Framework for Single Misfire Identification in a Marine Diesel Engine using Machine Learning

Abstract

Misfire is a common fault in compression ignition engines, characterized by the absence or flame loss due to insufficient fuel in the cylinders. This fault is difficult to diagnose and resolve due to its multiple potential causes. This study focuses on identifying misfires in a 12-cylinder V-type marine diesel engine by analyzing vibration data collected from 15 accelerometers mounted on the engine block. Three machine learning algorithms—K-Nearest Neighbors (K-NNs), support vector machines (SVMs), and random forests (RFs)—were employed to classify engine conditions using 18 time-domain features. Results showed that the K-NN, SVM and RF algorithms achieved F1 scores of 99.87%, 100%, and 99.87%, respectively, when using 18 time-domain features and all 15 accelerometers mounted on the engine block. Additionally, the study evaluated classification performance while reducing the number of accelerometers and features using two methods: Relief-F and general combinatory analysis (GCA). Although the GCA method yields better results when using only two accelerometers and nine features for misfire classification, its overall process required substantially more computational time compared to Relief-F. The best result obtained with Relief-F was achieved using 3 accelerometers and 18 features. Therefore, Relief-F proved to be more practical and take less overall computational time within the proposed framework.

Keywords

Misfire fault / Vibration / Marine diesel engine / K-NN / SVM / Random forest

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Harbin Engineering University and Springer-Verlag GmbH Germany, part of Springer Nature