珩磨网纹对维持活塞-缸套间润滑状态至关重要，磨损造成的网纹深度变浅会引起润滑恶化并加剧摩擦磨损，甚至造成拉缸等重大机械事故。提出一种基于声发射的缸套珩磨网纹磨损深度检测方法，从声发射信号中识别提取了网纹深度有关特征变量，通过构建卷积神经网络模型建立了声发射特征信号与网纹深度的映射关系。基于长行程往复式摩擦试验机，对不同网纹简约谷深的缸套试样在变转速、变载荷工况下实测的声发射信号进行采集分析，发现网纹简约谷深与声发射信号低频和高频段存在较强相关性。基于所建模型对早期缸套网纹磨损进行了检测评估，结果表明：珩磨网纹深度识别平均准确率达94%，证实了基于声发射技术进行缸套网纹磨损深度非介入式检测的可行性。

Honing mesh is crucial for maintaining the lubrication state between piston and cylinder liner, and shallowed mesh caused by wear will cause lubrication deterioration and intensified friction and wear, and even major mechanical accidents such as cylinder pulling. An acoustic emission-based method for detecting the wear depth of honing mesh on cylinder liner was put forward, which identified and extracted the feature variables related to the depth of mesh from the acoustic emission signals, and established the mapping relationship between the acoustic emission feature signals and the depth of mesh by constructing a convolutional neural network model. Based on a longstroke reciprocating friction tester, acoustic emission signals of honing mesh specimens with different depths were collected and analyzed under variable rotational speeds and loads, and it was found that there was a significant correlation between the depth of honing mesh and the response characteristics of the acoustic emission signals in the low-frequency and highfrequency regions. Based on the proposed model, the detection and evaluation of earlier wear failure on mesh was carried out. The results showed that the average accuracy of honing mesh depth recognition reached 94%, which verified the feasibility of non-intrusive detection of mesh wear depth based on acoustic emission.