Challenges and Opportunities of AI-Enabled Monitoring, Diagnosis & Prognosis: A Review

被引：94

作者：

Zhao, Zhibin ^{[1
]}

Wu, Jingyao ^{[1
]}

Li, Tianfu ^{[1
]}

Sun, Chuang ^{[1
]}

Yan, Ruqiang ^{[1
]}

Chen, Xuefeng ^{[1
]}

机构：

[1] Xi An Jiao Tong Univ, Sch Mech Engn, State Key Lab Mfg Syst Engn, Xian 710049, Peoples R China

来源：

CHINESE JOURNAL OF MECHANICAL ENGINEERING | 2021年 / 34卷 / 01期

基金：

中国国家自然科学基金;

关键词：

Monitoring; Diagnosis; Prognosis; PHM; Artificial intelligence; Deep learning; REMAINING USEFUL LIFE; CONVOLUTIONAL NEURAL-NETWORK; INTELLIGENT FAULT-DIAGNOSIS; PERFORMANCE DEGRADATION ASSESSMENT; ROLLING ELEMENT BEARINGS; DEEP BELIEF NETWORK; ANOMALY DETECTION APPROACH; ROTATING MACHINERY; LEARNING-METHOD; MODE DECOMPOSITION;

D O I：

10.1186/s10033-021-00570-7

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 ;

摘要：

Prognostics and Health Management (PHM), including monitoring, diagnosis, prognosis, and health management, occupies an increasingly important position in reducing costly breakdowns and avoiding catastrophic accidents in modern industry. With the development of artificial intelligence (AI), especially deep learning (DL) approaches, the application of AI-enabled methods to monitor, diagnose and predict potential equipment malfunctions has gone through tremendous progress with verified success in both academia and industry. However, there is still a gap to cover monitoring, diagnosis, and prognosis based on AI-enabled methods, simultaneously, and the importance of an open source community, including open source datasets and codes, has not been fully emphasized. To fill this gap, this paper provides a systematic overview of the current development, common technologies, open source datasets, codes, and challenges of AI-enabled PHM methods from three aspects of monitoring, diagnosis, and prognosis.

引用

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