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首页> 外文期刊>Journal of Manufacturing Processes >An online belt wear monitoring method for abrasive belt grinding under varying grinding parameters
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An online belt wear monitoring method for abrasive belt grinding under varying grinding parameters

机译：不同磨削参数下砂带磨削的在线砂带磨损监测方法

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摘要

Abrasive belt grinding has attracted attention in recent years in both industry and academia due to the rapid development of abrasive belts; however, online monitoring of abrasive belt wear under varying grinding parameters is challenging. In this paper, the multi-sensor fusion of sound and current signals is used to resolve the abovementioned problem. First, the characteristics of the grinding sound and current are investigated in the time-domain and frequency-domain, and then their differences under various belt wear states and grinding parameters are discussed; based on the investigation, several features are extracted that indicate the belt wear state. The abovementioned discussion demonstrated that grinding sound signals have an abundance of information at high frequencies (6-20 kHz); in contrast, grinding current signals contain information at low frequencies. Furthermore, the grinding sound and current signals have different sensitivities to the grinding parameters and belt wear. Finally, a Bayesian network is proposed to identify the wear state; moreover, its adaptability under changing parameters and the effect of mull-sensor fusion are discussed. The results show that the accuracy reaches 100% with enough training data; additionally, when the training data only covers a limited range of grinding parameters, the fusion of the sound and current substantially improves the accuracy of the prediction results from 86% to 95%.
机译：近年来，由于砂带的快速发展，砂带磨削在工业界和学术界都引起了人们的关注。但是，在变化的磨削参数下在线监测砂带的磨损是一项挑战。在本文中，声音和电流信号的多传感器融合被用来解决上述问题。首先，在时域和频域研究了磨削声和电流的特性，然后讨论了在各种皮带磨损状态和磨削参数下它们的差异。基于调查，提取了一些指示皮带磨损状态的特征。上述讨论表明，研磨声信号在高频（6-20 kHz）下具有丰富的信息。相反，磨削电流信号包含低频信息。此外，磨削声和电流信号对磨削参数和砂带磨损的敏感性不同。最后，提出了一种贝叶斯网络来识别磨损状态。此外，还讨论了其在变化的参数下的适应性以及鸥传感器融合的效果。结果表明，训练数据足够时，准确率达到100％。此外，当训练数据仅涵盖有限范围的磨削参数时，声音和电流的融合将预测结果的准确性从86％提高到95％。

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