SIGNAL PROCESSING PATHWAYS AND STRATEGIES FOR ACCURATE AND EFFICIENT VIBRATION DIAGNOSTICS

Vibration analysis is a critical diagnostic tool in modern engineering, enabling the monitoring, detection, and prediction of faults in mechanical systems.Central to this process is the application of digital signal processing (DSP) techniques that extract meaningful information from raw vibration data while minimizing noise and distortion.This paper provides a comprehensive review of the signal processing path in vibration analysis, from the acquisition of time-domain signals through sensors to their transformation into frequency-domain representations.Key methods such as Fast Fourier Transform (FFT), filtering, windowing, averaging, and antialiasing are examined in detail, along with their practical implications for resolution, accuracy, and computational efficiency.The study also discusses challenges including aliasing, integration errors, leakage, and trade-offs between frequency and amplitude resolution.Furthermore, advanced approaches such as overlap averaging, real-time processing, and power/band calculations are explored for their role in improving diagnostic reliability.By linking signal processing parameters to machine fault characteristics, this paper highlights how effective vibration analysis can facilitate early fault detection, reduce downtime, and improve safety and cost-efficiency across industries.