Advanced Sensor Networks for Real-Time Industrial Intelligence and Predictive Process Management

Ing Mohieddine Jelali

Advanced Sensor Networks for Real-Time Industrial Intelligence and Predictive Process Management

Author(s)	Dr. Ing Mohieddine Jelali
Country	United States
Abstract	Industrial production systems are increasingly expected to maintain high levels of productivity, quality, energy efficiency, safety, and operational resilience under continuously changing conditions. Conventional monitoring practices based on periodic inspection and fixed alarm thresholds frequently detect equipment degradation only after process performance has deteriorated. Advanced sensor networks offer an alternative architecture in which distributed sensing devices continuously collect information about vibration, temperature, pressure, acoustic emissions, electrical current, flow, material condition, and environmental variables. When these data are integrated with edge computing, machine learning, digital twins, and industrial decision-support systems, sensor networks can support real-time industrial intelligence and predictive process management. This simulation-based study develops a structured framework for evaluating three industrial monitoring configurations: periodic condition monitoring, connected sensor-network monitoring, and edge-intelligent predictive process management. A twelve-period synthetic operating scenario is used to examine changes in unplanned downtime, fault-detection latency, predictive-warning capability, data availability, and maintenance actionability. The analytical model treats sensor observations as time-dependent indicators of machine condition and combines them into a normalized health index. Predictive alerts are generated when the estimated probability of degradation exceeds configurable operational thresholds. The simulated results indicate that connected sensing improves industrial visibility and reduces downtime compared with periodic monitoring. The greatest improvement is obtained when sensor networks are combined with edge analytics and predictive management. In the illustrative scenario, unplanned downtime under the edge-intelligent configuration declines from 10.5% to 3.3% of scheduled production time across twelve operating periods. However, the reliability of such systems depends on sensor calibration, synchronization, data quality, cybersecurity, network resilience, model interpretability, and human oversight. The study concludes that advanced sensor networks should be treated as sociotechnical industrial infrastructures rather than collections of connected devices. Their value emerges when measurement, communication, analytics, operational knowledge, and accountable decision-making are designed as an integrated system.
Keywords	advanced sensor networks, industrial intelligence, predictive maintenance, process management, edge computing, Industrial Internet of Things, condition monitoring, digital twins.
Field	Engineering
Published In	Volume 8, Issue 4, July-August 2026
Published On	2026-07-31

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American Journal of Advanced Multidisciplinary Research and Innovation

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Advanced Sensor Networks for Real-Time Industrial Intelligence and Predictive Process Management

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