Fault Diagnosis of Bearings With the Common-Domain Data

Abstract

Rolling element bearings are one of the important components in rotating machines. Therefore, many studies on bearing diagnosis have been conducted with artificial intelligence (AI) to do maintenance on the machines on time. In general, AI successfully diagnoses the defects of bearing when it is trained with the sufficient data of a specific machine, but it hardly provides reasonable results when it is untrained or insufficiently trained. However, it is hard to obtain sufficient data even for a specific machine in practice. In this paper, a new method was developed to increase training data by transferring the cross-domain data into the common-domain data. Therefore, all the data from different kinds of machines with various bearings can be combined as a big training data. Bearings under consideration in this paper have different specifications and characteristics. In transferring into the common-domain, it is important to get rid of structural and environmental noise by signal processing, which makes it plausible to extract common features. With the common-domain data, one-dimensional convolutional neural network (1D-CNN) with feature domain adaptation is applied and successfully classifies the defects of each bearing. Moreover, 1D-CNN combined with support vector machine (SVM) can also classify defects successfully without feature domain adaptation, which makes it possible to train the model only with normal data of the machine in concern. To verify the proposed method, not only the bearing data from Case Western Reserve University and Paderborn University but also the bearing data with flow noise of Ajou University are used.INDEX TERMS Bearing fault diagnosis, common-domain data, convolutional neural network, cross-domain fault diagnosis, domain adaptation, signal processing, support vector machine.

Fault diagnosis of gas turbine generator bearings using enhanced valuable sample strategy and convolutional neural network

“…In order to fully learn the characteristics of the defect data and to uncover the hidden non-linear relationships within the dataset, they achieved this goal through the construction of neural networks. deep convolutional neural networks (CNNs), in particular, have found extensive applications in various interdisciplinary domains, including mechanical fault diagnosis [20][21][22]. The LeNet-5 based CNN was introduced by Wen et al [20] to detect bearing faults.…”