论文标题
一种声学机器学习方法，用于确定宽皮带砂光机的磨石皮带磨损
An Acoustical Machine Learning Approach to Determine Abrasive Belt Wear of Wide Belt Sanders
论文作者
论文摘要
本文介绍了一种机器学习方法，以确定基于声学数据的工业过程中使用的宽带砂光机的磨蚀带磨损，而与打磨过程相关的参数，进料速度，砂砾大小和材料类型，无论是什么。我们的方法利用决策树，随机森林，最近的邻居和神经网络分类器来检测光谱图，MEL频谱，MFCC，IMFCC和LFCC的皮带磨损，可在五个级别的皮带磨损上获得高达86.1％的精度。使用不同的决策树分类器可以实现96％的精度，专门从事不同的打磨参数配置。如果机器当前正在打磨或闲置并且精度为98.4％和98.8％，则分类器还可以准确地确定97％的精度，并且检测到砂纸参数的进料速度和砂砾大小。我们可以证明，高维特征的低维映射可用于使皮带磨损和打磨参数有意义地可视化。
This paper describes a machine learning approach to determine the abrasive belt wear of wide belt sanders used in industrial processes based on acoustic data, regardless of the sanding process-related parameters, Feed speed, Grit Size, and Type of material. Our approach utilizes Decision Tree, Random Forest, k-nearest Neighbors, and Neural network Classifiers to detect the belt wear from Spectrograms, Mel Spectrograms, MFCC, IMFCC, and LFCC, yielding an accuracy of up to 86.1% on five levels of belt wear. A 96% accuracy could be achieved with different Decision Tree Classifiers specialized in different sanding parameter configurations. The classifiers could also determine with an accuracy of 97% if the machine is currently sanding or is idle and with an accuracy of 98.4% and 98.8% detect the sanding parameters Feed speed and Grit Size. We can show that low-dimensional mappings of high-dimensional features can be used to visualize belt wear and sanding parameters meaningfully.
