KMSC-SSM: A Novel Self-Supervised Rotating Machinery Fault Detection Method Based on Multi-Scale Convolution and Bidirectional State Space Model

Abstract Rotating machinery plays a critical role in industrial systems, making timely fault detection essential for operational safety and economic efficiency. Traditional methods relying on manual feature extraction and expert knowledge are often insufficient for handling complex and variable fault conditions. While supervised and semi-supervised learning approaches partially address these limitations, they remain constrained by reliance on labeled data, poor identification of novel faults, and inadequate modeling of multi-feature interactions and temporal dependencies. To overcome these challenges, this paper proposes a self-supervised fault detection method termed KAN-Multi-Scale Convolutional State Space Model (KMSC-SSM), eliminating label dependency through signal reconstruction. Specifically, KMSC-SSM leverages a Kolmogorov-Arnold Network (KAN) to enhance nonlinear feature representation, employs a Multi-scale Convolutional Bidirectional State Space Module (MCB-BiSSM) to effectively capture spatiotemporal dynamics of vibration signals, and integrates Reversible Instance Normalization (RevIN) to mitigate signal non-stationarity. Moreover, a dynamic weighting mechanism optimizes module collaboration, further enhancing adaptability to complex operational environments. Experimental validations conducted on publicly available datasets demonstrate that the proposed KMSC-SSM achieves superior performance, obtaining high F1-scores without requiring labeled fault data, thus confirming its effectiveness and robustness in practical industrial applications.