停工期
预测性维护
信息物理系统
计算机科学
可靠性工程
可靠性(半导体)
预言
调度(生产过程)
过程(计算)
机器学习
人工智能
数据挖掘
工程类
功率(物理)
运营管理
物理
量子力学
操作系统
作者
Deepak Kumar Sharma,Shikha Brahmachari,Kartik Singhal,Deepak Gupta
标识
DOI:10.1016/j.cie.2022.108213
摘要
Cyber-physical systems (CPS) are an indispensable aspect of the modern age’s data driven industrial systems. These systems can be controlled and monitored with the help of computer-oriented devices and software that are responsible for integrating the physical environment with cyber frameworks. Owing to the nature of operations in any physical process industry, it becomes imperative to deal with potential failures before they occur. To avoid downtime and losses, predictive maintenance is one relevant policy that utilizes prior information and domain knowledge to help in scheduling operations and maintenance. Predictive maintenance (PdM) in industrial applications is known to improve the efficiency, lifetime, and reliability of the machines and thereby reducing the maintenance cost. With the advances in machine learning approaches in cyber physical systems, reliable predictions can be performed to significantly reduce downtime and operational losses associated with the physical processes. In this paper, usefulness of Temporal Convolutional Networks (TCNs) is investigated with the aim of forecasting the remaining useful life (RUL) for Turbofan engines. This paper demonstrates the effectiveness of using TCNs for prognosis under various evaluation conditions and also provides comparison of their performance with hybrid architectures like CNN-LSTM networks and meta-heuristically optimized LSTM networks. The proposed methods were able to achieve upto 94.47% accuracy in case of binary classification tasks and upto 98.7% precision in case of multi-label classification. The cumulative results in accordance to elaborated test cases are presented with the conclusion of the study.
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