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- W4381785695 abstract "With the continuous advancement of industry 4.0 and intelligent manufacturing, remaining useful lifetime (RUL) prediction can forecast the future degradation state of machinery and then estimate the remaining service time before it loses its safe operation ability. Accordingly, a series of predictive maintenance strategies can be regulated in advance for the equipment in the industrial Internet of things (IIOT). To tackle the challenges of insufficiency of failure data and lack of confidence level in the RUL prediction results, a similarity-based relevance vector machine (SRVM) is proposed in this article. Primarily, the relationship among latent variables in SRVM is learned adaptively through similarity computation to fully utilize the limited degradation data. Furthermore, these internal variables in SRVM are treated as time-varying variables and re-estimated dynamically to provide RUL prediction with reliable confidence. The experiment results show that the prediction accuracy of SRVM is higher than that of other baseline methods." @default.
- W4381785695 created "2023-06-24" @default.
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- W4381785695 date "2023-09-01" @default.
- W4381785695 modified "2023-10-09" @default.
- W4381785695 title "SRVM: A Similarity-based Relevance Vector Machine for Remaining Useful Lifetime Prediction in IIOT" @default.
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- W4381785695 doi "https://doi.org/10.1109/mis.2023.3289067" @default.
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