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@article{187973,
        author = {Pranjali.A.Bandgar and Ashlesha.A.Nikam and Harshvardhan.D.Sakale and Rushikesh.L.Jankar},
        title = {Fault Detection And Monitoring For Industrial Motor},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {289-294},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=187973},
        abstract = {This paper presents the design and implementation of a proof-of-concept (PoC) open-source data logger system for real-time fault detection and predictive maintenance of industrial electric motors. The system is developed using embedded systems and integrates a Raspberry Pi to enable advanced data processing and intelligent diagnostics using machine learning. It collects and analyzes sensor data in real-time from thermistor sensors, piezoelectric sensors, speed sensors, current sensors, and voltage sensors. The sensors monitor critical motor parameters such as temperature, vibration, and electrical characteristics in hybrid or enclosed motor environments. Thermistors detect overheating caused by shaft overloads, insulation failure, or bearing wear. Piezoelectric sensors are used to detect vibrations and mechanical stress, providing key insights into issues such as shaft misalignment, unbalanced loads, and improper mounting. Current and voltage sensors help identify electrical faults like overcurrent, undervoltage, and phase imbalances. This can indicate issues in the motor’s power system, helping to assess cooling system efficiency and potential overload conditions. A Raspberry Pi is embedded in the system to handle sensor interfacing, real-time data logging, and implementation of predictive maintenance algorithms. The algorithms detect early-stage faults and provide timely maintenance alerts to reduce downtime and enhance motor reliability. By using open-source hardware and software, the system offers a cost-effective, scalable, and adaptable solution for both industrial monitoring and academic research. Lab tests validate its effectiveness for continuous condition monitoring and intelligent fault detection in electric motor applications.},
        keywords = {Data Logging, Fault Detection, Industrial Motor, Predictive Maintenance, Raspberry Pi.},
        month = {November},
        }