Rolling Horizon Neurocomputing Optimizes Industrial IoT Compressor Monitoring at the Edge Under Constraints and Drift
DOI:
https://doi.org/10.66238/jaeee26Keywords:
Industrial Internet of Things, Neurocomputing, Rolling-Horizon Optimization, Predictive Maintenance, Edge ComputingAbstract
The rapid integration of the Industrial Internet of Things into modern manufacturing environments has fundamentally transformed the operational paradigms of heavy machinery management. Among the most critical assets in these industrial ecosystems are industrial air and gas compressors, which serve as the lifeblood of pneumatic processes and automated control systems. However, the continuous and reliable monitoring of such dynamic equipment presents significant challenges due to the high volatility of sensor data, the computational constraints of edge devices, and the inherent latency in centralized cloud architectures. This paper proposes a novel framework utilizing rolling-horizon neurocomputing optimization tailored specifically for Industrial Internet of Things compressor monitoring networks. By hybridizing predictive control philosophies with advanced artificial neural networks distributed across edge nodes, the proposed system dynamically adapts its optimization horizon to incoming data streams, thereby minimizing transmission latency and maximizing predictive anomaly detection accuracy. Extensive simulations demonstrate that this rolling-horizon neurocomputing approach significantly outperforms static diagnostic models by reducing communication overhead while maintaining superior predictive fidelity under transient operational states. The findings provide a robust theoretical foundation and practical deployment strategy for next-generation smart manufacturing facilities seeking to achieve zero-downtime operations through intelligent, decentralized machinery prognostics.
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