Evaluating algorithm performance metrics tailored for prognostics
Abhinav Saxena,Jose R. Celaya,Bhaskar Saha,Sankalita Saha,Kai Goebel +4 more
- 07 Mar 2009
- pp 1-13
TL;DR: This paper introduces several new evaluation metrics tailored for prognostics and shows that they can effectively evaluate various algorithms as compared to other conventional metrics.
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Abstract: Prognostics has taken center stage in Condition Based Maintenance (CBM) where it is desired to estimate Remaining Useful Life (RUL) of a system so that remedial measures may be taken in advance to avoid catastrophic events or unwanted downtimes. Validation of such predictions is an important but difficult proposition and a lack of appropriate evaluation methods renders prognostics meaningless. Evaluation methods currently used in the research community are not standardized and in many cases do not sufficiently assess key performance aspects expected out of a prognostics algorithm. In this paper we introduce several new evaluation metrics tailored for prognostics and show that they can effectively evaluate various algorithms as compared to other conventional metrics. Four prognostic algorithms, Relevance Vector Machine (RVM), Gaussian Process Regression (GPR), Artificial Neural Network (ANN), and Polynomial Regression (PR), are compared. These algorithms vary in complexity and their ability to manage uncertainty around predicted estimates. Results show that the new metrics rank these algorithms in a different manner; depending on the requirements and constraints suitable metrics may be chosen. Beyond these results, this paper offers ideas about how metrics suitable to prognostics may be designed so that the evaluation procedure can be standardized.
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Citations
Metrics for Offline Evaluation of Prognostic Performance
Abhinav Saxena,Jose R. Celaya,Bhaskar Saha,Sankalita Saha,Kai Goebel +4 more
- 22 Mar 2021
TL;DR: This paper presents several new evaluation metrics tailored for prognostics that were recently introduced and were shown to effectively evaluate various algorithms as compared to other conventional metrics.
472
An Adaptive Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-ion Batteries
Jie Liu,Abhinav Saxena,Kai Goebel,Bhaskar Saha,Wilson Wang +4 more
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TL;DR: The developed ARNN is constructed based on the adaptive/recurrent neural network architecture and the network weights are adaptively optimized using the recursive Levenberg-Marquardt (RLM) method.
Challenges to IoT-Enabled Predictive Maintenance for Industry 4.0
TL;DR: A comprehensive outlook of the current PdM issues is presented, with the final aim of providing a deeper understanding of the limitations and strengths, challenges and opportunities of this dynamic maintenance paradigm.
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Prognostics and health management for maintenance practitioners - Review, implementation and tools evaluation
Vepa Atamuradov,Kamal Medjaher,Pierre Dersin,Benjamin Lamoureux,Noureddine Zerhouni +4 more
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TL;DR: This paper attempts to provide a comprehensive review on PHM domain and discusses important issues on uncertainty quantification, implementation aspects next to prognostics feature and tool evaluation, and previous and on-going research in high-speed train bogies to highlight problems faced in train industry.
Lithium-ion battery remaining useful life estimation based on fusion nonlinear degradation AR model and RPF algorithm
TL;DR: Experimental results with the lithium-ion battery test data from NASA and CALCE show that the proposed fusion prognostic approach can effectively predict the battery RUL with more accurate forecasting result and uncertainty representation of probability density distribution.
215
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