Fault diagnosis of a marine power-generation diesel engine based on the Gramian angular field and a convolutional neural network
Congyue Li, Yihuai Hu, Jiawei Jiang, Dexin Cui
Abstract
Marine power-generation diesel engines operate in harsh environments. Their vibration signals are highly complex and the feature information exhibits a non-linear distribution. It is difficult to extract effective feature information from the network model, resulting in low fault-diagnosis accuracy. To address this problem, we propose a fault-diagnosis method that combines the Gramian angular field (GAF) with a convolutional neural network (CNN). Firstly, the vibration signals are transformed into 2D images by taking advantage of the GAF, which preserves the temporal correlation. The raw signals can be mapped to 2D image features such as texture and color. To integrate the feature information, the images of the Gramian angular summation field (GASF) and Gramian angular difference field (GADF) are fused by the weighted average fusion method. Secondly, the channel attention mechanism and temporal attention mechanism are introduced in the CNN model to optimize the CNN learning mechanism. Introducing the concept of residuals in the attention mechanism improves the feasibility of optimization. Finally, the weighted average fused images are fed into the CNN for feature extraction and fault diagnosis. The validity of the proposed method is verified by experiments with abnormal valve clearance. The average diagnostic accuracy is 98.40%. When −20 dB⩽signal-to-noise ratio (SNR)⩽20 dB, the diagnostic accuracy of the proposed method is higher than 94.00%. The proposed method has superior diagnostic performance. Moreover, it has a certain anti-noise capability and variable-load adaptive capability.