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基于可解释时空图卷积网络的多传感数据融合诊断方法

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随着大数据和人工智能技术的发展,深度学习及其衍化算法在故障诊断领域取得了丰硕成果并得到了广泛应用.然而,智能故障诊断方法也存在着诸多挑战:①现有的多传感数据融合方法难以充分挖掘多传感器间的时空信息来优化诊断性能;②智能诊断模型的决策过程可解释性弱,无法满足实际工业场景下的可靠性要求.因此,提出一种基于可解释时空图卷积网络的多传感数据融合诊断方法.首先,通过构建门控卷积层以学习并增强时间特征;其次,结合传感器空间布局关系和图卷积网络多传感信息融合能力,学习并提取多传感器数据的空间特征,并通过行星齿轮箱故障诊断任务验证了模型有效性;最后,利用改进梯度类激活映射算法分析各传感器数据对模型诊断决策过程的重要度,从而提升模型更新过程的可解释性.试验结果表明,所提方法不仅具有较好的诊断性能,而且为多源传感融合诊断提供了一种有效的事后解释方法.
Multi-sensor Data Fusion Diagnosis Method Based on Interpretable Spatial-temporal Graph Convolutional Network
With the development of big data and artificial intelligence technologies,deep learning and its derived algorithms have achieved fruitful results and have been widely used in the field of fault diagnosis.However,intelligent fault diagnosis methods also face many challenges:1)existing multi-sensor data fusion methods have difficulty fully exploring the spatial-temporal information between multiple sensors to optimize diagnostic performance;2)the decision-making process of intelligent diagnostic models has weak interpretability and cannot meet the reliability requirements in actual industrial scenarios.Therefore,we propose a multi-sensor data fusion diagnostic method based on an interpretable spatial-temporal graph convolutional network(ISTGCN).Firstly,a gate convolution layer is constructed to learn and enhance time features.Secondly,by combining the spatial layout relationship of sensors and the multi-sensor information fusion ability of graph convolutional networks,the spatial features of multi-sensor data are learned and extracted,and the effectiveness of the model is verified through the planetary gearbox fault diagnosis task.Finally,the improved gradient-based activation mapping algorithm is used to analyze the importance of each sensor data to the model diagnostic decision-making process,thereby improving the interpretability of the model update process.The experimental results show that the proposed method not only has good diagnostic performance but also provides an effective post-explanation method for multi-source sensor fusion diagnosis.

intelligent fault diagnosisgraph convolutional networksexplainable artificial intelligenceplanetary gearboxes

温楷儒、陈祝云、黄如意、李东鹏、李巍华

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华南理工大学吴贤铭智能工程学院 广州 511442

人工智能与数字经济广东省试验室 广州 510335

华南理工大学机械与汽车工程学院 广州 510641

智能故障诊断 图卷积网络 可解释性人工智能 行星齿轮箱

国家自然科学基金国家自然科学基金国家自然科学基金国家自然科学基金广东省基础与应用基础研究基金自然科学基金面上中国博士后科学基金

522051005227511152205101U23A206202023A15150128562022M711197

2024

10.3901/JME.2024.12.158

机械工程学报
中国机械工程学会

机械工程学报

CSTPCD北大核心
影响因子：1.362
ISSN：0577-6686
年,卷(期)：2024.60(12)