Energy-Efficient AI-Enabled Edge Devices for Real-Time Predictive Maintenance in Industrial IoT Systems

Abstract

Industrial Internet of Things (IIoT) platforms rely heavily on continuous equipment condition monitoring to minimize unscheduled downtime, extend asset operational life, and optimize manufacturing throughput. However, traditional cloud-centric predictive maintenance (PdM) architectures introduce severe bandwidth constraints, transmission latencies, and critical data privacy risks when transmitting massive high-frequency sensor telemetry. Deploying artificial intelligence (AI) directly onto resource-constrained edge hardware offers a promising alternative, yet it remains heavily constrained by tight memory limits, thermal design power (TDP) bounds, and restricted computational budgets on microcontroller units (MCUs) and neural processing units (NPUs). This review paper comprehensively examines state of-the-art energy-efficient AI-enabled edge architectures tailored for real-time predictive maintenance in modern IIoT ecosystems. We systematically evaluate advanced model compression techniques—including post-training 8 bit/4-bit quantization, quantization-aware training, dynamic structured pruning, knowledge distillation, and Hardware-Aware Neural Architecture Search (HW-NAS)—and quantify their performance on embedded hardware platforms such as ARM Cortex-M microcontrollers and RISC-V edge processors. Furthermore, we analyze adaptive energy-management paradigms, including event-triggered duty-cycling, multi-modal sensor fusion, and energy harvesting integration. Experimental benchmark evaluations on high-frequency industrial bearing and vibration datasets demonstrate that optimized 8-bit quantized convolutional networks achieve over 96.8% anomaly classification accuracy while reducing dynamic energy consumption by 87.8% and inference latency to under 12 ms compared to uncompressed floating-point models. Finally, this paper synthesizes emerging research challenges, outlines critical architectural gaps, and identifies future trajectories toward ultra-low-power neuromorphic edge processing nodes for self-healing industrial systems.

KEYWORDS: Predictive Maintenance; Edge Artificial Intelligence; Industrial Internet of Things (IIoT); TinyML; Model Quantization; Energy Efficiency; Condition Monitoring.