Learning in the Model Space: Fault Diagnosis by Co-objective Learning in DynInt Model Space

Fault Diagnosis (FD) in time-varying systems faces challenges like limited training data, varying environments, and timeliness. Building upon the framework of model-space learning (MSL), we introduce co-objective learning in Dynamic-Integration network (DynInt) model space as a solution for FD. MSL involves utilizing well-fitted models that capture the dynamics within the data as more stable and parsimonious representations of the original data. DynInt integrates pooling and reservoir computing to adequately capture the data-inherent multi-scale dynamics. Representing the signal with the fitted DynInt model transforms the original signal from data space into the DynInt model space. A co-objective optimization is further introduced on DynInt, improving the fitting accuracy and category discrimination. Validation on real-world data confirms our method’s effectiveness, especially in data-limited scenarios.