## 1 Introduction
Therefore, an essential issue in engine vibration tests is the appropriate interpretation of complex measurement signals through increasingly more perfect signal processing methods [19]. The main tasks in diagnosis include the separation of useful signals and the determination of the characteristic parameters of the processed signals sensitive to faults. The imperfection of operational tests is the difference between the instant of occurrence of the unfitness and its finding. Due to the severe secondary failure, selecting the injection apparatus for diagnostics is expedient [9]. The advantage of the proposed method is that it detects damage before the failure and, based on the prediction model, determines the time remaining for a critical failure. The difference between the proposed work and others in the literature is the presentation of the application of selected advanced signal processing methods, including classification and machine learning in neural networks. [PERSON] et al. [20] used classical signal analysis to diagnose injector faults in the time and frequency domain. [PERSON] [21] presented the study assumptions of the identification of slowly varying energy processes in high-cycle fatigue of structural materials of a diagnostic ship propulsion system. In addition, the author presented the results of preliminary experimental tests conducted on a Schenck fatigue test machine using vibration acceleration measurements, acoustic emissions, and infrared radiation temperature. [PERSON] et al. [8] indicated that changes in mild steel and high-strength steel samples cause changes in the observed acoustic emission signals. Machine learning and classification have also been used to diagnose damage development in internal combustion engines. [PERSON] and [PERSON] [22] presented the use of a vibration signal to classify valve clearance using machine learning. Among other factors, the authors developed a classifier as a neural network and a \(k\)-nearest neighbor algorithm. Instability, nonlinearities, significant noise, and unstable operating conditions characterize diesel engine vibration signals. [PERSON] et al. [23] proposed a method of improved wavelet packet frequency and convolutional neural network to diagnose valve train faults. They developed time-frequency distribution feature matrices with mean packet-Mel frequency cepstral coefficient and wavelet distribution frequency bands. The authors also used a hierarchical structure and vibration signal state neurons. The study's results validated the method's efficiency in the diagnosis of the valve clearance of self-ignition engines. The largest portion, i.e., around 59%, of undesirable events in marine power plants are damage to the main and auxiliary engines. Removing such effects accounted for 34% of all costs incurred in the period under review [9; 24]. [PERSON] et al. [25] applied the Bayesian neural network model to a marine self-ignition engine's performance prognostics to monitor its state. The Bayesian logistic regression model quantifies the development damage process of the marine compression ignition engine. The authors showed that the proposed approach is superior to the other online technical state monitoring methods for prognostics. [PERSON] et al. [26] compared artificial neural networks (ANNs) and support vector machines (SVMs) in their study on predicting the response of marine diesel engines supported by a common rail direct injection system. The authors demonstrated that SVM was well suited for predicting engine response with limited, expensive experimental data. [PERSON] et al. [27] described the analysis and causes of the cracking of the nozzle body of marine self-ignition engines. In a cause-effect study, the authors applied the theory of inventive problem solving (TRIZ) for various damage hypotheses. Modern marine diesel engines have a computerized control unit, which usually has self-diagnostic capability. However, marine systems have no prevailing standard, and obtaining condition information requires expensive equipment.