Paper Info
Title
FaultFace: Deep Convolutional Generative Adversarial Network (DCGAN) based Ball-Bearing failure detection method
Abstract
Failure detection is employed in the industry to improve system performance and reduce costs due to unexpected malfunction events. So, a good dataset of the system is desirable for designing an automated failure detection system. However, industrial process datasets are unbalanced and contain little information about failure behavior due to the uniqueness of these events and the high cost for running the system just to get information about the undesired behaviors. For this reason, performing correct training and validation of automated failure detection methods is challenging. This paper proposes a methodology called FaultFace for failure detection on Ball-Bearing joints for rotational shafts using deep learning techniques to create balanced datasets. The FaultFace methodology uses 2D representations of vibration signals denominated faceportraits obtained by time–frequency transformation techniques. From the obtained faceportraits, a Deep Convolutional Generative Adversarial Network is employed to produce new faceportraits of the nominal and failure behaviors to get a balanced dataset. A Convolutional Neural Network is trained for fault detection employing the balanced dataset. The FaultFace methodology is compared with other deep learning techniques to evaluate its performance in for fault detection with unbalanced datasets. Obtained results show that FaultFace methodology has a good performance for failure detection for unbalanced datasets.
Year
DOI
Venue
2021
10.1016/j.ins.2020.06.060
Information Sciences
Keywords
DocType
Volume
DCGAN networks,FaultFace,CNN,Failure detection,Deep Learning
Journal
542
ISSN
Citations 
PageRank 
0020-0255
3
0.44
References 
Authors
0
3
Name
Order
Citations
PageRank
julio viola141.81
Yangquan Chen22257242.16
Jing Wang33610.33