Intelligent Fault Diagnosis Method Based on Multi-source Data and Multi-feature Fusion

Deep learning (DL) theory has made great progress in the field of intelligent fault diagnosis, and the development of domain adaptation has greatly promoted fault diagnosis under polytropic working conditions (PWC). Extensive studies have been conducted to solve the problem of fault diagnosis under PWC. However, the existing fault diagnosis methods based on domain adaptation have the following shortcomings. First, multi-source information fusion is rarely considered. Second, the utilization of inherent labels is also insufficient in classification problems. To deal with the above problems, a novel multi-source dense adaptation adversarial network (MDAAN) is proposed, which leverages multi-sensor vibration information and classification label information. Specifically, the frequency spectrum of multi-sensor data is first employed to make full use of fault information. Afterwards, the dense convolution and fusion convolution blocks are used for deep feature extraction and fusion. Finally, a joint loss function is reconstructed under the framework of unsupervised learning, which considers the distribution differences of the features and the label information simultaneously. The experimental results from various working conditions, including still distant working conditions, all demonstrate that the proposed method can achieve state-of-the-art performances, which has shown great promise as an intelligent fault diagnosis method.