International Journal of Science, Engineering and TechnologyAuthors: Nithin Aadharsh S, Shreeniva BA, Dr. Saravanakumar R
Abstract: Condition monitoring of electrical machines is es-sential for improving operational reliability, reducing unexpected downtime, and enabling predictive maintenance. Direct current (DC) motors are widely used in industrial drives, robotics, electric traction, and automated systems, where degradation of electrical and mechanical components can adversely affect performance. This paper presents the design and development of an intelligent DC motor health and fault diagnosis system using the National Instruments LabVIEW graphical development environment. The proposed system acquires multiple motor parameters, including armature current, mechanical vibration, rotational speed, and temperature, through a multi-channel data acquisition interface. A queue-based Producer–Consumer architecture is employed to separate continuous data acquisition from computationally intensive signal-processing operations. The acquired current and vibration signals are subjected to digital filtering and frequency-domain analysis using the Fast Fourier Transform (FFT). Time-domain statistical features such as root mean square (RMS), peak value, kurtosis, and crest factor are extracted to characterize motor operating conditions. The extracted features are subse-quently supplied to an intelligent classification layer based on fuzzy logic and/or a machine-learning classifier. The diagnosed motor condition is displayed through a LabVIEW graphical user interface using numerical indicators, waveform charts, fault indicators, and threshold-based alarms. The proposed architecture provides a modular framework for real-time motor condition monitoring and automated fault diagnosis and can be extended for experimental validation under different electrical and mechanical fault conditions.
DOI: https://doi.org/10.5281/zenodo.23793747
