Ship Diesel Engine Fault Diagnosis Using Data Science and Machine Learning

Abstract

One of the most important elements of the reliability structure of a motor vessel is its power subsystem, with the most crucial component being the engine. An engine failure excludes the ship from operation or significantly limits its operation. Therefore, accurate fault diagnosis should be a crucial issue for modern maintenance strategies. In mechanical engineering, the vibration and acoustic signals recorded during the operation of the device are the most meaningful data used to identify the reliability state. In this paper, a novel system-oriented method of reliability state identification is proposed. The method consists of the analysis of the vibration and noise signals collected on each of the engine cylinders using supervised machine learning. The main novelty of this method is data augmentation application and SVM classifier implementation. Due to these aspects, the method becomes robust in the case of poor-quality data or a limited and incomplete learning dataset. The quality of the proposed identification method was evaluated by addressing a new industrial issue (Sulzer 6AL20/24 marine engine reliability state identification). During the tests, the efficiency of the method was analyzed in the case of a complete learning data set (all types of inability states were presented in the learning data set) and an incomplete learning data set (in the testing data set, there were new types of inability states). As a result, in both cases, a very high (100%) identification accuracy of the reliability state and the type of inability state was obtained. This is a significant increase in accuracy (4.6% for the complete and 22% for the incomplete learning data set) in comparison to the efficiency of the same method without the use of machine learning and data science.

Improved Generative Adversarial Network for Bearing Fault Diagnosis with a Small Number of Data and Unbalanced Data

“…The reduction rate r is chosen as 32 to reduce the computational overhead. Additionally, two separate 1 × 1 convolutional transforms, f h and f W , are applied to convert f h and f W into tensors with the same number of channels as X, which can be obtained as follows in Equations ( 14) and (15), respectively:…”

“…The GAN and its variants have received more and more attention in the field of fault diagnosis [12][13][14]. In order to solve the problem of the poor quality of one-dimensional data and limited learning datasets, Pajak et al [15] performed data augmentation by slicing and randomly disrupting the original one-dimensional time domain signal. Yang et al [16] addressed the issue of insufficient bearing data by first converting the original vibration signals into grayscale images.…”

Unmanned Surface Vehicle Thruster Fault Diagnosis via Vibration Signal Wavelet Transform and Vision Transformer under Varying Rotational Speed Conditions

“…Also, periodic maintenance, overhauls, and regulations of necessary replacement parts on board enable a ship to maintain long-term operation and maintenance [ 18 ]. However, most Unmanned Surface Vehicles (USVs) follow scheduled maintenance and reactive strategies, and most fault-related research is heavily focused on commercial-class surface vessels [ 19 , 20 , 21 , 22 ].…”

Fault diagnosis and identification of rotating machinery based on one-dimensional convolutional neural network

“…Compared to frequency domain analysis methods, more researchers believe that time-domain analysis methods are more effective. For example, Marcin Kluczyk et al believed that in the time domain, the classification based fault diagnosis is used as a method for vibration analysis [16]. Time-domain analysis method was applied to the signals of the engine vibrations and noise recorded during the engine operation on a laboratory test stand [17][18].…”