Embedded fault detection for crewed and uncrewed defence vehicle suspension systems via explainable machine learning and four post rig testing

Abstract

Suspension faults in defence vehicles (either crewed or uncrewed ground vehicles), such as damper leakage, loose mountings, and bushing degradation, can significantly impair stability, mobility, and control performance under severe off road terrain conditions. This paper presents an explainable machine learning framework for embedded suspension fault detection using multi domain vibration and acoustic sensing. The approach integrates eight physically interpretable time/frequency features with a Random Under Sampling Boosted (RUSBoost) ensemble classifier to discriminate four suspension health states. Full scale experimental validation was carried out on a Land Rover Defender 110 mounted on a four-post hydraulic vibration rig capable of reproducing broadband, defence-relevant excitation. The proposed method achieved 100% accuracy (AUC = 1) in a holdout evaluation and maintained a mean accuracy of 74% (AUC = 0.87) under tenfold cross validation, demonstrating strong generalisation. Feature importance analysis identified peak-to-peak amplitude, standard deviation, and RMS as the most influential indicators, confirming that suspension degradation is primarily characterised by increased vibration excursion and dispersion rather than spectral redistribution. Principal component analysis further showed partial overlap among fault classes, highlighting the necessity of nonlinear ensemble decision boundaries. Despite higher training cost, the ensemble classifier achieved prediction times that indicate computational feasibility for future real time embedded implementation. Overall, the framework provides a transparent, robust, and control compatible diagnostic layer, supporting future integration with predictive maintenance and fault tolerant suspension control in defence mobility systems.