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International Journal of Advanced Research in Computer and Communication Engineering A monthly Peer-reviewed & Refereed journal
ISSN Online 2278-1021ISSN Print 2319-5940Since 2012
IJARCCE adheres to the suggestive parameters outlined by the University Grants Commission (UGC) for peer-reviewed journals, upholding high standards of research quality, ethical publishing, and academic excellence.
← Back to VOLUME 11, ISSUE 4, APRIL 2022

Remote Machine Condition Monitoring

Harsh Merchant, Ayan Mulla, Umair Sayed, Hamza Rangwala, Sufiyan Ansari,Abdulaziz Kazi

DOI: 10.17148/IJARCCE.2022.11407

Abstract: The exponential growth of data generation is difficult to perceive. Every enterprise has a lot of data, some of which they don’t even accumulate due to difficulty of data extraction and selecting the most relevant data. Hence, appears the mistake of neglecting useful data amidst other unproductive data. This phenomenon does not depend on the company’s ability to compute and communicate, but rather on the ability to provide adequate information, to take good decisions and to compare results with the planned objectives. These can be done through adopting modern approaches to machine condition monitoring. Machine condition monitoring or condition-based monitoring is the process of monitoring machinery conditions while in operation. The data generated by machines provide with real insights into near real-time values of the machine parameters which are helpful for analysis. This paper shows how this methodology has developed, the main features and benefits of Data Extraction and Remote Machine Condition Monitoring. By taking measurements of pressure, temperature, and vibration, we are more likely to identify early malfunctions resulting in costly shutdowns. In turn, it will ensure the long-term and effective operation of entire machine systems.

Keywords: Condition Monitoring, Dashboards, Fault detection, Data extraction.

How to Cite:

[1] Harsh Merchant, Ayan Mulla, Umair Sayed, Hamza Rangwala, Sufiyan Ansari,Abdulaziz Kazi, “Remote Machine Condition Monitoring,” International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), DOI: 10.17148/IJARCCE.2022.11407