## 1 Introduction  
Despite increasingly stringent regulations concerning air pollution caused by toxic and non-toxic compounds, particularly those contributing to global warming, internal combustion piston engines continue to be used for powering transport vehicles and electric generators. Employing advanced fuels or adapting engines to burn hydrogen or ammonia significantly reduces their environmental impact. In energy farms, they complement renewable electricity sources as additional power generators. They hold an advantage over turbine units by achieving operational readiness more rapidly. As internal combustion piston engines are projected to remain a key solution for converting chemical energy into mechanical work in the foreseeable future, it becomes increasingly important to develop and refine reliable diagnostic techniques for their maintenance and performance monitoring. Numerous methods have been developed to diagnose the technical condition of internal combustion engines. These can be divided into methods using operational processes (indicating torque variations as a function of crankshaft rotation, measurement of exhaust gas pressure and temperature, pressure above and below the piston, fuel supply parameters, exhaust smoke, etc.) and residual processes (vibrations, noise, and thermal and electrical processes, among others). Investigations based on operational processes enable conclusions about the engine's overall condition, while residual processes provide insights into the condition of individual components and kinematic pairs. Therefore, residual processes are utilized as autonomous or supplementary diagnostic methods [1,2]. Mechanical vibrations produced during engine operation offer highly informative signals for diagnosing the technical state. A major benefit of such signals is their ability to reflect condition changes with little to no delay. Analysis of the current literature indicates the growing importance of vibroacoustic methods in the diagnostics of internal combustion engine performance, particularly diesel engines. Publications [3,4] present the application of vibration signal analysis and acoustic emission (AE) monitoring to assess the general technical condition of power units. The authors highlight that such methods are highly effective in detecting combustion process anomalies, injection system faults, and other irregularities, whose early detection contributes to improved operational efficiency and engine durability. Building upon these insights, our work aims to enhance diagnostic fidelity by reconstructing internal pressure signals--commonly requiring invasive or expensive measurement tools--using only external vibration data and deep learning models. An essential application area of vibroacoustic diagnostics is the monitoring of combustion process quality. Authors of studies [5,6] utilize wavelet transform to evaluate combustion parameters such as the injection start timing, flame front development, and fuel system performance. They indicate that multi-resolution analysis enables precise extraction of key components from the vibration signal, significantly enhancing fault detection sensitivity and enabling more effective engine operation optimization. The implementation of vibroacoustic diagnostics under real-world conditions, particularly during engine operation, is discussed in the publication [7]. The work describes techniques for continuous, parameter-based diagnostics of marine engines, conducted in real time. The application of such methods allows operators to react immediately to any changes in the engine's performance characteristics, directly reducing the risk of serious failures and minimizing operational downtime costs. A crucial factor in enhancing the effectiveness of vibroacoustic methods is the proper selection of signal analysis parameters and indicators. Authors of publications [8, 9, 10] emphasize the need for detailed selection of diagnostic parameters such as amplitude, envelope analysis, statistical measures, and time-frequency characteristics.