It is increasingly important to monitor sliding interfaces within machines, since this is where both energy is lost, and failures occur. Acoustic emission (AE) techniques offer a way to monitor contacts remotely without requiring transparent or electrically conductive materials. However, acoustic data from sliding contacts is notoriously complex and difficult to interpret. Herein, we simultaneously measure coefficient of friction (with a conventional force transducer) and acoustic emission (with a piezoelectric sensor and high acquisition rate digitizer) produced by a steel–steel rubbing contact. Acquired data is then used to train machine learning (ML) algorithms (e.g., Gaussian process regression (GPR) and support vector machine (SVM)) to correlated acoustic emission with friction. ML training requires the dense AE data to first be reduced in size and a range of processing techniques are assessed for this (e.g., down-sampling, averaging, fast Fourier transforms (FFTs), histograms). Next, fresh, unseen AE data is given to the trained model and the resulting friction predictions are compared with the directly measured friction. There is excellent agreement between the measured and predicted friction when the GPR model is used on AE histogram data, with root mean square (RMS) errors as low as 0.03 and Pearson correlation coefficients reaching 0.8. Moreover, predictions remain accurate despite changes in test conditions such as normal load, reciprocating frequency, and stroke length. This paves the way for remote, acoustic measurements of friction in inaccessible locations within machinery to increase mechanical efficiency and avoid costly failure/needless maintenance.

监控机器内的滑动界面变得越来越重要，因为这是能量损失和故障发生的地方。声发射 (AE) 技术提供了一种远程监控接触的方法，无需透明或导电材料。然而，滑动接触的声学数据非常复杂且难以解释。在这里，我们同时测量钢与钢摩擦接触产生的摩擦系数（使用传统的力传感器）和声发射（使用压电传感器和高采集率数字转换器）。然后，获取的数据用于训练机器学习 (ML) 算法（例如高斯过程回归 (GPR) 和支持向量机 (SVM)），以将声发射与摩擦相关联。 ML 训练要求首先减小密集 AE 数据的大小，并为此评估一系列处理技术（例如下采样、平均、快速傅立叶变换 (FFT)、直方图）。接下来，将新的、未见过的 AE 数据提供给训练模型，并将所得摩擦预测与直接测量的摩擦进行比较。当 GPR 模型用于 AE 直方图数据时，测量摩擦力与预测摩擦力之间具有极好的一致性，均方根 (RMS) 误差低至 0.03，皮尔逊相关系数达到 0.8。此外，尽管正常负载、往复频率和行程长度等测试条件发生变化，预测仍然准确。这为在机械内难以接近的位置进行远程声学摩擦测量铺平了道路，以提高机械效率并避免代价高昂的故障/不必要的维护。