This chapter explores the implementation and effectiveness of statistical diagnostics systems for monitoring and diagnosing electrical equipment (EE) components using information-measuring systems (IMSs). The chapter delves into the limitations of traditional diagnostic methods and how IMSs, through advanced data analysis and automation, overcome these limitations. By proposing a generalized structure for IMSs ‒ incorporating sensors, processing units, and software components, the chapter showcases specific applications such as diagnosing rolling bearings and various other EE units including internal combustion engines and electrical machines. It emphasizes the role of statistical hypothesis testing, linear random process models, and the critical use of training sets in constructing accurate diagnostic decision rules. Experimental examples, including shock and vibration diagnostics, illustrate the system's capabilities in real-world applications. Through detailed analysis of errors including methodological, instrumental, discretization, and quantization errors, the chapter presents calculation relations for metrological control, thereby enhancing diagnostic accuracy. This comprehensive overview confirms the potential of IMSs to significantly improve the diagnostic processes of EE, highlighting the importance of statistical methods, advanced data processing, and the strategic selection of diagnostic signals in reducing errors and improving reliability.