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首页> 外文会议>International Symposium on Digital Manufacture; 20061015-17; Wuhan(CN) >Improved K-means Algorithm for Manufacturing Process Anomaly Detection and Recognition
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Improved K-means Algorithm for Manufacturing Process Anomaly Detection and Recognition

机译：改进的K均值算法在制造过程异常检测与识别中的应用

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摘要

Anomaly detection and recognition are of prime importance in process industries. Faults are usually rare, and, therefore, predicting them is difficult. In this paper, a new greedy initialization method for the K-means algorithm is proposed to improve traditional K-means clustering techniques. The new initialization method tries to choose suitable initial points, which are well separated and have the potential to form high-quality clusters. Based on the clustering result of historical disqualification product data in manufacturing process which generated by the Improved-K-means algorithm , a prediction model -which is used to detect and recognize the abnormal trend of the quality problems is constructed . This simple and robust alarm-system architecture for predicting incoming faults realizes the transition of quality problems from diagnosis afterward to prevention beforehand indeed. In the end , the alarm model was applied for prediction and avoidance of gear-wheel assembly faults at a gear-plant.
机译：异常检测和识别在过程工业中至关重要。故障通常很少见，因此很难预测。本文提出了一种新的贪婪初始化方法，用于改进K均值聚类技术。新的初始化方法试图选择合适的初始点，这些初始点被很好地分离并且有可能形成高质量的簇。基于改进的K均值算法生成的制造过程中历史不合格产品数据的聚类结果，构建了预测模型，该模型用于检测和识别质量问题的异常趋势。这种用于预测传入故障的简单而强大的警报系统架构确实实现了质量问题从事后诊断到事前预防的过渡。最后，将警报模型应用于齿轮厂的齿轮总成故障的预测和避免。

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