Methodology for hardware diagnostics using an information technology of digital twin

This paper presents a novel methodology for diagnosing hardware systems based on the use of the digital twin by using a vector-based representation of both input and output values. The method involves comparing the output vector of a real electronic device with a corresponding reference vector derived from a mathematical model (digital twin). Deviations between these vectors are analyzed using a specially designed table of discrete qualitative correspondences, which enables identification of the likely causes of incorrect behavior in specific components without requiring intrusive measurements or additional sensors.The approach models the behavior of an electronic system as a transformation from an input vector to an output vector, where each deviation in input causes a deterministic change in output. This vector-based abstraction allows for the construction of decision rules and classification zones using relative relationships between components rather than absolute values, making the system robust to noise, parasitic effects, and measurement uncertainties. The proposed method is computationally efficient, allowing deployment on low-power embedded platforms, and does not rely on deep physical modeling or extensive machine learning training datasets. The methodology introduces the concept of a “gray zone” in the multidimensional output space, where small deviations are treated as acceptable tolerances, and only significant directional changes trigger diagnostic logic. Additionally, a normal-ization step and the introduction of threshold vectors allow for scalable precision and adaptation to non-linear dependencies in hardware behavior.Advantages of this approach include simplicity of implementation, human readability of diagnostic outputs (e.g., “+”, “++”, “–”, “--”), low computational requirements, and high resilience in noisy or constrained environments. Moreover, the system enables real-time monitoring and provides a foundation for future extensions such as remaining useful life (RUL) pre-diction and adaptive table generation through machine learning.The methodology is particularly suited for power electronics and embedded systems, but can be generalized to any ap-plication where the device can be abstracted as a system with measurable input/output behavior. This work provides a flexible, scalable, and interpretable diagnostic framework that relies on vector mappings within a model-based digital twin approach, enabling robust and low-cost fault detection without the need for high-precision measurements, extensive sensor networks, or machine learning.