Experimental And Theoretical Investigation of Rotating Machinery Faults Using LabVIEW Based Data Acquisition and Analysis

Condition monitoring and fault diagnosis of rotating machinery are critical to ensuring operational reliability and preventing catastrophic failures in industrial systems. This study presents a comprehensive experimental and theoretical investigation of the three most common mechanical failures (sliding crank, mass imbalance, and shaft misalignment (parallel, angular, and combined)). A specially designed experimental test setup was used to simulate failure conditions under controlled operating parameters. Acceleration signals were captured using Integrated Circuit Piezoelectric (ICP) accelerometers at a shaft speed of 1000 RPM. A custom Data Acquisition (DAQ) and signal processing software was developed in NI LabVIEW to perform real-time Time Domain and Fast Fourier Transform (FFT) spectrum analyses. Experimental results show different spectral signatures for each failure condition; where mass imbalance predominantly produces spikes of 1X amplitude in the vibration graph, while misalignment produces harmonic amplitudes of 2X and 3X dimensions. Experimentally obtained root mean square (RMS) acceleration values showed a critical increase (up to 6.0 m/s² under combined misalignment) compared to the baseline healthy condition. The experimental findings were validated using the classical Jeffcott rotor model and kinematic forcing functions, proving that the developed LabVIEW-based system offers a highly accurate, cost-effective, and robust tool for predictive maintenance and engineering training.