Stack Denoising Autoencoder and State-Space Model Based Bearing RUL Prediction Method
ID:4 View Protection:PUBLIC Updated Time:2022-12-15 11:23:19 Hits:205 Poster Presentation

Start Time:Pending (Asia/Shanghai)

Duration:Pending

Session:[No Session] [No Session Block]

Abstract
Rolling element bearing is a critical component in a machinery, so its remaining useful life (RUL) prediction becomes a research hotspot in recent years. In this work, a RUL prediction method based on stack denoising autoencoder (SDA) and non-overlapping sliding window (NOSW) threshold method is proposed. The health indicator is constructed by the SDA from 19 time-domain features, which balances the sensitivity and robustness of different features. A novel NOSW threshold method is used to identify the degradation initial time and divide the life cycle into normal operating stage and degradation stage. A state-space model based on the Paris-Erdogan model is established and its noise intensity is estimated by a smoothing estimation method. The particle filtering is employed to track the degradation path and quantify the uncertainty of RUL prediction.
Keywords
remaining useful life;stack denoising autoencoder;particle filter;Paris-Erdogan model;state-space model
Speaker
Lei Yang
Xi’an Jiaotong University

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Important Dates

15th August 2022 25th September 2022 - Manuscript Submission
15th October 2022 - Acceptance Notification
1st November 2022 - Camera Ready Submission
1st November 2022 10th November 2022Early Bird Registration

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