Cross-domain privacy-preserving broad network for fault diagnosis of rotating machinery

Abstract

The broad learning system-based data processing method has been widely used in the field of industrial intelligent operation and maintenance, and has achieved impressive results. However, data island created by privacy concerns in real industrial scenarios makes centralized deep training extremely difficult. In addition, the fluctuating operating conditions of machines including speed and load significantly affect the distribution of monitoring data representing the operating state of the device. These two problems make it difficult to apply broad learning system to practical application scenarios. Therefore, in view of the above problems, a novel cross-domain privacy-preserving broad network (CDPPBN) for cross-domain fault classification of rotating machinery is presented. Specifically, a cross-domain interactive data encryption protocol feature extractor is designed firstly to ensure the privacy and security of both source and target domain data during feature extraction process. Then, a domain adaptation target classifier is constructed to achieve the fault classification task in the target domain without labels. In this classifier, a maximum mean discrepancy regularization term is constructed to match the mean projections of the source and target domains to adapt the source classifier to the target domain, and a semi-supervised local graph embedding regularization term is constructed to mine the structural features of the target domain data. CDPPBN not only has high performance in terms of data privacy protection, but also makes full use of both labeled source data and unlabeled target data to generate a domain adaptation target classifier. The effectiveness of the presented method is verified through bearing monitoring signal. Experimental analysis shows that the proposed CDPPBN has better data privacy protection capability and data cross-domain diagnosis ability compared with other methods.