Application of Machine Learning to Classify the Technical Condition of Marine Engine Injectors Based on Experimental Vibration Displacement Parameters

Abstract

The article presents the possibility of using machine learning (ML) in artificial intelligence to classify the technical state of marine engine injectors. The technical condition of the internal combustion engine and injection apparatus significantly determines the composition of the outlet gases. For this purpose, an analytical package using modern technology assigns experimental test scores to appropriate classes. The graded changes in the value of diagnostic parameters were measured on the injection subsystem bench outside the engine. The influence of the operating conditions of the fuel injection subsystem and injector condition features on the injector needle vibration displacement waveforms was subjected to a neural network (NN) ML process and then tested. Diagnostic parameters analyzed in the amplitude, frequency, and time–frequency domains were subjected after a learning process to recognize simulated various regulatory and technical states of suitability and unfitness with single and complex damage of new and worn injector nozzles. Classification results were satisfactory in testing single damage and multiple changes in technical state characteristics for unfitness states with random wear injectors. Testing quality reached above 90% using selected NNs of Statistica 13.3 and MATLAB R2022a environments.

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“…Jin Yan, Jian-bin Liao, and their colleagues combined second-order convolutional neural networks (QCNN) with audio and vibration signals from bearings, indicating the improved CNNs' ability to diagnose complex parameters such as vibration signals [7]. Jan Monieta, Lech Kasyk, and others achieved a diagnostic accuracy of over 90% for fuel injection systems using neural network (NN) machine learning methods for amplitude and frequency analysis [8]. Caiyun Wu, Kai Zhang, and their team established an improved BAS algorithm on FPGA and verified its feasibility through hardware simulation, demonstrating FPGA's capability for algorithmic computations [9].…”