IEEE Transactions on Industrial Informatics· 2017Q1
RUL Prediction of Deteriorating Products Using an Adaptive Wiener Process Model
- 358citations
- Q1SCImago
- 2017year
Short summary
A novel adaptive Wiener process model using Brownian motion for adaptive drift improves Remaining Useful Life (RUL) prediction accuracy and simplifies model estimation compared to existing autoregressive models.
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Key points
- Proposes a new adaptive Wiener process model for Remaining Useful Life (RUL) prediction.
- Utilizes Brownian motion for the adaptive drift, improving upon autoregressive models.
- Simplifies model estimation and RUL prediction processes.
- Validated using simulations and lithium-ion battery degradation data.
AI-generated from the title and abstract; the full text is not read.
Abstract
Degradation modeling plays an important role in system health diagnosis and remaining useful life (RUL) prediction. Recently, a class of Wiener process models with adaptive drift was proposed for degradation-based RUL prediction, which has been proven flexible and effective. However, the existing studies use an autoregressive model of order 1 for the adaptive drift, which can result in difficulties in both model estimation and RUL prediction. This paper proposes a new adaptive Wiener process model that utilizes a Brownian motion for the adaptive drift. The new model shares the flexibility of the existing models, but avoids the difficulties in model estimation and RUL prediction. A model estimation procedure based on maximum likelihood estimation is developed, and the RUL prediction based on the proposed model is formulated. The effectiveness of the model in RUL prediction is validated using simulation and through an application to the lithium-ion battery degradation data.
The authors' abstract, as published at the source. IEEE Transactions on Industrial Informatics, 2017 · DOI ↗
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Field: Safety, Risk, Reliability and Quality
Safety, Risk, Reliability and QualityEngineering