International Scientific Journal of Engineering and Management

An International Scholarly || Multidisciplinary || Open Access || Indexing in all major Database & Metadata
ISSN: 2583-6129

Impact Factor: 6.674

Diagnostic Machine safety with IoT Monitoring

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Diagnostic Machine safety with IoT Monitoring

Md Umer Mulla1, Rahul Galimath1, Shreyas Jodagunde1, Vishwanath Shinde1 Prof. Suvarna Karki2, Dr. Supanna Shirguppe3

1Students, Electrical & Electronics Engg, S. G. Balekundri Institute of Technology

2Asst Professor, Electrical & Electronics Engg, S. G. Balekundri Institute of Technology

3Professor & Head, Electrical & Electronics Engg, S. G. Balekundri Institute of Technology

 

Abstract - After analyzing the current situation of safety of diagnostic machine, it is realized that there is a need to develop a system that could inform the users about various faults in the machine, thereby protecting from severe damages. The proposed project presents a novel system that monitors machines and detects faults. This project is a model for a real time monitoring system using contemporary technologies like Internet of Things (IoT)-based sensors that are promising for efficient machine monitoring. The proposed project is a model for a real-time monitoring system using IoT-based sensors accessible via webpages. The system involves connecting the machine to two sensors: a current sensor to continuously monitor current flow and a temperature sensor to gauge heat production. These sensors provide input signals to an Arduino controller, which processes them into real values based on sensor output electrical signals. Subsequently, these values are translated from machine-level language to low-level language and displayed on an LCD. A Wi-Fi module facilitates transmission of these values over the internet to cloud storage. In this system "ThingSpeak" is opted as cloud platform, offering data collection. This platform stores the data, enabling access to values worldwide via webpages. It also generates graphs depicting sensor values over time.

Key Words: Fault detection, Machinery monitoring, Arduino controller, Cloud storage, Real-time monitoring


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