A Neural-Symbolic Network for Interpretable Fault Diagnosis of Rolling Element Bearings Based on Temporal Logic

This study examines the issue of interpretability in fault diagnosis for rolling bearings using a symbolic learning technique. We propose the adoption of weighted signal temporal logic (wSTL) as a formal language and introduce the temporal logic network (TLN) as a neural-symbolic learning architecture capable of encoding symbolic wSTL representations for input signals. TLN comprises three subnetworks: a basic predicate network for abstracting features and generating predicates from vibration signals, an autoencoder for identifying significant signal components, and a logic network for constructing a formal language that aids in fault classification and model interpretation. To improve comprehensibility, timed failure propagation graphs (TFPGs) are used to visually represent the logical relationships and propagation of fault events. Experimental results demonstrate TLN’s ability to extract impulse fault patterns from signals, accurately describe fault events through learned wSTL formulas, and enhance understanding of fault events for nonexpert individuals through TFPGs. These findings contribute to the field of fault diagnosis in rolling bearings by incorporating symbolic learning techniques, using formal language representation and TFPG for improved interpretability.