Development and Application of a Method for Real Time Motor Fault Detection

被引：9

作者：

Joung, Byung Gun ^{[1
]}

Lee, Wo Jae ^{[1
]}

Huang, Aihua ^{[1
]}

Sutherland, John. W. ^{[1
]}

机构：

[1] Purdue Univ, Envrionm & Ecol Engn, 610 Purdue Mall, W Lafayette, IN 47907 USA

来源：

PROCEEDINGS OF THE 8TH INTERNATIONAL CONFERENCE ON THROUGH-LIFE ENGINEERING SERVICES (TESCONF 2019) | 2020年 / 49卷

关键词：

Condition Monitoring; Smart Manufacturing; Predictive Maintenance (PdM); Artificial Intelligence (AI); Principal Component Analysis (PCA); PRINCIPAL-COMPONENT ANALYSIS; MACHINE; DIAGNOSIS;

D O I：

10.1016/j.promfg.2020.07.002

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Predictive maintenance (PdM) has been widely used in manufacturing to reduce maintenance cost and unexpected downtime. A common element within manufacturing equipment/machines is a motor. This paper aims to detect motor faults by collecting and analyzing vibration data with wireless sensors. A cloud-based motor condition monitoring system is also built to detect motor faults by analyzing the data. An Artificial Intelligence (AI) model is trained using the collected vibration data, and principal component analysis (PCA) is utilized to detect abnormal behaviors of the motor. Hostelling's T-2 statistics and squared prediction error (SPE) statistics are then applied to clarify criterions for abnormal operations of the motor. (C) 2020 The Authors. Published by Elsevier B.V.

引用

页码：94 / 98

页数：5

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