Park Byeonghui, Lee Yoonjae, Yeo Myeonghwan, Lee Haemi, Joo Changbeom, Lee Changwoo
Department of Mechanical Design and Production Engineering, Konkuk University, Seoul 05030, Korea.
Department of Mechanical Engineering, Stevens Institute of Technology, 1 Castle Pointe Terrace, Hoboken, NJ 07030, USA.
Sensors (Basel). 2022 Mar 3;22(5):1975. doi: 10.3390/s22051975.
Fault diagnosis systems are used to improve the productivity and reduce the costs of the manufacturing process. However, the feature variables in existing systems are extracted based on the classification performance of the final model, thereby limiting their applications to models with different conditions. This paper proposes an algorithm to improve the characteristics of feature variables by considering the cutting conditions. Regardless of the frequency band, the noise of the measurement data was reduced through an oversampling method, setting a window length through a cutter sampling frequency, and improving its sensitivity to shock signal. An experiment was subsequently performed to confirm the performance of the model. Using normal and wear tools on AI7075 and SM45C, the diagnosis accuracies were 97.1% and 95.6%, respectively, with a reduction of 85% and 83%, respectively, in the time required to develop a diagnosis model. Therefore, the proposed algorithm reduced the model computation time and developed a model with high accuracy by enhancing the characteristics of the feature variable. The results of this study can contribute significantly to the establishment of a high-precision monitoring system for various processing processes.
故障诊断系统用于提高生产效率并降低制造过程的成本。然而，现有系统中的特征变量是基于最终模型的分类性能提取的，从而将其应用局限于不同条件的模型。本文提出一种算法，通过考虑切削条件来改善特征变量的特性。无论频带如何，通过过采样方法降低测量数据的噪声，根据刀具采样频率设置窗口长度，并提高其对冲击信号的灵敏度。随后进行了实验以确认模型的性能。在AI7075和SM45C上使用正常刀具和磨损刀具时，诊断准确率分别为97.1%和95.6%，开发诊断模型所需的时间分别减少了85%和83%。因此，所提出的算法通过增强特征变量的特性减少了模型计算时间，并开发出了高精度模型。本研究结果可为建立针对各种加工过程的高精度监测系统做出重大贡献。
