Faculdade de Engenharia, UNESP-University Estadual Paulista, Bauru, Departamento de Engenharia Elétrica, Av. Eng. Luiz Edmundo C. Coube 14-01, 17033-360 Bauru⁻SP, Brazil.
Dipartimento di Ingegneria Chimica, Università degli Studi di Napoli Federico II, dei Materiali e della Produzione Industriale; 80138 Napoli NA, Italy.
Sensors (Basel). 2018 Dec 16;18(12):4453. doi: 10.3390/s18124453.
This paper presents an approach for impedance-based sensor monitoring of dressing tool condition in grinding by using the electromechanical impedance (EMI) technique. This method was introduced in Part 1 of this work and the purpose of this paper (Part 2) is to achieve an optimal selection of the excitation frequency band based on multi-layer neural networks (MLNN) and k-nearest neighbor classifier (-NN). The proposed approach was validated on the basis of dressing tool condition information obtained from the monitoring of experimental dressing tests with two industrial stationary single-point dressing tools. Moreover, representative damage indices for diverse damage cases, obtained from impedance signatures at different frequency bands, were taken into account for MLNN data processing. The intelligent system was able to select the most damage-sensitive features based on optimal frequency band. The best models showed a general overall error lower than 2%, thus robustly contributing to the efficient automation of grinding and dressing operations. The promising results of this study foster the EMI-based sensor monitoring approach to fault diagnosis in dressing operations and its effective implementation for industrial grinding process automation.
本文提出了一种基于阻抗的传感器监测方法，用于通过机电阻抗（EMI）技术监测磨削中的修整工具状况。该方法在本工作的第 1 部分中进行了介绍，本文（第 2 部分）的目的是基于多层神经网络（MLNN）和 K-最近邻分类器（k-NN）实现激励频带的最佳选择。该方法在使用两个工业固定式单点修整工具进行实验性修整测试的监测中获得的修整工具状况信息的基础上进行了验证。此外，代表性的损伤指标，从不同频带的阻抗特征中获得，被用于 MLNN 数据处理。智能系统能够基于最佳频带选择最敏感的损伤特征。最佳模型显示出总体误差低于 2%，因此为磨削和修整操作的高效自动化做出了稳健贡献。这项研究的有希望的结果促进了基于 EMI 的传感器监测方法在修整操作中的故障诊断及其在工业磨削过程自动化中的有效实施。
