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.
概要
目的
船用发电柴油机工作环境恶劣, 在内外多激励源的干扰下, 振动信号呈现非线性非平稳性的特点。本文旨在对船舶发电柴油机的振动信号进行有效特征提取并准确识别故障类型。同时, 研究所提方法的有效性, 以提高船舶发电柴油机的故障诊断精度。
创新点
1. 一维振动信号可通过格拉姆角场转换为二维图像;一维振动信号可以映射到二维图像的颜色、点、线和其他特征;为充分利用故障特征信息, 将格拉姆角和场和格拉姆角差场获得的图像进行加权平均融合。2. 利用多注意力机制来优化卷积神经网络学习机制, 使网络有选择地提取信号中的关键特征信息。
方法
1. 对船舶发电柴油机的气阀间隙进行故障预设, 采集柴油机不同健康状态振动信号。2. 将振动信号转化为二维图像, 并将格拉姆角和场和格拉姆角差场获得的图像进行加权平均融合, 以充分利用原信号中的故障特征信息。3. 将融合后的图像输入到卷积神经网络中进行自适应特征提取和故障识别。
结论
1. 所提方法可准确识别故障类型,平均诊断精度可达98.40%;与其他方法相比,所提方法具有更高的故障诊断精度。2. 在不同信噪比下, 所提方法与无注意力机制方法相比, 准确精度可提高14.80%。3. 融合后的图像可为神经网络提供更充足的特征信息,因此具有更高的故障识别精度。4. 变负荷实验中, 所提方法的准确率均保持在89.00%以上, 进一步验证了所提方法的稳定性。
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Acknowledgments
This work is supported by the Project of Shanghai Engineering Research Center for Intelligent Operation and Maintenance and Energy Efficiency Monitoring of Ships (No. 20DZ2252300), China. We sincerely thank Dexin CUI (the superintendent of Ningbo Ocean Shipping Co., Ltd., China) for providing experimental guidance.
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Congyue LI and Yihuai HU designed the research. Congyue LI wrote the first draft of the manuscript. Jiawei JIANG and Dexin CUI helped to organize the manuscript. Congyue LI and Yihuai HU revised and edited the final version.
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Congyue LI, Yihuai HU, Jiawei JIANG, and Dexin CUI declare that they have no conflict of interest.
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Li, C., Hu, Y., Jiang, J. et al. Fault diagnosis of a marine power-generation diesel engine based on the Gramian angular field and a convolutional neural network. J. Zhejiang Univ. Sci. A (2024). https://doi.org/10.1631/jzus.A2300273
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DOI: https://doi.org/10.1631/jzus.A2300273
Key words
- Multi-attention mechanisms (MAM)
- Convolutional neural network (CNN)
- Gramian angular field (GAF)
- Image fusion
- Marine power-generation diesel engine
- Fault diagnosis