Stability-based system for bearing fault early detection

被引：16

作者：

Diaz, Moises ^{[1
]}

Henriquez, Patricia ^{[1
]}

Ferrer, Miguel A. ^{[1
]}

Pirlo, Giuseppe ^{[2
]}

Alonso, Jesus B. ^{[1
]}

Carmona-Duarte, Cristina ^{[1
]}

Impedovo, Donato ^{[2
]}

机构：

[1] Univ Las Palmas Gran Canaria, Inst Univ Desarrollo Tecnol & Innovac Comunicac, Las Palmas Gran Canaria 35017, Spain

[2] Univ Bari, Dipartimento Informat, I-70126 Bari, Italy

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 2017年 / 79卷

关键词：

Bearing fault detection; Direct matching points; Dynamic time warping; Stability analysis; INDUCTION MACHINES; ROTATING MACHINERY; WAVELET TRANSFORM; WORD RECOGNITION; ENERGY OPERATOR; STATOR CURRENT; DIAGNOSIS; VIBRATION; VERIFICATION; SIGNALS;

D O I：

10.1016/j.eswa.2017.02.030

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents a new and straightforward system for bearing fault detection. The system computes the stability of two vibration signals by using the direct matching points (DMP) of an elastic and nonlinear align function. It is able to find discriminant properties in the stability of fault-free and faulty bearing vibration signals from the early and late stages of the fault in critical bearing parts. Because training data constitutes one of the critical challenges in most expert and intelligent systems, one of the novelties of the proposed stability-based system is that it requires neither training nor fine-tuning. A significant impact on the robustness of the system is demonstrated using two publicly available vibration signal databases under several load conditions, with real faults, during multiple machine working states. Experimental results validate the use of the proposed stability-based system for predictive maintenance in bearings. (C) 2017 Elsevier Ltd. All rights reserved.

引用

页码：65 / 75

页数：11

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