This chapter explores the advancement in diagnostic signal simulations for electrical equipment, emphasizing acoustic emission (AE) signals, vibration during shock diagnostics, and the detection of rod fastening defects using vibration analysis. The first section introduces a comprehensive approach for simulating diagnostic signals utilizing linear random processes (LRPs), providing a foundation for the efficient analysis and verification of diagnostic features without extensive physical experimentation. It elaborates on the development and application of a simulation model for AE signals in electrical equipment, showcasing how this model facilitates the understanding of signal propagation. Further, the chapter delves into the simulation of AE signals and vibration in electrical equipment under shock diagnostics, highlighting a developed program that generates AE signals mimicking those from a piezoelectric transducer. The program's ability to modify various parameters allows for a detailed analysis of their influence on AE and vibration signals, offering insights consistent with theoretical models and real measurements. Moreover, the study proposes the use of the parameter β, derived from the power spectrum, to detect defects in rod fastening through vibration analysis, marking a significant step towards predictive maintenance and fault diagnosis in mechanical systems. By comparing the β values of suspended and fixed rods, the chapter validates the effectiveness of numerical models in replicating real-world dynamics. The investigation extends to the simulation of failure modes in diesel-electric generator cylinders, suggesting a novel approach to diagnosing specific cylinder failures through amplitude-frequency and phase-frequency characteristics analysis. Collectively, this research presents a valuable contribution to the field of diagnostic signal simulation, offering novel methods and tools for the analysis, verification, and simulation of diagnostic signals in electrical and mechanical systems. The findings promise to enhance the precision of fault diagnostics, reduce dependency on physical experiments, and pave the way for more effective predictive maintenance strategies.