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PDF(1189 KB)
PDF(1189 KB)
数据驱动的加工过程异常诊断
Data-Driven Anomaly Diagnosis for Machining Processes
为了在计算机数控（CNC）加工过程中实现零缺陷生产，开发有效的异常检测诊断系统势在必行。然而，由于加工过程中机床和工装的动态条件限制，目前在工业生产中采用的相关诊断系统所能发挥的作用往往非常有限。为了解决这个问题，本文提出了一种全新的异常数据驱动的诊断系统。在该系统之中，我们持续收集随动态加工过程而产生的状态监测功率数据，并以此支持在线诊断分析。为了便于分析，我们设计了预处理机制对所监视的数据进行去噪、标准化以及校准。随后我们即从监控数据中提取关键特征，并定义阈值以识别异常。考虑到加工过程中机床和工装的动态条件，用于识别异常的阈值可以调整。我们还可以基于历史数据利用果蝇优化（FFO）算法优化阈值，以实现更准确的检测。通过实践验证，我们证明了该系统在工业应用中的有效性和巨大前景。
To achieve zero-defect production during computer numerical control (CNC) machining processes, it is imperative to develop effective diagnosis systems to detect anomalies efficiently. However, due to the dynamic conditions of the machine and tooling during machining processes, the relevant diagnosis systems currently adopted in industries are incompetent. To address this issue, this paper presents a novel data-driven diagnosis system for anomalies. In this system, power data for condition monitoring are continuously collected during dynamic machining processes to support online diagnosis analysis. To facilitate the analysis, preprocessing mechanisms have been designed to denoise, normalize, and align the monitored data. Important features are extracted from the monitored data and thresholds are defined to identify anomalies. Considering the dynamic conditions of the machine and tooling during machining processes, the thresholds used to identify anomalies can vary. Based on historical data, the values of thresholds are optimized using a fruit fly optimization (FFO) algorithm to achieve more accurate detection. Practical case studies were used to validate the system, thereby demonstrating the potential and effectiveness of the system for industrial applications.
计算机数控加工 / 异常检测 / 果蝇优化算法 / 数据驱动方法
Computer numerical control machining / Anomaly detection / Fruit fly optimization algorithm / Data-driven method
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