Multisensory data fusion-based deep learning approach for fault diagnosis of an industrial autonomous transfer vehicle

作者：

Highlights：

• A deep learning-based data-driven operational fault diagnosis model is proposed.

• The model utilizing multisensory data fusion is applied to ATV use-case.

• A new ATV testbed and dataset collected via multiple sensors are presented.

• The results obtained from single, dual and multiple sensor models are compared.

摘要

•A deep learning-based data-driven operational fault diagnosis model is proposed.•The model utilizing multisensory data fusion is applied to ATV use-case.•A new ATV testbed and dataset collected via multiple sensors are presented.•The results obtained from single, dual and multiple sensor models are compared.

论文关键词：Autonomous Transfer Vehicle,Condition Monitoring,Deep Learning,Sensor Fusion,Short Time Fourier Transform

论文评审过程：Received 3 June 2021, Revised 17 February 2022, Accepted 28 March 2022, Available online 4 April 2022, Version of Record 5 April 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.117055