Expert label for explainable fault diagnosis and for unknown fault generalization

This study proposes the expert label that incorporates specific process knowledge into sample labels to enhance the explainability of diagnostic results. Traditional sample label is a one-hot encoding vector without inherent physical meaning, limiting the diagnostic scope to predefined fault types without explainability. In contrast, expert label as multi-label provides multi-dimensional information including disturbance type, unit location, and associated variable type. Since multi-label can accommodate an exponential number of fault types, the diagnosis models based on expert label have the potential to generalize unknown faults. Meanwhile, we develop the process multi-scale feature mixture network (PMFMN), which excels in deep feature extraction through modules including multi-scale decomposition, multiple pattern representation, and Fourier attention mechanism. The effectiveness of PMFMN with expert label is validated through case studies in two chemical processes, which further improves the fault diagnosis rate, provides reliable diagnostic results with explainability, and can diagnose unknown faults.