Empirical Study on Aero-Engine Bearing Fault Diagnosis Using Single-Channel LG-SSMNet

Abstract

Multi-sensor data fusion is widely recognized as a key enabler for reliable aero-engine condition monitoring under complex operating conditions. However, this paradigm inherently increases system complexity. This paper conducts a systematic empirical study to evaluate the practical efficacy and boundaries of deep feature mining using only a single channel. We propose a Local-Global State Space Model Network (LG-SSMNet), which uses multi-scale 1D CNNs to fit local transient pulse responses and employs a Selective State Space Model (Mamba) to construct a long-term periodic integrator, achieving feature decoupling within a single channel. To address the discretization divergence issue of continuous vibration signals, an empirical stabilization configuration involving dual normalization and stratified learning rates is introduced. Deep quantitative analysis on the HIT aero-engine bearing dataset shows that the four acceleration channels achieve stable accuracies over 99.6% (approaching 100%), while displacement channels show significant divergence. The underperforming channel (Sensor 1) reached only 91.31% due to a critical missed detection rate of outer race faults (4.3% misclassified as healthy). Through joint empirical evidence using ROC-AUC, F1-scores, and qualitative feature visualization, we demonstrate that with reasonably selected acceleration sensors, single-channel deep mining can serve as a practical alternative to multi-channel fusion.