## 1 Introduction
An appropriately chosen set of diagnostic parameters ensures higher sensitivity in detecting even subtle changes in engine performance quality. A comprehensive overview of vibroacoustic techniques and their practical applications is provided in review articles [11, 12, 13]. The authors of these works emphasize the advantages of non-invasive methods that allow for regular diagnostics without the need to disassemble engine components. They also highlight the growing role of diagnostic systems that continuously monitor engine performance, enabling preventive maintenance scheduling and repairs. In studies concerning the diagnosis of internal combustion engines, authors often use advanced signal processing methods as well as machine learning [14]. In this paper, a decomposition technique based on improved variational mode decomposition and the random forest algorithm was applied. Engine diagnostic techniques based on vibration measurement should take into account the non-stationarity of signals. The authors in the work [15] considered features such as fractal correlation dimension, wavelet energy, and entropy extracted from the decomposed vibration acceleration signals obtained from the cylinder head. The FastICA-SVM classifier was used for state classification. The study [16] utilizes two methods for diagnosing faults in internal combustion engines. The first method involves extracting classical signal features from engine vibration measurements and using them to train artificial neural networks and support vector machines. The second method leverages convolutional neural networks (CNNs) and spectrograms of engine vibration signals, obtained using short-time Fourier transform (STFT). In the study [17], vibration signals and acoustic emission were utilized, along with time-domain, frequency-domain, and time-frequency analysis, to extract statistical feature parameters. Subsequently, an artificial neural network was used to predict and classify faults in engine components. In the work [18], the significance of studying in-cylinder pressure was emphasized, as it provides insights into the combustion process. Due to direct costs, pressure measurements during operation are not applied. Instead, the possibility of reconstructing pressure based on acoustic emission signals was presented. The study utilized a neural network. A similar approach can be found in the work [19]. In this article, autoregressive models and polynomial fitting were applied for pressure reconstruction. In summary, existing research clearly demonstrates the high potential of vibroacoustic methods in evaluating diesel engine performance quality. Nevertheless, the authors agree that further development of these methods is necessary, particularly in the area of advanced data analysis algorithms, integration of signal processing techniques, and the implementation of effective diagnostic solutions in practical operation. Continued research in this field is therefore justified both scientifically and practically. As indicated by the literature review, the topic of non-invasive methods for diagnosing engine condition is developing and remains relevant, hence the need for further research. The significance of leveraging vibration signals for engine diagnostics has inspired growing interest in applying modern data analysis and AI-based reconstruction techniques, as demonstrated in this work. This study introduces a new methodology within the field of vibroacoustic diagnostics, specifically focusing on the combustion process in internal combustion engines. It explores the diagnostic value of vibration signals by leveraging their embedded information about in-cylinder pressure during the fuel-air combustion cycle. The method proposed generates pressure signal profiles based on vibration data captured directly from the engine's cylinder head and a deep learning-based signal-to-signal reconstruction technique. Our approach offers a robust and high-resolution tool to infer internal pressure profiles from easily obtainable vibration signals.