Cross-domain decision method based on instance transfer and model transfer for fault diagnosis

As the digitalization of industrial assets advances, data-driven fault diagnosis has increasingly garnered attention. However, models often underperform due to the lack of sufficient training data and the complexity of operational environments. In scenarios where a similar task with abundant data exists in the source domain, leveraging the knowledge embedded in this source data could be key to constructing an effective diagnostic model for the target domain. Following this idea, this study introduces a novel cross-domain decision method, weighted structure expansion and reduction (WSER), for fault diagnosis. This method initially extracts features from the time, frequency, and time-frequency domains. It then estimates data weights following the idea of instance transfer to mitigate the dissimilarity between the source and target data distributions. Based on these estimated weights, feature selection is further performed. The extracted source knowledge is subsequently transferred to the target domain using the proposed WSER method. The proposed method is applied on two public engineering fault datasets, and the results demonstrate the effectiveness of the proposed method in increasing the accuracy of fault diagnosis.