• Siddhesh Prabhu
• Dishant Vakte
• Aniket Thakker

Predictive maintenance monitors the performance and condition of the paraphernalia used in the regular operation process to lower and subsequently avoid the likelihood of failures altogether. The primary objective behind predictive maintenance is to be capable of predicting when an equipment failure could possibly occur based on certain factors, followed by the subsequent prevention of the same through consistently scheduled and appropriate corrective maintenance. Predictive maintenance can only be possible through condition monitoring, which stands for the regular monitoring of machines during process conditions to ensure the flawless use of these machines. This Predictive Machine Maintenance System will be a way of preventing any asset failure in the future by understanding and properly analyzing production data to identify certain peculiar patterns and predict any issues, small or large scale, before their occurrence and thereby preventing such issues from damaging the machine or the system and consequently preventing the otherwise unforeseen circumstances. Implementing technologies pertaining to industries, to analyze and observe asset health closely, optimize the machine’s maintenance schedules, and being in a position to monitor these situations closely by receiving real time alerts to any operational risks, allows the designers to henceforth avoid incurring any high maintenance and servicing costs, thereby maximizing the uptime and improving the production throughput.

Predictive Maintenance, Machine Learning, Artificial Intelligence, Turbofan Engine, Sensor Technology.

"Predictive Maintenance for Turbofan Engine", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 5, page no.d78-d81, May-2021, Available :http://www.jetir.org/papers/JETIR2105393.pdf

"Predictive Maintenance for Turbofan Engine", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 5, page no. ppd78-d81, May-2021, Available at : http://www.jetir.org/papers/JETIR2105393.pdf