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This work is licensed under a Creative Commons Attribution 4.0 International License.
INDUSTRIAL FAULT MONITORING SYSTEM
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Abstract: Industrial operations often face challenges due to unexpected machine failures, inefficient monitoring, and lack of real-time fault detection, which can lead to production loss, increased maintenance costs, and safety risks. Traditional fault monitoring methods rely on manual inspection and periodic maintenance, making them ineffective for early fault detection and continuous supervision. Therefore, there is a growing need for an intelligent system that can automate fault monitoring and enhance industrial efficiency.
This paper presents the design and implementation of an Industrial Fault Monitoring System using IoT and embedded intelligence. The proposed system integrates various sensors such as temperature sensors for detecting overheating, vibration sensors for identifying mechanical faults, and current/voltage sensors for monitoring electrical conditions. An embedded platform such as Raspberry Pi or microcontroller is used to process real-time sensor data.
Machine learning techniques are employed to analyze machine behavior, detect anomalies, and predict potential faults based on historical and real-time data. The system continuously monitors machine parameters and automatically updates the system status whenever abnormal conditions are detected. Users can remotely access machine data through a mobile application enabled by IoT connectivity.
In addition, the system provides instant alerts for fault conditions such as overheating, excessive vibration, or electrical overload, ensuring timely corrective actions. It can also assist in predictive maintenance by analyzing performance trends and suggesting maintenance schedules.
The proposed Industrial Fault Monitoring System offers a reliable, efficient, and cost-effective solution for modern industries. By combining IoT, sensor integration, and intelligent data analytics, it enhances machine reliability, improves safety, and supports smart industrial automation in Industry 4.0 environments.
Keywords: Industrial Fault Monitoring System, Internet of Things (IoT), Machine Learning, Embedded Systems, Raspberry Pi, ESP32, Temperature Sensor, Vibration Sensor, Current Sensor, Real-Time Monitoring, Predictive Maintenance, Wireless Communication, Cloud Computing, Industrial Automation, Smart Factory, Fault Detection, Remote Monitoring, Data Analytics, Industry 4.0, Safety Monitoring.
How to Cite:
[1] Mr. M. RAVI, M. Tech(Ph. D), T. VENU MADHAV*, βINDUSTRIAL FAULT MONITORING SYSTEM,β International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), DOI: 10.17148/IJARCCE.2026.154158
This work is licensed under a Creative Commons Attribution 4.0 International License.