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Multi-Dimensional Self Attention based Approach for Remaining Useful Life Estimation by Lai, Zhi; Liu, Mengjuan; Pan, Yunzhu; Chen, Dajiang is a scholarly article available to read on EtoBox.

What is Multi-Dimensional Self Attention based Approach for Remaining Useful Life Estimation about?

Remaining Useful Life (RUL) estimation plays a critical role in Prognostics and Health Management (PHM). Traditional machine health maintenance systems are often costly, requiring sufficient prior expertise, and are difficult to fit into highly complex and changing industrial scenarios. With the widespread deployment of sensors on industrial equipment, building the Industrial Internet of Things (IIoT) to interconnect these devices has become an inexorable trend in the development of the digital factory. Using the device's real-time operational data collected by IIoT to get the estimated RUL through the RUL prediction algorithm, the PHM system can develop proactive maintenance measures for the device, thus, reducing maintenance costs and decreasing failure times during operation. This paper carries out research into the remaining useful life prediction model for multi-sensor devices in the IIoT scenario. We investigated the mainstream RUL prediction models and summarized the basic steps of RUL prediction modeling in this scenario. On this basis, a data-driven approach for RUL estimation is proposed in this paper. It employs a Multi-Head Attention Mechanism to fuse the multi-dimensio

Author

Lai, Zhi; Liu, Mengjuan; Pan, Yunzhu; Chen, Dajiang

Published

2022

Language

EN