Latest developments in gear defect diagnosis and prognosis: A review

被引：169

作者：

Kumar, Anil ^{[1
,2
]}

Gandhi, C. P. ^{[3
]}

Zhou, Yuqing ^{[1
]}

Kumar, Rajesh ^{[4
]}

Xiang, Jiawei ^{[1
]}

机构：

[1] Wenzhou Univ, Coll Mech & Elect Engn, Wenzhou 325035, Peoples R China

[2] Amity Univ Uttar Pradesh, Noida 201313, India

[3] Rayat Bahra Univ, Mohali 140104, India

[4] St Longowal Inst Engn & Technol, Longowal 148106, India

来源：

MEASUREMENT | 2020年 / 158卷

关键词：

Gear; Machine learning; Deep learning; Prognosis; Health indicator; Varying speed; REMAINING USEFUL LIFE; CONVOLUTIONAL NEURAL-NETWORK; INTELLIGENT FAULT-DIAGNOSIS; TURBINE PLANETARY GEARBOX; TIME-FREQUENCY ANALYSIS; SIGNAL ANALYSIS; STATISTICAL FEATURES; FEATURE-EXTRACTION; VIBRATION SIGNALS; VARYING-SPEED;

D O I：

10.1016/j.measurement.2020.107735

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Gears are an important component of industrial machinery and a breakdown of machinery on account of the failure of gears could result in immense production loss. Timely monitoring of machine health is always important. This has motivated the researchers in this field to develop methods to identify defects in gears. This paper provides an insight into various defects that generally occur in gears. In addition, a state-of-the-art review is provided on the latest and most widely used diagnosis methods for gearbox condition monitoring. Furthermore, the challenges faced in the area of gear defect diagnosis are discussed and a summary of various diagnostic methods is also provided. (C) 2020 Elsevier Ltd. All rights reserved.

引用

页数：19

共 159 条

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