期刊
INFORMATION SCIENCES
卷 542, 期 -, 页码 195-211出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2020.06.060
关键词
DCGAN networks; FaultFace; CNN; Failure detection; Deep Learning
Failure detection is crucial in industries for improving system efficiency and cost reduction, but due to unbalanced datasets and limited failure behavior information, training and validating automated failure detection methods is challenging. The FaultFace methodology uses deep learning techniques to create balanced datasets and shows good performance in detecting failures.
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. (C) 2020 Elsevier Inc. All rights reserved.
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