Multimodal Industrial Predictive Maintenance Using Hybrid Deep Learning and Explainable AI: A Literature Survey

Abstract: Predictive maintenance has become an important application of artificial intelligence for reducing unexpected equipment failures, downtime, and maintenance costs. Recent studies have investigated machine learning, deep learning, Remaining Useful Life estimation, anomaly detection, and Explainable Artificial Intelligence for improving industrial maintenance decisions. However, predictive performance alone is not sufficient for practical maintenance environments, where engineers also need to understand why a model produces a particular prediction. This literature survey reviews fifteen selected studies covering machine learning approaches, deep learning architectures, RUL prediction, anomaly detection, explainable predictive maintenance, digital twins, and maintenance servitisation. The reviewed studies show that Random Forest, Support Vector Regression, XGBoost, CNN-LSTM, LSTM, autoencoders, ensemble learning, and reinforcement learning have been explored for different predictive maintenance tasks. SHAP, LIME, L2X, and other explanation techniques have also been used to improve model transparency. The review identifies recurring challenges involving dataset availability, cross-domain generalization, preprocessing, explanation quality, real-time deployment, and integration of multiple sensor modalities. Based on these findings, the survey identifies a research direction toward multimodal industrial predictive maintenance using suitable hybrid deep learning and XAI techniques, while leaving the final model and dataset selection for subsequent experimental evaluation.

Keywords: Predictive Maintenance, Industrial Sensor Data, Deep Learning, Explainable Artificial Intelligence, SHAP, Remaining Useful Life, Industry 4.0.