Abstract
Accurate and fast rolling bearing fault diagnosis is required for the normal operation of rotating machinery and equipment. Although deep learning methods have achieved excellent results for rolling bearing fault diagnosis, the performance of most methods declines sharply when the working conditions change. To address this issue, we propose a one-dimensional lightweight deep subdomain adaptation network (1D-LDSAN) for faster and more accurate rolling bearing fault diagnosis. The framework uses a one-dimensional lightweight convolutional neural network backbone for the rapid extraction of advanced features from raw vibration signals. The local maximum mean discrepancy (LMMD) is employed to match the probability distribution between the source domain and the target domain data, and a fully connected neural network is used to identify the fault classes. Bearing data from the Case Western Reserve University (CWRU) datasets were used to validate the performance of the proposed framework under different working conditions. The experimental results show that the classification accuracy for 12 tasks was higher for the 1D-LDSAN than for mainstream transfer learning methods. Moreover, the proposed framework provides satisfactory results when a small proportion of the unlabeled target domain data is used for training.
Highlights
Due to advances in industrial technology, rotating machinery is increasingly used in many fields, such as electric power generation, chemical production, and aerospace [1,2]
domain adaptation (DDA) approaches have been used for fault diagnosis, most existing methods assume that the global distribution differs for the target and source domains and try to reduce this difference
The experimental results show that the proposed framework has strong feature extraction and domain adaptation ability and can 12 transfer tasks, the proposed model achieved more than 98% accuracy based on the remaining 90% of the target domain test data
Summary
Due to advances in industrial technology, rotating machinery is increasingly used in many fields, such as electric power generation, chemical production, and aerospace [1,2]. Most DL models, such as the long short-term memory network (LSTM) [11], deep belief network (DBN) [12], and convolutional neural network (CNN) [13,14,15], perform well if the datasets of the source domain and target domain tasks have the same distribution [16]. This assumption is rarely applicable in practical conditions. It is essential to consider the change in working conditions to improve the accuracy and efficiency of bearing fault diagnosis
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