ML-Based Predictive Maintenance in Industrial IoT Networks

Abstract

Industrial Internet of Things (IIoT) deployments are transforming asset-intensive sectors by instrumenting machines with dense sensor networks, enabling real-time monitoring and data-driven maintenance. Yet, many plants still rely on fixed-interval or reactive maintenance, which increases downtime, spare-parts waste, and safety risk. This manuscript presents an end-to-end, ML-based predictive maintenance (PdM) framework designed specifically for IIoT networks operating under realistic bandwidth, latency, and reliability constraints. The proposed architecture combines edge analytics for fast anomaly screening, fog-node feature aggregation for context fusion, and cloud orchestration for model lifecycle management. The learning stack integrates supervised classification for failure prediction windows, regression for remaining useful life (RUL) estimation, and unsupervised anomaly detection for new or rare failure modes.

We first review PdM literature across signal processing, feature learning, and networked systems considerations, highlighting common pitfalls such as label sparsity, class imbalance, data drift, and domain shift across sites. We then outline a methodology covering data acquisition (vibration, acoustic, current, temperature, pressure), multi-rate synchronization, feature engineering (time/frequency/cepstral), automated model selection (tree ensembles, temporal deep learning), and cost-aware thresholding. For statistical validation, we define a plant-realistic simulation comprising 240 virtual rotating assets and compressors producing multi-modal streaming data over MQTT/OPC UA with injected degradation processes and intermittent network loss.

Results show that a hybrid model (LSTM sequence encoder + XGBoost decision head) improves failure F1-score by 28.7 percentage points over a threshold baseline, reduces RUL error by 61.2%, and lowers mean alarm lead time variance, while keeping inference latency within a 20 ms budget via edge batching. A one-way ANOVA on model F1-scores confirms significant differences (p < 0.001), with Tukey HSD indicating the hybrid’s superiority over random forests (p = 0.003) and a modest but significant gain over a pure LSTM (p = 0.047). We conclude with deployment guidance on edge-cloud partitioning, active learning for label scarcity, condition-based work-order integration in CMMS/ERP, and governance for model risk management in safety-critical environments.