life Estimation Based on a Segmental Hidden Markov Model With Continuous Observations

2017 ◽  
Author(s):  
Zhen Chen ◽  
Tangbin Xia ◽  
Ershun Pan
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Goodness Of Fit ◽  
Hidden Markov ◽  
Remaining Useful Life ◽  
Likelihood Method ◽  
Distribution Model ◽  
Unknown Parameters ◽  
Useful Life ◽  
Hidden States

In this paper, a segmental hidden Markov model (SHMM) with continuous observations, is developed to tackle the problem of remaining useful life (RUL) estimation. The proposed approach has the advantage of predicting the RUL and detecting the degradation states simultaneously. As the observation space is discretized into N segments corresponding to N hidden states, the explicit relationship between actual degradation paths and the hidden states can be depicted. The continuous observations are fitted by Gaussian, Gamma and Lognormal distribution, respectively. To select a more suitable distribution, model validation metrics are employed for evaluating the goodness-of-fit of the available models to the observed data. The unknown parameters of the SHMM can be estimated by the maximum likelihood method with the complete data. Then a recursive method is used for RUL estimation. Finally, an illustrate case is analyzed to demonstrate the accuracy and efficiency of the proposed method. The result also suggests that SHMM with observation probability distribution which is closer to the real data behavior may be more suitable for the prediction of RUL.

scholarly journals Precision degradation prediction of inertial test turntable based on Hidden Markov Model and optimized particle filtering

2020 ◽  
Vol 12 (12) ◽  
pp. 168781402097249
Author(s):  
Liming Li ◽  
Xunyi Zhou ◽  
Xingqi Zhang ◽  
Zhenghu Zhong
Keyword(s):  
Markov Model ◽  
Prediction Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Vibration Signal ◽  
Remaining Useful Life ◽  
Health State ◽  
Starting Point ◽  
Useful Life ◽  
Position Precision

In order to solve the problem that there is no effective evaluation method for the precision degradation state of inertial test turntable, a prediction model for the position precision degradation trend of test turntable was proposed based on the Hidden Markov Model (HMM) algorithm and Particle Filter (PF) algorithm. The initial parameter of the PF algorithm was optimized by the Particle Swarm Optimization (PSO) algorithm. The vibration signal was selected as the research data, which could be obtained from an velocity test of turntable precision degradation. Firstly, the original vibration signal was denoised by Ensemble Empirical Mode Decomposition and Principal Component Analysis (EEMD-PCA) algorithm, and the signal with fault characteristic was extracted for signal reconstruction; Secondly, a HMM model could be trained by using the statistical characteristic values as observation matrix, and the diagnosis of early position precision degradation and the health state indexes could be obtained. Finally, a prediction model of the test turntable precision degradation could be established by using PF algorithm, and the Remaining Useful Life (RUL) of the test turntable precision could be calculated. When the 50th group data were taken as the prediction starting point, the predicted remaining useful life was 21 years, and the actual measured result was 17 years, which are close to each other. Comparing the model calculation results and the test measurement results, it is shown that the model could effectively and accurately predict the change trend and remaining useful life of the test turntable precision.

scholarly journals Semi-supervised Constrained Hidden Markov Model Using Multiple Sensors for Remaining Useful Life Prediction and Optimal Predictive Maintenance

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Xinyu Zhao ◽  
Yunyi Kang ◽  
Hao Yan ◽  
Feng Ju
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Remaining Useful Life ◽  
Predictive Maintenance ◽  
Multiple Sensors ◽  
Degradation Data ◽  
State Identification ◽  
Markov Decision ◽  
Useful Life

Remaining Useful Life (RUL) estimation is critical in many engineering systems where proper predictive maintenance is needed to increase a unit's effectiveness and reduce time and cost of repairing. Typically for such systems, multiple sensors are normally used to monitor performance, which create difficulties for system state identification. In this paper, we develop a semi-supervised left-to-right constrained Hidden Markov Model (HMM) model, which is effective in estimating the RUL, while capturing the jumps among states in condition dynamics. In addition, based on the HMM model learned from multiple sensors, we build a Partial Observable Markov Decision Process (POMDP) to demonstrate how such RUL estimation can be effectively used for optimal preventative maintenance decision making. We apply this technique to the NASA Engine degradation data and demonstrate the effectiveness of the proposed method.

Tool wear and remaining useful life prediction in micro-milling along complex tool paths using neural networks

2021 ◽  
Vol 71 ◽  
pp. 679-698
Author(s):  
Sumant Bagri ◽  
Ashish Manwar ◽  
Alwin Varghese ◽  
Soham Mujumdar ◽  
Suhas S. Joshi
Keyword(s):  
Neural Networks ◽  
Tool Wear ◽  
Life Prediction ◽  
Remaining Useful Life ◽  
Micro Milling ◽  
Tool Paths ◽  
Useful Life

scholarly journals Prognostics uncertainty reduction by right-time prediction of remaining useful life based on hidden Markov model and proportional hazard model

2021 ◽  
Vol 23 (1) ◽  
pp. 154-165
Author(s):  
Gao Zhiyong ◽  
Li Jiwu ◽  
Wang Rongxi
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Survival Function ◽  
Remaining Useful Life ◽  
Proportional Hazard ◽  
Proportional Hazard Model ◽  
Time Prediction ◽  
Useful Life ◽  
Degradation State

Uncertainty is a key problem in remaining useful life (RUL) prediction, and measures to reduce uncertainty are necessary to make RUL prediction truly practical. In this paper, a right-time prediction method is proposed to reduce the prognostics uncertainty of mechanical systems under unobservable degradation. Correspondingly, the whole RUL prediction process is divided into three parts, including offline modelling, online state estimating and online life predicting. In the offline modelling part, hidden Markov model (HMM) and proportional hazard model (PHM) are built to map the whole degradation path. During operation, the degradation state of the object is estimated in real time. Once the last degradation state reached, the degradation characteristics are extracted, and the survival function is obtained with the fitted PHM. The proposed method is demonstrated on an engine dataset and shows higher accuracy than traditional method. By fusing the extracted degradation characteristics, the obtained survival function can be basis for optimal maintenance with lower uncertainty.

Sign in / Sign up

Export Citation Format

Share Document