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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM63126
Volume:
10 |
5 |
May 2026
Pages:
1-6
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Information Technology
Sir Visvesvaraya institute of technology, nashik
Information Technology
Sir Visvesvaraya institute of technology, nashik
Information Technology
Sir Visvesvaraya institute of technology, nashik
Information Technology
Sir Visvesvaraya institute of technology, nashik
Information Technology
Sir Visvesvaraya institute of technology, nashik
This paper presents an IoT-based system for monitoring and analyzing the performance of electronic products using sensor data. The system uses sensors such as temperature, humidity, voltage, and current to continuously track the condition of a product. These sen sors collect real-time data on how the product operates under different conditions and send it to a cloud platform for storage and analysis. The collected data helps manufacturers under stand important parameters like overheating, power consumption, and performance varia tions. By studying this information, compa nies can identify faults, estimate product lifes pan, and improve the design and functional ity of future products. This approach builds a smart connection between the product and the manufacturer, ensuring better quality, im proved performance, and increased reliability through data-driven insights.
KEYWORDS
This project presents an effective IoT and AI based system for smart monitoring and pre dictive maintenance of home appliances. The system creates a useful connection between users and manufacturers, where real-time sen sor data helps in predicting faults and improving the overall product design. By using sim ple and efficient AI models, the system stays practical, cost-effective, and suitable for real world applications, even in low-resource envi ronments. In the future, the system can be further im proved by using advanced data-sharing meth ods, applying deep learning for better accu racy in multi-sensor analysis, and connecting it with smart home ecosystems for seamless automation and control.
[1] R. S. Rana, ’AI-Driven Fault Detection and Predictive Maintenance in Electrical Power Systems,’ AJATES, 2025. [2] A. K. Sahu et al., ’Internet of Things En abled Device Fault Prediction System Using Machine Learning,’ IJIRSET, 2024. [3] S. Verma et al., ’Review of Self-Healing IoT Networks Based on AI-Driven Models,’ IEEE Access, 2023 [4] P. Chaves, T. Fonseca, L. L. Ferreira, B. Cabral, O. Sousa, A. Oliveira, and J. Landeck, “An IoT Cloud and Big Data Architecture for the Maintenance of Home Appliances,” IEEE, 2025. [5] A. Papaioannou, A. Dimara, C. Pa paioannou, I. Papaioannou, S. Krinidis, C.-N. Anagnostopoulos, C. Korkas, E. Kosmatopou los, D. Ioannidis, and D. Tzovaras, “Simu lation of Malfunctions in Home Appliances’ Power Consumption,” Energies, vol. 17, no. 4529, pp. 1–31, 2024. [6] E. Zero, M. Sallak, and R. Sacile, “Pre dictive Maintenance in IoT-Monitored Sys tems for Fault Prevention,” Journal of Sensor and Actuator Networks, vol. 13, no. 57, pp. 1–20, 2024
Publication Date
5/13/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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No funding information available for this research.
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