Evaluating time series encoding techniques for Predictive Maintenance

作者：

Highlights：

• We analyze Predictive Maintenance task aiming to minimize down-times of an equipment.

• We use time series encoding techniques with CNN-based models in different scenarios.

• An extensive evaluation has been performed for HDD and bearings failure prediction.

• Performance has been evaluated w.r.t. other benchmarking neural network models.

摘要

•We analyze Predictive Maintenance task aiming to minimize down-times of an equipment.•We use time series encoding techniques with CNN-based models in different scenarios.•An extensive evaluation has been performed for HDD and bearings failure prediction.•Performance has been evaluated w.r.t. other benchmarking neural network models.

论文关键词：Predictive maintenance,Time series Encoding techniques,Failure prediction,Deep learning

论文评审过程：Received 28 March 2022, Revised 3 August 2022, Accepted 4 August 2022, Available online 9 August 2022, Version of Record 17 August 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.118435