Stepper motors are critical elements in industrial automation because of their accurate motion control, but they are susceptible to faults that compromise performance and lead to expensive downtimes. Despite earlier journal works e.g., by Neuzil et al. (2013), McInerny and Dai (2003), Basak et al. (2006), and Shreve (1995) pushed the bounds of vibration-based fault detection, most of those works do not have complete fault classification, real-world validation, and rigorous comparison of sophisticated signal processing methods. To address these limitations, the current research introduces an integrated approach by merging Fast Fourier Transform (FFT), Power Spectral Density (PSD), and real-time IoT-based monitoring to improve early fault detection of faint fault signals in stepper motors and to distinguish between transient and permanent faults effectively under different operating conditions. Experimental results, presented in graphical and tabular comparisons, show the better performance of the proposed method. In addition, the paper presents a detailed future work plan, specifying practical constraints like sensor variability and environmental noise, and theoretical issues like model generalizability and computational complexity. These contributions provide a solid basis for improving predictive maintenance approaches in industrial contexts.