This chapter delves into the intricate field of statistical diagnostics for electric power equipment, emphasizing the integration of modern computational and statistical methods rooted in pattern recognition theory. It provides an extensive overview of the Ukrainian power industry, highlighting the distinct roles of thermal, hydro, and nuclear power plants and their associated reliability challenges. Key diagnostic methods—non-destructive testing, vibration, and acoustic emission signals—are discussed, focusing on their critical application in identifying faults in electric machines, particularly winding and bearing damages which contribute to the majority of failures. The chapter also examines the diagnostic processes, touching upon the historical evolution of measurement technologies, the development of diagnostic systems, and compliance with industry standards. Furthermore, it explores mathematical modeling and statistical spline functions for forecasting failures, demonstrating how these methods can enhance the prediction and reliability of industrial equipment by monitoring gradual changes in diagnostic parameters. The comprehensive analysis presented integrates theoretical foundations with practical applications, offering substantial insights into the methodologies for ensuring the optimal functioning and longevity of electrical machinery.