Although many advanced signal processing techniques and novel machine learning algorithms have been applied to the monitoring of grinding processes in the literature, most of these techniques and algorithms are only effective under specific conditions and are unusable under other grinding conditions, such as varying wheel types or workpiece materials. This article proposes a robust grinding wheel wear monitoring system to eliminate these restrictions. Physical information generated during the grinding process is collected by a power sensor, accelerometers, and acoustic emission sensors. After the signals are preprocessed, features are extracted via different signal processing techniques, and a novel normalization scheme is applied to make these features independent of the wheel type, workpiece material, and grinding parameters. The features that are most related to wheel wear are selected according to the statistical criterion. An interval type-2 fuzzy basis function network is adopted to develop a wheel wear monitoring model, which is capable of predicting wheel wear under various grinding conditions and generating upper and lower prediction bounds according to the fluctuation of features. Based on the wheel wear model, a robust monitoring scheme to schedule timely wheel dressing and ensure workpiece surface finish could be established.

中文翻译：

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圆柱横磨的鲁棒轮磨损监测系统

尽管文献中已将许多先进的信号处理技术和新颖的机器学习算法应用于磨削过程的监控，但是这些技术和算法中的大多数仅在特定条件下才有效，而在其他磨削条件下（例如变化的砂轮类型或工件材料。本文提出了一种鲁棒的砂轮磨损监测系统，以消除这些限制。在磨削过程中生成的物理信息由功率传感器，加速计和声发射传感器收集。对信号进行预处理后，可通过不同的信号处理技术提取特征，然后采用新颖的归一化方案使这些特征与砂轮类型，工件材料和磨削参数无关。根据统计标准选择与车轮磨损最相关的特征。采用区间2型模糊基函数网络建立砂轮磨损监测模型，该模型能够预测各种磨削条件下的砂轮磨损，并根据特征的波动产生上下预测界限。基于砂轮磨损模型，可以建立一个强大的监控方案来安排及时的砂轮修整并确保工件表面光洁度。它能够预测各种磨削条件下的车轮磨损，并根据特征的波动生成上下预测界限。基于砂轮磨损模型，可以建立一个强大的监控方案来安排及时的砂轮修整并确保工件表面光洁度。它能够预测各种磨削条件下的车轮磨损，并根据特征的波动生成上下预测界限。基于砂轮磨损模型，可以建立一个强大的监控方案来安排及时的砂轮修整并确保工件表面光洁度。