• ADARSH MANDLIYA
• ADITYA PARASIA
• CHANDRAPAL SINGH PARIHAR
• HIMANSHU BHIWAPURKAR
• UMESH CARPENTER

This paper presents a Condition-Based Monitoring and Maintenance System for tunnel booster fans, focusing on vibration analysis as a key diagnostic tool. As critical components in tunnel ventilation, booster fans must operate efficiently to ensure safety and air quality. This system utilizes real-time vibration data to assess fan performance and detect potential mechanical issues before they escalate into failures. By employing advanced signal processing techniques and machine learning algorithms, the system identifies patterns indicative of wear, imbalance, or misalignment. The implementation of this proactive maintenance approach not only reduces downtime and maintenance costs but also enhances the reliability and lifespan of tunnel ventilation systems, ultimately contributing to safer transportation environments. Advanced techniques, including vibration signature analysis, allow for the identification of specific fault patterns, enabling targeted maintenance strategies. The integration of deep learning methods with vibration analysis enhances the accuracy of fault detection and predictive maintenance, streamlining the monitoring process.

Condition-Based Monitoring, Tunnel Booster Fans, Machine Learning, Predictive Maintenance, Failure Detection, Data Analytics

"CONDITION-BASED MONITORING AND MAINTENANCE SYSTEM OF TUNNEL BOOSTER FAN THROUGH VIBRATION ANALYSIS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 4, page no.n619-n626, April-2025, Available :http://www.jetir.org/papers/JETIR2504D75.pdf

"CONDITION-BASED MONITORING AND MAINTENANCE SYSTEM OF TUNNEL BOOSTER FAN THROUGH VIBRATION ANALYSIS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 4, page no. ppn619-n626, April-2025, Available at : http://www.jetir.org/papers/JETIR2504D75.pdf