This comprehensive research explores the application of the linear periodic random processes (LPRP) and the linear random processes (LRP) models’ parameters in diagnosing and understanding the technical conditions and operational issues of diesel engine generators (DEG) and electric machines (EM). By integrating these models, the study aims to capture the cyclical and stochastic nature of the operational behaviors of EM and their components, particularly focusing on the vibrations and rotational dynamics of DEGs and EMs. Through the analysis of the uneven rotation of the crankshaft and the distribution of cylinder power, the research demonstrates how LPRP and LRP can be effectively used to diagnose potential malfunctions and optimize the performance of these systems. The methodology includes measuring the kinetic energy of the shaft, calculating acceleration deviations, and applying discrete Fourier transform to identify harmonics indicative of operational integrity or issues. The findings suggest that the absence or presence of specific harmonics can diagnose uneven cylinder power distribution, crucial for maintaining efficient and reliable operation. This chapter extends the application of LPRP and LRP in analyzing stochastically periodic behavior, offering significant insights for enhancing diagnostic techniques for DEGs and EMs, marking a pioneering step in applying periodic random processes to mechanical diagnostic fields.