SMART MULTI-SENSOR CONDITION MONITORING SYSTEM FOR PREDICTIVE MAINTENANCE OF ROTARY DRIVE ASSEMBLIES

Abstract

The rapid growth of Industry 4.0 has increased the demand for intelligent condition monitoring systems that can continuously evaluate the health of industrial machinery and reduce unexpected failures. Rotary drive assemblies are widely used in manufacturing industries, where faults such as bearing wear, shaft misalignment, excessive vibration, overheating, and speed fluctuations can significantly affect machine performance and maintenance costs. Conventional maintenance methods are generally based on periodic inspection or manual observation, making early fault detection difficult. This research presents the development of a Smart Multi-Sensor Based Condition Monitoring System for rotary drive assemblies. The proposed system integrates four sensing technologies, including a Hall Effect Sensor for rotational speed (RPM) measurement, a 10k NTC Thermistor for bearing temperature monitoring, an MPU6050 Accelerometer and Gyroscope for three-axis vibration measurement (AX, AY, and AZ), and two HC-SR04 Ultrasonic Sensors for shaft alignment monitoring. Two Arduino microcontrollers are employed for efficient sensor interfacing and real-time data acquisition. A Python-based monitoring dashboard was developed using Visual Studio Code to receive live sensor data, display real-time values, generate automatic graphs, and store measurement records in Microsoft Excel files. Signal processing techniques, including Fast Fourier Transform (FFT) and digital filtering methods, were applied to analyze vibration signals and improve fault detection accuracy.