Forecasting remaining useful life: Interpretable deep learning approach via variational Bayesian inferences
Kraus M, Feuerriegel S (2019)
Publication Type: Journal article
Publication year: 2019
Journal
Book Volume: 125
Article Number: 113100
DOI: 10.1016/j.dss.2019.113100
Abstract
Predicting the remaining useful life of machinery, infrastructure, or other equipment can facilitate preemptive maintenance decisions, whereby a failure is prevented through timely repair or replacement. This allows for a better decision support by considering the anticipated time-to-failure and thus promises to reduce costs. Here a common baseline may be derived by fitting a probability density function to past lifetimes and then utilizing the (conditional) expected remaining useful life as a prognostic. This approach finds widespread use in practice because of its high explanatory power. A more accurate alternative is promised by machine learning, where forecasts incorporate deterioration processes and environmental variables through sensor data. However, machine learning largely functions as a black-box method and its forecasts thus forfeit most of the desired interpretability. As our primary contribution, we propose a structured-effect neural network for predicting the remaining useful life which combines the favorable properties of both approaches: its key innovation is that it offers both a high accountability and the flexibility of deep learning. The parameters are estimated via variational Bayesian inferences. The different approaches are compared based on the actual time-to-failure for aircraft engines. This demonstrates the performance and superior interpretability of our method, while we finally discuss implications for decision support.
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Switzerland (CH)How to cite
APA:
Kraus, M., & Feuerriegel, S. (2019). Forecasting remaining useful life: Interpretable deep learning approach via variational Bayesian inferences. Decision Support Systems, 125. https://dx.doi.org/10.1016/j.dss.2019.113100
MLA:
Kraus, Mathias, and Stefan Feuerriegel. "Forecasting remaining useful life: Interpretable deep learning approach via variational Bayesian inferences." Decision Support Systems 125 (2019).
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