基于机床外部物理特性的机器学习算法监测机床

Chia-Ruei Liu, L. Duan, Po-Wei Chen, Chao-Chun Yang
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引用次数: 2

摘要

利用三维加速度传感器和电流传感器采集机床的振动数据和电流数据来获取机床信息,而不需要可编程逻辑控制器(PLC)是直接的方法。通过特征提取工程处理数据,通过机器学习算法建立模型。由此可以准确地识别机床的状态。该系统具有较高的可扩展性和可移植性,可以帮助中小企业对机床进行监控。
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Monitoring Machine Tool Based on External Physical Characteristics of the Machine Tool Using Machine Learning Algorithm
Using the three-dimensional acceleration sensor and the current sensor to collect vibration data and current data to get information from machine tools without Programmable Logic Controller(PLC) is the direct method. Processing the data by characteristic extraction engineering, and building models by machine learning algorithms. So it can identify the status of machine tools from this models accurately. Then, it can help the small-and medium sized enterprises to monitor machine tools with high scalability and portability.
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