This comprehensive research chapter delves into the application of innovative diagnostic methods for electrical equipment (EE), focusing on vibration and acoustic analyses to detect and diagnose faults in rolling bearings of electric motors (EM) and the technical condition of diesel-electric generators (DEGs). Through extensive experimental setups, the chapter validates probabilistic models for diagnostic signals by utilizing real-world EE units and bespoke test benches. It introduces a specialized installation for analyzing rolling bearing vibrations, demonstrating the significance of frequency characteristics and the impact hammer test in pinpointing natural frequencies crucial for diagnostics. Furthermore, the study explores the use of shock diagnostics and acoustic emission (AE) analysis for evaluating the mechanical loading of EE components, highlighting the effectiveness of vibration maxima, and AE signal coefficients as reliable diagnostic indicators. Employing statistical hypothesis testing, particularly the Neyman-Pearson procedure, the research identifies specific faults in DEG cylinder-piston groups, showcasing the method's potential in early fault detection and preventive maintenance to enhance engine reliability. The findings contribute to the advancement of diagnostic methodologies for EE, promising improved maintenance strategies and operational efficiency.