Methodology for Feature Selection of Time Domain Vibration Signals for Assessing the Failure Severity Levels in Gearboxes

Early failure detection in gear systems reduces unplanned downtime and associated maintenance costs in rotating machinery. Although numerous indicators can be extracted from vibration signals, selecting the most relevant ones remains challenging. This study proposes a methodology for selecting time-domain features to classify fault severity levels in spur gearboxes. Vibration signals are acquired using six accelerometers and processed to extract 64 statistical condition indicators (CIs). The most informative subset of CIs is identified and selected through a wrapper-based selection approach and artificial intelligence tools. The selected features are then evaluated based on the classification accuracy and the area under the curve (AUC) in receiver operating characteristic (ROC) achieved using Random Forest (RF) and K-nearest neighbours (K-NN) models, with performance exceeding 98%. Additionally, the effect of sensor position and inclination on signal quality and classification performance is analysed using factorial analysis of variance (ANOVA) and multiple comparison tests. The results confirm the robustness of the selected CIs and the minimal influence of sensor placement variability, supporting the practical applicability of the proposed approach in industrial settings. The methodology offers a structured framework for selecting condition indicators in vibration signals, experimentally validated using multiple sensors and fault severity levels, and it is both automated and straightforward to implement.