Statistical and sensitivity analysis of ultrasound signals for effective condition monitoring of electro-motors using industrial approach

This paper researches the importance of ultrasound methodology for swiftly detecting faults in electric motors and rotating machines. The primary focus of this research is on the intricate signal processing of ultrasound signals from both faulty and fault-free electro-motors. The principal goal is to conduct a comprehensive statistical investigation into signal factors, examining the effects of defect progression on the factors associated with continuously operating faulty electro-motors. In addition to the statistical analysis, this study explores the envelope-frequency spectrum of the signal under both healthy and defective conditions, employing the envelope method alongside Hilbert transformation. The objective is to thoroughly scrutinize the dynamic changes in ultrasound waveform and envelope spectrum of defective states, considering diverse degrees of defect severity over an extended time span. Moreover, the paper meticulously tracks the trajectory of factor changes over a 40-day operational period of a defective electro-motor. Additionally, the study delves into the sensitivity of the ultrasound method to impulse-wise shocks, which are recurrently observed in ultrasound signals, leading to deviations in certain signal factors from their established healthy thresholds. In response to this challenge, this paper conducts a particular analysis of signal factor sensitivity to impulse-wise noises, identifying robust factors that serve as reliable tools for firm condition monitoring. These identified factors are then presented as invaluable contributors to ensuring the precision and reliability of condition monitoring, especially in the presence of disruptive impulse-wise noises. Article Highlights Ultrasound’s Crucial Role: This article delves into the pivotal role of ultrasound in monitoring the health of electric motors and rotating machines, highlighting its significance in detecting defects. Signal Analysis: The study initially focused on analyzing ultrasound signals from healthy and defective motors. The waveform differences between these states were examined, revealing distinct changes when defects were present. Notably, the emergence of intermittent impulses altered the waveform as defect severity increased. Severity Impact: The research uncovered a correlation between defect intensity and the number/intensity of impulses. As defects worsened, the waveform exhibited increased modulation and a higher occurrence of these impulses, aiding in gauging de