Forecasting remaining useful life: Interpretable deep learning approach via variational Bayesian inferences

Open access
Date
2019-10Type
- Journal Article
Citations
Cited 48 times in
Web of Science
Cited 57 times in
Scopus
ETH Bibliography
yes
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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. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000352450Publication status
publishedExternal links
Journal / series
Decision Support SystemsVolume
Pages / Article No.
Publisher
ElsevierSubject
Forecasting; Remaining useful life; Machine learning; Neural networks; Deep learningOrganisational unit
09623 - Feuerriegel, Stefan (ehemalig) / Feuerriegel, Stefan (former)
Funding
183569 - Design and Evaluation of a Vehicle Hypoaglycemia Warning System in Diabetes (HEADWIND Project) (SNF)
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Show all metadata
Citations
Cited 48 times in
Web of Science
Cited 57 times in
Scopus
ETH Bibliography
yes
Altmetrics