Enhancing Industrial Reliability Through Predictive Maintenance Using AI and Sensor Analytics

Enhancing Industrial Reliability Through Predictive Maintenance Using AI and Sensor Analytics

ML-Based Insights for Maintenance Scheduling

1stVaishnavi Jadhav
Data science,

D. Y. Patil College of Engineering and Technology

kolhapur

3rdSanjyot Chavan
Data Science

D. Y. Patil College of Engineering and Technology

kolhapur

5th Prof. DR. G. V. Patil
Data Science(HOD)

D. Y. Patil College of Engineering and Technology

Kolhapur

2nd Ankita Mhaishalkar
Data Science

D. Y. Patil College of Engineering and Technology

Kolhapur

4th Shivtej Patil
Data Science

D. Y. Patil College of Engineering and Technology

Kolhapur

Abstract—An extensive overview of a predictive maintenance (PdM) system designed for industrial applicationsis provided. With predictive maintenance, equipment faults are predicted before they happen by using data-driven methods like machine learning and sensor data analysis. The suggested PdM system attempts to lower unplanned downtime, optimize maintenance schedules, and improve overall operational efficiency by com- bining real-time monitoring and historical data analysis. This system will include case studies illustrating the efficacy of PdM in several industrial contexts, as well as a description of the methodology for feature extraction, data collecting, and model construction. Significant cost savings, increased asset reliability, and a decrease in interruptionsfrom maintenance are among the anticipated results. In the end,this system highlights the significance of implementing a predictive maintenance strategy in contemporary industrialsettings, opening the door for a more sustainable and efficient future.

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