A data-driven approach to diagnostics of repetitive processes in the distribution domain - Applications to gearbox diagnostics in industrial robots and rotating machines

被引：25

作者：

Bittencourt, Andre Carvalho ^{[1
]}

Saarinen, Kari ^{[2
]}

Sander-Tavallaey, Shiva ^{[2
]}

Gunnarsson, Svante ^{[1
]}

Norrlof, Mikael ^{[1
,3
]}

机构：

[1] Linkoping Univ, Dept Elect Engn, S-58183 Linkoping, Sweden

[2] ABB Corp Res, Vasteras, Sweden

[3] ABB Robot, Vasteras, Sweden

来源：

MECHATRONICS | 2014年 / 24卷 / 08期

关键词：

Repetitive processes; Diagnostics; Fault detection and isolation; Wear; Vibration analysis; Gearbox; FAULT-DETECTION; STATE ESTIMATION; MANIPULATORS;

D O I：

10.1016/j.mechatronics.2014.01.013

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper presents a data-driven approach to diagnostics of systems that operate in a repetitive manner. Considering that data batches collected from a repetitive operation will be similar unless in the presence of an abnormality, a condition change is inferred by comparing the monitored data against an available nominal batch. The method proposed considers the comparison of data in the distribution domain, which reveals information of the data amplitude. This is achieved with the use of kernel density estimates and the Kullback-Leibler distance. To decrease sensitivity to disturbances while increasing sensitivity to faults, the use of a weighting vector is suggested which is chosen based on a labeled dataset. The framework is simple to implement and can be used without process interruption, in a batch manner. The approach is demonstrated with successful experimental and simulation applications to wear diagnostics in an industrial robot gearbox and for diagnostics of gear faults in a rotating machine. (C) 2014 Elsevier Ltd. All rights reserved.

引用

页码：1032 / 1041

页数：10

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