A Predictive Maintenance Framework Using Deep Learning and IoT Techniques to Power the Digital Transformation in Service Organizations

Abstract

Digital transformation is reshaping maintenance strategies in service organizations. The paradigm is shifting from reactive and preventive approaches to predictive maintenance (PdM). Telecom cabinets are the backbone of modern connectivity. They house the essential networking and power equipment. Most are deployed outdoors in uncontrolled environments. These cabinets face severe environmental stressors: extreme temperatures, fluctuating humidity, particulate air pollution, and physical security threats. Historically, the telecommunications industry managed this infrastructure through reactive maintenance. It relied on rigid time-based inspection schedules or intervened only after customer complaints led to service disruptions. This approach results in unexpected equipment failures, costly emergency repairs, and significant service downtime.  In this article, we propose a comprehensive IoT-based predictive maintenance framework. It leverages deep learning to ensure safer, more reliable, and cost-effective operations of telecom cabinets. Moving beyond routine system integration, this work delivers four key innovations across methodology and operational deployment. The first is a hybrid LSTM-residual anomaly detection mechanism with domain-weighted severity scoring. The second is a cost-informed risk matrix that uses uncertainty-based deferment for maintenance action selection. Third, we operationalize explainable AI (XAI) and integrate it directly into technician workflows. Fourth, edge-algorithm co-design enables real-time inference on resource-constrained hardware. To evaluate the proposed framework, we conducted extensive experiments in a telecom service organization. We used a real-world dataset covering a 12-month monitoring period across 150 physical telecom cabinets. Data was sampled at 5-minute intervals, generating approximately 15.7 million records. The results show the proposed framework achieves 97.4% accuracy, 86.5% precision, 89.2% recall, and 87.8% F1-score. It outperforms hybrid models such as CNN-LSTM. The framework achieved a precision-recall AUC of 0.885 and an extended detection lead time of 18.7 hours. The proposed framework was benchmarked internally against rule-based, SVM, MLP, XGBoost, standalone RF, and hybrid configurations using the same temporally isolated dataset. The results demonstrate improved minority-class detection and operational lead time within the experimental setting of this study. Beyond predictive accuracy, the framework provides significant operational impact. There is a 71% reduction in unnecessary technician dispatches. Mean time to repair (MTTR) improves by 66%. Operator trust rating rises to 89%. First-visit repair rate reaches 94%. These results establish the framework as a robust solution for predictive maintenance in service organizations. It bridges the gap between algorithmic innovation and operational practice.