Design methodology of a reduced-scale test bench for fault detection and diagnosis

Mechatronics ◽  
2017 ◽  
Vol 47 ◽  
pp. 14-23 ◽  
Author(s):  
E. Esteban ◽  
O. Salgado ◽  
A. Iturrospe ◽  
I. Isasa
Keyword(s):  
Fault Detection ◽  
Design Methodology ◽  
Test Bench ◽  
Fault Detection And Diagnosis ◽  
Scale Test ◽  
Detection And Diagnosis ◽  
Reduced Scale

scholarly journals Generative Oversampling Method for Imbalanced Data on Bearing Fault Detection and Diagnosis

2019 ◽  
Vol 9 (4) ◽  
pp. 746 ◽  
Author(s):  
Sungho Suh ◽  
Haebom Lee ◽  
Jun Jo ◽  
Paul Lukowicz ◽  
Yong Lee
Keyword(s):  
Fault Detection ◽  
Test Bench ◽  
Imbalanced Data ◽  
Fault Detection And Diagnosis ◽  
Data Driven ◽  
Image Transformation ◽  
Vibration Signals ◽  
Bearing Fault ◽  
Transformation Procedure ◽  
Detection And Diagnosis

In this study, we developed a novel data-driven fault detection and diagnosis (FDD) method for bearing faults in induction motors where the fault condition data are imbalanced. First, we propose a bearing fault detector based on convolutional neural networks (CNN), in which the vibration signals from a test bench are used as inputs after an image transformation procedure. Experimental results demonstrate that the proposed classifier for FDD performs well (accuracy of 88% to 99%) even when the volume of normal and fault condition data is imbalanced (imbalance ratio varies from 20:1 to 200:1). Additionally, our generative model reduces the level of data imbalance by oversampling. The results improve the accuracy of FDD (by up to 99%) when a severe imbalance ratio (200:1) is assumed.

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