Journal of Shanghai Jiao Tong University

Journal of Shanghai Jiaotong University >

2023 , Vol. 57 >Issue 10: 1346 - 1354

DOI: https://doi.org/10.16183/j.cnki.jsjtu.2022.234

Abnormal Detection Method of Tool Machining Monitoring Data Based on Self-Paced Learning

Expand
  • a. School of Mechanical Engineering, Shanghai 200240, China
    b. Institute of Marine Equipment, Shanghai Jiao Tong University, Shanghai 200240, China

Received date: 2022-06-21

  Revised date: 2022-07-22

  Accepted date: 2022-09-08

  Online published: 2022-12-09

Fold

Abstract

Aimed at the problem that the accuracy of tool remaining life prediction was reduced due to the abnormal monitoring data in machining process, a data anomaly detection method based on self-paced learning was proposed. First, a multi-layer perceptron model was established to correlate the tool processing monitoring data with the tool remaining life. Next, in the process of updating model weight, the model weight parameters were fixed first, and the loss threshold of abnormal samples was obtained by predicting loss fitting Gaussian distribution. Then, the loss function based on the self-paced learning method was constructed to update model parameters iteratively. At the end of the model training, abnormal samples were divided according to the loss threshold. Finally, the actual processing monitoring data of turbine rotor groove were used to verify the proposed method, and compare with the local anomaly factor algorithm, the density-based clustering algorithm, the K-means algorithm, the isolated forest algorithm, and the one-class support vector machines.

Key words： tool machining monitoring; data quality; anomaly detection; self-paced learning

Cite this article

ZHANG Jian, HU Xiaofeng, ZHANG Yahui . Abnormal Detection Method of Tool Machining Monitoring Data Based on Self-Paced Learning[J]. Journal of Shanghai Jiaotong University, 2023 , 57(10) : 1346 -1354 . DOI: 10.16183/j.cnki.jsjtu.2022.234

