Identification of wear mechanisms of main bearings of marine diesel engine using recurrence plot based on CNN model

Abstract

Wear failure of the main bearings on marine diesel engines is difficult to diagnose online, especially to identify the wear mechanisms. To solve the problem, the recurrence plot (RP) was used to characterize the nonlinear characteristics of vibration signals. It was combined with convolutional neural networks (CNN), and the RP-CNN method was proposed. The new method was also compared with six common machine learning methods. Results show that the diagnostic accuracy of the RP-CNN method is more than 96% under five typical working conditions, which is better than the other methods. The confusion matrix shows that the normal state and the three mechanisms of fatigue wear, abrasive wear, and adhesive wear can be effectively identified.