Predictive Maintenance of Industrial Gearbox Systems Using Multimodal Sensor Fusion and Explainable Machine Learning

Abstract

In manufacturing plants, power-generation systems, transportation equipment, mining machinery, and automated production systems, industrial gearbox systems are essential components that can cause production losses, equipment damage, safety concerns, and substantial maintenance expenses if they fail unexpectedly. Traditional maintenance strategies, such as corrective maintenance and time-based preventive maintenance, are ineffective at detecting early signs of faults because they rely on set maintenance intervals or single sensor readings. This research presents a predictive maintenance framework for an industrial gearbox system based on fusing multiple sensor types with explainable machine learning (XAI). Common gearbox degradation mechanisms such as gear tooth damage, bearing defects, shaft misalignment, lubrication issues, and abnormal wear are characterized using time, frequency and time-frequency features. For fault classification, condition assessment, and remaining useful life prediction, the Study Considers Machine-Learning models such as Random Forest, Support Vector Machine, Gradient Boosting, and deep neural networks. Rather than using a single information source, the study uses multimodal fusion to improve model robustness by combining complementary information from multiple sensing modalities. One significant finding is the inclusion of explainable machine learning in predictive maintenance. The study uses techniques such as SHAP (SHapley Additive Explanations), feature importance analysis, and local explanation methods to identify which sensor signals and features contribute most to model predictions. This provides greater transparency and helps maintenance engineers understand why a gearbox has been graded healthy, degraded, or faulty. The framework is expected to enable earlier fault detection, more reliable diagnostics, less unplanned downtime, better maintenance scheduling, and more efficient use of maintenance resources. The multimodal sensing and explainable AI approach offers a practical, interpretable solution for intelligent condition monitoring of industrial gearboxes. The framework can facilitate the shift from traditional preventive maintenance to data-based, condition-based, and predictive maintenance, thereby enhancing equipment reliability, operational efficiency, and industrial productivity.