Computer Integrated Manufacturing System ›› 2022, Vol. 28 ›› Issue (9): 2805-2814.DOI: 10.13196/j.cims.2022.09.013

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Semi-supervised incremental-learning method for tool breakage detection under variable operation conditions

SUN Shixu1,2,HU Xiaofeng1+,XIA Mingyuan1   

  1. 1.School of Mechanical Engineering,Shanghai Jiao Tong University
    2.Shanghai Aerospace Control Technology Institute
  • Online:2022-09-30 Published:2022-10-16
  • Supported by:
    Project supported by the National Defense Basic Scientific Research Program,China(No.JCKY2021110B048),the National Key Research and Development Program,China(No.2018YFB1700502),and the Science and Technology Commission of Shanghai Municipality,China(No.19511105302).

变工况刀具破损监测的半监督增量学习方法

孙世旭1,2,胡小锋1+,夏铭远1   

  1. 1.上海交通大学机械与动力工程学院
    2.上海航天控制技术研究所
  • 基金资助:
    国防基础科研资助项目(JCKY2021110B048);国家重点研发计划资助项目(2018YFB1700502);上海市科委资助项目(19511105302)。

Abstract: Data-driven tool breakage detection methods suffer from the limited availability of anomaly samples and variable distribution of normal samples.A semi-supervised incremental learning method was proposed for the online detection of cutting tool breakages.A certain number of normal samples were acquired,and the low-dimensional representation features of the normal samples were learned by an autoencoder.Based on the distribution of the representation features,a breakage detection model was established and used for online detection.During breakage detection,the reconstruction errors of the trained autoencoder were used for concept drift detection of the observation samples.When concept drift was detected or a sample was falsely recognized,the model should implement incremental learning with the latest sample to retain ideal performance.The proposed method was applied to an experimental cutting tool breakage dataset and compared with the standard autoencoder method,one-class support vector machine method and local outlier factor method.The experimental results demonstrated that the proposed method significantly improved the recognition performance on samples acquired under different conditions.

Key words: tool breakage detection, incremental learning, semi-supervised learning, imbalanced data, operation conditions

摘要: 针对零件加工过程刀具破损监测存在的破损样本数量极少、样本分布随工况变化的问题,提出一种半监督的增量学习方法。首先,采集部分刀具正常状态的样本,通过自编码器学习正常样本的低维共性特征,并根据样本在特征空间的分布建立刀具破损监测模型,在无破损样本的条件下开始破损监测。在破损监测过程中,采用自编码器的重建误差检测样本分布是否变化,在样本分布变化或模型识别错误时,采用最新获取的样本对模型进行增量训练,使模型保持良好的识别能力。利用发电机转子铣削过程刀片破损数据进行验证,与标准的自编码器方法、一分类支持向量机方法和局部离群因子方法进行对比分析,结果表明,所提方法显著提高了对不同工况获取的样本的识别效果。

关键词: 刀具破损监测, 增量学习, 半监督学习, 不平衡数据, 工况

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