An adaptable physics-informed fault diagnosis approach via hybrid signal processing and transferable feature learning for structural/machinery health monitoring

Structural damages, such as structural looseness and structural cracks, are commonly observed as the root causes of failures in industrial plants. These issues have been extensively studied, and deep diagnostic tools have shown promise in identifying and addressing them. However, these tools rely on large amounts of data, which leads to computational burdens and time consumption. To tackle this challenge, a groundbreaking technique is proposed within the context of this study. The key innovation of this approach lies in its ability to integrate information from various processing functions and utilize an efficient feature bank that facilitates the execution of an effective feature learning method based on a multisource strategy. This novel research also focuses on the selection of transferable features from multiple distributions for diagnostics involving unseen failure distributions. By minimizing the mean squared error function, which is based on various source domains, the accuracy of diagnostics is significantly improved. Furthermore, the joint minimization of diagnostics independence concerning failure distribution, as well as the dimension of the transferable feature space between the source domains, leads to enhanced diagnostics speed and feature visualization. To validate the effectiveness of this approach, a real case study of a structural/machinery vibration dataset is conducted to address the multi-fault diagnosis problem, encompassing machinery health conditions, foundation looseness, and cracks under various operational conditions. The results obtained from this study demonstrate that the proposed algorithm performs remarkably well in real diagnostics scenarios involving unseen failure distributions.