References

[1] 孙宇. 电力设备监测数据处理和数据库设计[D]. 浙江: 浙江大学, 2022.
[1] SUN Yu. Data processing and database design of power equipment monitoring[D]. Zhejiang: Zhejiang University, 2022.
[2] 尚文利, 石贺, 赵剑明, 等. 基于SAE-LSTM的工艺数据异常检测方法[J]. 电子学报, 2021, 49(8): 1561-1568.
[2] SHANG Wenli, SHI He, ZHAO Jianming, et al. An anomaly detection method of process data based on SAE-LSTM[J]. Acta Electronica Sinica, 2021, 49(8): 1561-1568.
[3] 夏英, 韩星雨. 融合统计方法和双向卷积LSTM的多维时序数据异常检测[J]. 计算机应用研究, 2022, 39(5): 1362-1367.
[3] XIA Ying, HAN Xingyu. Multi-dimensional time series data anomaly detection fusing statistical methods and bidirectional convolutional LSTM[J]. Application Research of Computers, 2022, 39(5): 1362-1367.
[4] 孙滢涛, 张锋明, 陈水标, 等. 基于多域特征提取的电力数据异常检测方法[J]. 电力系统及其自动化学报, 2022, 34(6): 105-113.
[4] SUN Yingtao, ZHANG Fengming, CHEN Shuibiao, et al. Power data anomaly detection algorithm based on multi-domain feature extraction[J]. Proceedings of the CSU-EPSA, 2022, 34(6): 105-113.
[5] 傅世元, 高欣, 张浩, 等. 基于元学习动态选择集成的电力调度数据异常检测方法[J]. 电网技术, 2022, 46(8): 3248-3261.
[5] FU Shiyuan, GAO Xin, ZHANG Hao, et al. Anomaly detection for power dispatching data based on meta-learning dynamic ensemble selection[J]. Power System Technology, 2022, 46(8): 3248-3261.
[6] 刘鑫. 无监督异常检测方法研究及其应用[D]. 成都: 电子科技大学, 2018.
[6] LIU Xin. Research on unsupervised anomaly detection algorithm and application[D]. Chengdu: University of Electronic Science and Technology of China, 2018.
[7] DU H, ZHAO S, ZHANG D, et al. Novel clustering-based approach for local outlier detection[C]//International Conference on Computer Communications Workshops. San Francisco, CA, USA: IEEE, 2016: 802-811.
[8] 吴蕊, 张安勤, 田秀霞, 等. 基于改进K-means的电力数据异常检测算法[J]. 华东师范大学学报(自然科学版), 2020(4): 79-87.
[8] WU Rui, ZHANG Anqin, TIAN Xiuxia, et al. Anomaly detection algorithm based on improved K-means for electric power data[J]. Journal of East China Normal University (Natural Science), 2020(4): 79-87.
[9] 吴金娥, 王若愚, 段倩倩, 等. 基于反向k近邻过滤异常的群数据异常检测[J]. 上海交通大学学报, 2021, 55(5): 598-606.
[9] WU Jin’e, WANG Ruoyu, DUAN Qianqian, et al. Collective data anomaly detection based on reverse k-nearest neighbor filtering[J]. Journal of Shanghai Jiao Tong University, 2021, 55(5): 598-606.
[10] 陈砚桥, 孙彤, 张侨禹. 基于DBSCAN的智能机舱多源数据异常检测方法[J]. 舰船科学技术, 2021, 43(17): 156-160.
[10] CHEN Yanqiao, SUN Tong, ZHANG Qiaoyu. Intelligent engine room multi-source data detecting method based on DBSCAN cluster algorithm[J]. Ship Science and Technology, 2021, 43(17): 156-160.
[11] 宋丽娜, 刘淼, 秦韬, 等. 基于LOF与CEEMD的城镇取用水监测数据异常值识别[J]. 水利信息化, 2022(2): 33-40.
[11] SONG Lina, LIU Miao, QIN Tao, et al. Outlier identification of urban water intake monitoring data based on LOF and CEEMD[J]. Water Resources Informatization, 2022(2): 33-40.
[12] 王锋, 高欣, 贾欣, 等. 一种基于对数区间隔离森林的电力调度数据异常检测集成算法[J]. 电网技术, 2021, 45(12): 4818-4827.
[12] WANG Feng, GAO Xin, JIA Xin, et al. An anomaly detection ensemble algorithm for power dispatching data based on log-interval isolation[J]. Power System Technology, 2021, 45(12): 4818-4827.
[13] 王燕晋, 易忠林, 郑思达, 等. 基于孤立森林算法的电力用户数据异常快速识别研究[J]. 电子设计工程, 2022, 30(3): 11-14.
[13] WANG Yanjin, YI Zhonglin, ZHENG Sida, et al. Research on fast identification of power user data abnormal based on isolation forest algorithm[J]. Electronic Design Engineering, 2022, 30(3): 11-14.
[14] 卓琳, 赵厚宇, 詹思延. 异常检测方法及其应用综述[J]. 计算机应用研究, 2020, 37(Sup.1): 9-15.
[14] ZHUO Lin, ZHAO Houyu, ZHAN Siyan. Anomaly detection and its application[J]. Application Research of Computers, 2020, 37(Sup.1): 9-15.
[15] BENGIO Y, LOURADOUR J, COLLOBERT R, et al. Curriculum learning[C]//Proceedings of the 26th Annual International Conference on Machine Learning. New York, NY, USA: Association for Computing Machinery, 2009: 41-48.
[16] KUMAR M P, PACKER B, KOLLER D. Self-paced learning for latent variable models[C]//Proceedings of the 23rd International Conference on Neural Information Processing Systems-Volume 1. Red Hook, NY, USA: Curran Associates Inc., 2010: 1189-1197.
[17] 王艺玮, 邓蕾, 郑联语, 等. 基于多通道融合及贝叶斯理论的刀具剩余寿命预测方法[J]. 机械工程学报, 2021, 57(13): 214-224.
[17] WANG Yiwei, DENG Lei, ZHENG Lianyu, et al. A multi-channel signal fusion and Bayesian theory based method for tool remaining useful life prediction[J]. Journal of Mechanical Engineering, 2021, 57(13): 214-224.
[18] 吴浩, 卢楠, 邹进贵, 等. GNSS变形监测时间序列的改进型3σ粗差探测方法[J]. 武汉大学学报(信息科学版), 2019, 44(9): 1282-1288.
[18] WU Hao, LU Nan, ZOU Jingui, et al. An improved 3σ gross error detection method for GNSS deformation monitoring time series[J]. Geomatics and Information Science of Wuhan University, 2019, 44(9): 1282-1288.
[19] 徐洪钟, 吴中如, 李雪红, 等. 基于小波分析的大坝变形观测数据的趋势分量提取[J]. 武汉大学学报(工学版), 2003(6): 5-8.
[19] XU Hongzhong, WU Zhongru, LI Xuehong, et al. Abstracting trend component of dam observation data based on wavelet analysis[J]. Engineering Journal of Wuhan University, 2003(6): 5-8.
[20] 党英, 吉卫喜, 陆家辉, 等. 基于深度学习的铣刀剩余寿命预测方法研究[J]. 现代制造工程, 2021(12): 79-87.
[20] DANG Ying, JI Weixi, LU Jiahui, et al. Research on prediction method of remaining useful life of milling cutter based on deep learning[J]. Modern Manufacturing Engineering, 2021(12): 79-87.
Options
Outlines

/

〈 〉