Design and Development of an Intelligent DC Motor Health and Fault Diagnosis System Using NI LabVIEW

Condition monitoring of electrical machines is es-sential for improving operational reliability, reducingunexpected downtime, and enabling predictive maintenance. Direct current (DC) motors are widely used inindustrial drives, robotics, electric traction, and automated systems, where degradation of electrical andmechanical components can adversely affect performance. This paper presents the design and development of anintelligent DC motor health and fault diagnosis system using the National Instruments LabVIEW graphicaldevelopment environment. The proposed system acquires multiple motor parameters, including armature current,mechanical vibration, rotational speed, and temperature, through a multi-channel data acquisition interface. Aqueue-based Producer–Consumer architecture is employed to separate continuous data acquisition fromcomputationally intensive signal-processing operations. The acquired current and vibration signals are subjectedto digital filtering and frequency-domain analysis using the Fast Fourier Transform (FFT). Time-domain statisticalfeatures such as root mean square (RMS), peak value, kurtosis, and crest factor are extracted to characterize motoroperating conditions. The extracted features are subse-quently supplied to an intelligent classification layer basedon fuzzy logic and/or a machine-learning classifier. The diagnosed motor condition is displayed through aLabVIEW graphical user interface using numerical indicators, waveform charts, fault indicators, and thresholdbased alarms. The proposed architecture provides a modular framework for real-time motor condition monitoringand automated fault diagnosis and can be extended for experimental validation under different electrical andmechanical fault conditions.