| Title | TinyML-Based Predictive Maintenance for Resource-Constrained Industrial IoT Devices: A Lightweight Edge Intelligence Framework |
| Research Area | TinyML |
| Abstract | The Industrial Internet of Things (IIoT) has transformed industrial monitoring by connecting ma-chines, sensors, controllers, and analytical platforms through intelligent communication networks. Continuous monitoring of industrial equipment creates opportunities for predictive maintenance, where machine-learning techniques can identify abnormal operating conditions before equipment failure occurs. However, conventional cloud-based machine-learning approaches often require con-tinuous transmission of sensor data to remote servers. This can increase communication overhead, latency, network dependency, and energy consumption. Tiny Machine Learning (TinyML) provides an alternative by enabling lightweight machine-learning models to execute directly on resource-con-strained edge devices and microcontrollers. This paper proposes a conceptual TinyML-based predic-tive maintenance framework for industrial IoT environments. The proposed framework collects vi-bration, temperature, acoustic, current, and operational measurements from industrial equipment and performs local preprocessing and inference at the edge. Lightweight machine-learning models are optimized using techniques such as feature reduction, quantization, pruning, and compact model ar-chitectures. The framework introduces an adaptive anomaly score that combines multiple sensor characteristics to identify early signs of equipment degradation. Instead of continuously transmitting raw sensor streams to a cloud platform, only selected events, summarized features, or maintenance alerts are communicated to higher-level systems. This approach can reduce network traffic and ena-ble faster local decisions. The paper analyzes the architecture, feature-extraction process, anomaly-detection mechanism, model optimization strategy, communication design, security considerations, and evaluation methodology. |
| Keywords | TinyML, Industrial Internet of Things, Predictive Maintenance, Edge AI, Machine Learning, Anomaly Detection, Embedded Systems, Model Compression, Edge Computing, Intelli-gent Manufacturing |
| Paper Status | Published |
| Volume | 2 |
| Issue | 4 |
| Published On | 25/07/2026 |
| Published File |
IJSRTD_4886.pdf
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