This paper introduces a practical hybrid framework for predictive maintenance in industrial machinery, integrating unsupervised anomaly detection via a long short-term memory (LSTM) autoencoder (AE) and supervised fault prediction using a multilayer perceptron (MLP). The framework is validated on a realistic digital-twin dataset simulating five years of operation, addressing common challenges such as class imbalance and operational variability. The LSTM autoencoder was trained on time-series data representing normal behavior and detected 129 threshold exceedances (anomalous excesses) out of 8735 test sequences (1.48%) based on a validation-derived reconstruction error threshold. The MLP classifier, trained on operational features and rule-derived fault labels, achieved a classification accuracy of 99.93%, with precision, recall and F1-score values of 99.53%, 99.88% and 99.70%, respectively. Data preprocessing included cleaning, robust scaling, and sequence generation to ensure data quality and model reliability. The results demonstrate that the proposed architecture effectively combines sequence-based anomaly detection with a compact classification model, offering a reliable solution. Beyond model design, the study provides practical guidance on threshold calibration, fault labeling, and SCADA/cloud integration, bridging the gap between theoretical research and industrial implementation. This work contributes a scalable, industry-ready predictive maintenance solution that combines strong technical performance with real-world applicability.
Mavrelis et al. (2026) studied this question.