Version 1
: Received: 29 June 2021 / Approved: 1 July 2021 / Online: 1 July 2021 (22:38:28 CEST)
How to cite:
Yu, W.; Li, X.; Sun, Y. Towards Making Predictive Maintenance System Adaptive and Interpretable. Preprints2021, 2021070040. https://doi.org/10.20944/preprints202107.0040.v1
Yu, W.; Li, X.; Sun, Y. Towards Making Predictive Maintenance System Adaptive and Interpretable. Preprints 2021, 2021070040. https://doi.org/10.20944/preprints202107.0040.v1
Yu, W.; Li, X.; Sun, Y. Towards Making Predictive Maintenance System Adaptive and Interpretable. Preprints2021, 2021070040. https://doi.org/10.20944/preprints202107.0040.v1
APA Style
Yu, W., Li, X., & Sun, Y. (2021). Towards Making Predictive Maintenance System Adaptive and Interpretable. Preprints. https://doi.org/10.20944/preprints202107.0040.v1
Chicago/Turabian Style
Yu, W., Xuejiao Li and Yong Sun. 2021 "Towards Making Predictive Maintenance System Adaptive and Interpretable" Preprints. https://doi.org/10.20944/preprints202107.0040.v1
Abstract
Using data-driven models to solve predictive maintenance problems has been prevalent for original equipment manufacturers (OEMs). However, such models fail to solve two tasks that OEMs are interested in: (1) Making the well-built failure prediction models working on existing scenarios (vehicles, working conditions) adaptive to target scenarios. (2) Finding out the failure causes, furthermore, determining whether a model generates failure predictions based on reasonable causes. This paper investigates a comprehensive architecture towards making the predictive maintenance system adaptive and interpretable by proposing (1) an ensemble model dealing with time-series data consisting of a long short-term memory (LSTM) neural network and Gaussian threshold to achieve failure prediction one week in advance and (2) an online transfer learning algorithm and a meta learning algorithm, which render existing models adaptive to new vehicles with limited data volumes. (3) Furthermore, the Local Interpretable Model-agnostic Explanations (LIME) interpretation tool and super-feature methods are applied to interpret individual and general failure causes. Vehicle data from Isuzu Motors, Ltd., are adopted to validate our method, which include time-series data and histogram data. The proposed ensemble model yields predictions with 100% accuracy for our test data on engine stalling problem and is more rapidly adaptive to new vehicles with smaller error following application of either online transfer learning or the meta learning method. The interpretation methods help elucidate the global and individual failure causes, confirming the model credibility.
Keywords
predictive maintenance; transfer learning; interpretable machine learning
Subject
Engineering, Industrial and Manufacturing Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
