(untitled) (Eureka Journal of Education & Learning Technologies, 2026)
In contemporary industrial setups, signal diagnostics forms the core of equipment condition monitoring. While traditional spectral techniques like the Fourier transform excel at stationary signal analysis, real-world production signals are typically time-variant, impulsive, and nonlinear. This research introduces a multiscale energy-based diagnostic framework leveraging wavelet transformation, with its mathematical robustness, detection sensitivity, and precise localization rigorously established through functional analysis tools. The method outperforms conventional techniques in pinpointing local signal singularities. Simulation outcomes confirm the diagnostic metric’s invariance to noise variance alongside its quadratic responsiveness to impulse perturbations. It offers a solid theoretical groundwork for predictive diagnostics in robotic production lines, pneumatic conveyors, and high-speed rotors.