This research focuses on creating a fault detection system for industrial air blowers using vibration signal analysis, as these machines are crucial for maintaining production efficiency and minimizing downtime in manufacturing processes. This study uses advanced signal processing techniques to analyse vibration signals from industrial air blowers, aiming to identify fault types like imbalance, misalignment, and bearing wear, which can lead to performance degradation or breakdowns. The methodology involves acquiring vibration data from vibration sensors on an air blower unit, using vibration analysis techniques like time domain analysis and frequency domain analysis to extract relevant features, which are then used for a fault detection of blower. The effectiveness of the proposed methodology is demonstrated through experimental validation on a real industrial air blower system. Results indicate that vibration signal analysis enables early detection of faults, allowing for proactive maintenance and minimizing downtime. Overall, this study highlights the potential of vibration analysis as a valuable tool for enhancing the reliability and performance of industrial air blower systems.