Optimizing Predictive Maintenance in Industrial IoT Cloud Using Dragonfly Algorithm

Cloud computing enables users to access and utilize various computing resources over the internet, including servers, storage, databases, and analytics. This paradigm offers flexibility, scalability, and cost efficiency, making it a critical technology for numerous applications. In the realm of the...

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Vydané v:IEEE internet of things journal Ročník 12; číslo 17; s. 36001 - 36018
Hlavní autori: Rani S, Sheeja, AbuRukba, Raafat, El-Fakih, Khaled
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 01.09.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Accuracy
Algorithms
Brokenstick regression multiobjective dragonfly optimization
cloud
Cloud computing
Data analysis
Data collection
Downtime
Error analysis
Error reduction
Fault diagnosis
Heuristic methods
Industrial applications
Industrial Internet of Things
Industrial IoT
Internet of Things
Machine learning algorithms
Maintenance
Multiple objective analysis
Optimization
Prediction algorithms
Predictive maintenance
predictive maintenance optimization
Real-time systems
Recall
Scalability
ISSN:2327-4662, 2327-4662
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Popis
Shrnutí:Cloud computing enables users to access and utilize various computing resources over the internet, including servers, storage, databases, and analytics. This paradigm offers flexibility, scalability, and cost efficiency, making it a critical technology for numerous applications. In the realm of the Internet of Things (IoT), cloud computing provides a scalable and flexible infrastructure for managing the vast amount of data generated by IoT devices. Specifically, in Industrial IoT applications (IIoT), predictive maintenance has become a key focus, leveraging advanced technologies to forecast equipment failures and minimize downtime. However, achieving high accuracy in fault prediction remains a challenge. To address this, we propose a novel approach called Brokenstick Regression-based multiobjective dragonfly predictive optimization (BR-MDPO). This method aims to optimize predictive maintenance with enhanced accuracy and execution time (ET). The process begins with IoT devices collecting data, such as vibration, temperature, speed, torque, and operational hours, from industrial machinery. This data is then sent to centralized cloud data centers for predictive analysis. The BR-MDPO technique utilizes the Multiobjective Dragonfly Optimization algorithm, a metaheuristic inspired by the natural behavior of dragonflies, to solve multiobjective optimization problems. Brokenstick regression analyzes the data to optimize various objective functions. The technique identifies potential failures, facilitating proactive maintenance and informed decision-making to ensure continuous productivity. The proposed method shows a significant improvement in accuracy, precision, and recall by 7%, 5%, and 6%, respectively. The observed results reveal a 6%, 4%, and 5% enhancement in the accuracy, precision, and recall. Furthermore, the proposed technique realizes a substantial reduction in error rate by 68%, 15%, and 13% reduction in ET as well as latency compared to conventional methods.
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ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2025.3582671