Health indicator construction and remaining useful life estimation for mechanical systems using vibration signal prognostics

2021 ◽  
Author(s):  
Nikhil M. Thoppil ◽  
V. Vasu ◽  
C. S. P. Rao
Keyword(s):  
Mechanical Systems ◽  
Vibration Signal ◽  
Remaining Useful Life ◽  
Health Indicator ◽  
Life Estimation ◽  
Useful Life

An ensemble decision tree methodology for remaining useful life prediction of spur gears under natural pitting progression

2019 ◽  
Vol 19 (3) ◽  
pp. 854-872 ◽  
Author(s):  
Pradeep Kundu ◽  
Ashish K Darpe ◽  
Makarand S Kulkarni
Keyword(s):  
Random Forest ◽  
Correlation Coefficient ◽  
Life Prediction ◽  
Vibration Signal ◽  
Remaining Useful Life ◽  
Spur Gears ◽  
Health Indicator ◽  
Residual Vibration ◽  
Random Forest Regression ◽  
Useful Life

This article presents an ensemble decision tree–based random forest regression methodology for remaining useful life prediction of spur gears subjected to pitting failure mode. The random forest regression methodology does not require an elaborate statistics background knowledge and has an inbuilt health indicator selection capability compared to other existing data-driven remaining useful life prediction approaches. A correlation coefficient parameter based on the residual vibration signal is used for monitoring and detecting the pitting progression in spur gears. The effectiveness of the correlation coefficient of the residual vibration signal is assessed over the other existing health indicators for pitting fault progression. To show the inbuilt best health indicator selection capability of the random forest regression model, initially, eight indicators (existing seven and the correlation coefficient of the residual vibration signal) were used for model training. In addition, the effect of fusing the vibration sensor data from multiple positions on the gearbox on prediction accuracy of the random forest regression model is also evaluated. The accuracy in the remaining useful life prediction is found to increase after fusing the correlation coefficient of the residual vibration signal based health indicator derived from the accelerometers located at multiple positions on the gearbox in comparison to data from a single accelerometer. Furthermore, the accuracy of the proposed methodology is tested and proven using five accelerated run-to-failure experimental data collected from the specially built test rig.

Remaining useful life estimation: a review on stochastic process-based approaches

2020 ◽  
Vol 14 ◽  
Author(s):  
Dangbo Du ◽  
Jianxun Zhang ◽  
Xiaosheng Si ◽  
Changhua Hu
Keyword(s):  
Stochastic Process ◽  
Health Management ◽  
Remaining Useful Life ◽  
Future Trends ◽  
Prognostics And Health Management ◽  
Inverse Gaussian ◽  
Life Estimation ◽  
Inverse Gaussian Process ◽  
Pros And Cons ◽  
Useful Life

Background: Remaining useful life (RUL) estimation is the central mission to the complex systems’ prognostics and health management. During last decades, numbers of developments and applications of the RUL estimation have proliferated. Objective: As one of the most popular approaches, stochastic process-based approach has been widely used for characterizing the degradation trajectories and estimating RULs. This paper aimed at reviewing the latest methods and patents on this topic. Methods: The review is concentrated on four common stochastic processes for degradation modelling and RUL estimation, i.e., Gamma process, Wiener process, inverse Gaussian process and Markov chain. Results: After a briefly review of these four models, we pointed out the pros and cons of them, as well as the improvement direction of each method. Conclusion: For better implementation, the applications of these four approaches on maintenance and decision-making are systematically introduced. Finally, the possible future trends are concluded tentatively.

Sign in / Sign up

Export Citation Format

Share Document