Multitask Learning Based Collaborative Modeling of Heterogeneous Data for Compound Fault Diagnosis in Manufacturing Processes

Due to the complex and variable working conditions of manufacturing processes as well as the interconnection and coupling loops, faults of multiple processes, subsystems or control loops may occur at the same time or cascade successively, compound fault becomes the norm. After the compound fault occurs, it may be characterized by the abnormal sensor data, the image or video data. Most of the traditional methods use a single type of sensor data for fault diagnosis, which may affect the diagnosis performance. To solve this problem, considering the heterogeneity of compound fault data, a multitask learning-based collaborative modeling method is proposed for compound fault diagnosis. Specifically, the heterogeneous compound fault data is mapped into a common subspace, of which a dynamic combination coefficient is reasonably set considering the different contributions of unstructured and structured data. Moreover, considering the correlations among heterogeneous data, the low-rank constraint trace norm is introduced into multitask learning, and an attention-based feature fusion network is designed for compound fault diagnosis. Finally, two cases on the actual manufacturing process, hot rolling process, are conducted to evaluate the effectiveness of the proposed method. The experimental results demonstrate that the new collaborative modeling scheme of heterogeneous data can better perform compound fault diagnosis tasks than the existing state-of-the-art algorithms.