Citation :

Vyas Dipesh Shantilal, "IoT-Enabled Instrumentation Architectures for Predictive Maintenance and Remote Monitoring in Large-Scale Automated Industrial Plants," International Journal of Engineering Trends and Technology (IJETT), vol. 74, no. 9, pp. 128-137, 2026. Crossref, https://doi.org/10.14445/22315381/IJETT-V74I9P111

Abstract

The fast industrial digitalization process results in the implementation of Internet of Things (IoT) technologies into the big automated plants to improve its operational precision, reliability, and sustainability. The paper presents a novel IoT-based instrumentation architecture to predictive maintenance and remote monitoring in the scale of industrial setup. The suggested architecture combines distributed smart sensors, edge computing nodes, industrial communication systems, cloud-based analytics systems, and machine-learning models so that they can be used to monitor assets health in real-time and detect early faults. The system reduces system downtime occurrence and minimizes maintenance expenses by making use of condition-based monitoring, anomaly detection and time-series based predictive modeling. The architecture allows deploying the large-scale distribution with geographically distributed plants, as well as provides interoperability with previous industrial control systems like SCADA, and based on PLC infrastructures. The hybrid edge-cloud data processing model is offered to decrease the latency and to manage bandwidth overheads. Simulated large-scale industrial operational conditions show that using the experimental outcome provides remarkable advancements in fault detection, maintenance schedule Optimization and Equipment Effectiveness in general (OEE). Nonetheless, the facts on the ground, like cyber vulnerability threats, inadequate sensor reliability, high entry upfront costs, insufficiency in data control and network delays, continue to be major impediments to full industrialization. Possible directions of future research are the incorporation of digital twins, federated learning in decentralized predictive modeling, the use of AI to calibrate adaptive instrumentation based on upcoming needs, blockchain-based secure data exchange, and 5G-based ultra-reliable and low-latency communication in industrial processes that are mission-critical.

Keywords

Cloud analytics, Edge computing, Industrial automation, Industrial IoT (IIoT), IoT, Machine learning, Predictive maintenance, Remote monitoring, Smart sensors, SCADA integration.

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