Noise-Aware Sparse Gaussian Processes and Application to Reliable Industrial Machinery Health Monitoring

被引：15

作者：

Yang, Jingyu ^{[1
,2
]}

Yue, Zuogong ^{[1
,2
]}

Yuan, Ye ^{[1
,2
]}

机构：

[1] Huazhong Univ Sci & Technol, Key Lab Image Proc & Intelligent Control, Minist Educ, Wuhan 430074, Peoples R China

[2] Huazhong Univ Sci & Technol, Sch Artificial Intelligence & Automation, Wuhan 430074, Peoples R China

来源：

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS | 2023年 / 19卷 / 04期

基金：

国家重点研发计划;

关键词：

Monitoring; Uncertainty; Training; Reliability; Machinery; Gaussian processes; Bayes methods; Explainable artificial intelligence; machinery health monitoring; reliability; sparse Gaussian processes; PROCESS REGRESSION; FAULT-DIAGNOSIS; NEURAL-NETWORKS; PROPAGATION; UNCERTAINTY;

D O I：

10.1109/TII.2022.3200428

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Maintenance of machinery equipment in smart manufacturing requires real-time health monitoring, strongly supported by the rapid evolution of Artificial Intelligence (AI) technologies. Most AI-based health monitoring systems are powered by advanced modeling methods and intensive high-quality monitoring data. Such monitoring systems center on high-accuracy predictive performance but cannot necessarily convey reliability, such as satisfactory resistance to strong noises, credible uncertainty analysis, and model interpretability. This article novelly proposes noise-aware sparse Gaussian processes (NASGP) within the Bayesian inference framework. NASGP are capable of consistent high-performance and credible uncertainty assessment under strong noises. Based on NASGP, we then develop an explainable generalized additive model to bridge the gap between latent inference mechanism and domain expert knowledge. The efficacy of the proposed approach is corroborated through two case studies including remaining useful life prognosis and fault diagnosis for rolling bearings.

引用

页码：5995 / 6005

页数：11

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