电子学报 ›› 2021, Vol. 49 ›› Issue (11): 2234-2240.DOI: 10.12263/DZXB.20201225

• 学术论文 • 上一篇    下一篇

一种基于数据变化率的预处理及主元分析故障诊断方法

鲍中新1, 文成林2, 马雪1   

  1. 1.杭州电子科技大学自动化学院,浙江 杭州 310018
    2.广东石油化工学院自动化学院,广东 茂名 525000
  • 收稿日期:2020-11-01 修回日期:2021-03-31 出版日期:2021-11-25 发布日期:2021-11-25
  • 作者简介:鲍中新 男,1996年出生于安徽六安,杭州电子科技大学在读硕士研究生,主要研究方向为故障诊断、模式识别. E-mail: 981316982@qq.com
    文成林 男,1963年出生于河南开封,广东石油化工学院教授.主要研究方向为信息融合、多目标跟踪、深度学习、故障诊断. E-mail: wencl@hdu.edu.cn
    马 雪 女,1992年出生于江苏淮安,杭州电子科技大学在读博士研究生.研究方向为专家系统、机器学习与深度学习、信息融合与故障诊断. E-mail: maxue@163.com
  • 基金资助:
    国家电网有限公司科技项目(SGHB0000KXJS1800375)

Data Preprocessing and PCA Fault Diagnosis Method Based on Rate of Change Transformation

Zhong-xin BAO1, Cheng-lin WEN2, Xue MA1   

  1. 1.School of Automation,Hangzhou Dianzi University,Hangzhou,Zhejiang 310018,China
    2.School of Automation,Guangdong University of Petrochemical Technology,Maoming,Guangdong 525000,China
  • Received:2020-11-01 Revised:2021-03-31 Online:2021-11-25 Published:2021-11-25

摘要:

基于深度学习的方法解决微小故障已经取得了很大的进展和很好的效果, 但是前提要有充足的样本数据,在现有的情况下却难以实现.所以基于传统的数据预处理的故障诊断方法仍然有很好的必要性和现实性.主元分析(Principal Component Analysis, PCA) 被广泛应用在故障诊断中,由于传统的数据预处理方法各有优势和不足,造成特征提取不准确,为此该文提出了一种基于数据变化率(Rate Of Change, ROC)的数据预处理方法以提高PCA在故障诊断中的性能指标.通过变化率变换对原始数据集预处理后,能够有效地检测系统变量中的微小故障.最后,通过仿真验证基于数据变化率的PCA故障诊断方法的可行性和有效性.

关键词: 故障诊断, 数据驱动, 数据预处理, Gap度量, 主元分析, 变化率变换, 特征提取

Abstract:

The method based on deep learning has made great progress and good results in solving small faults, but the prerequisite for sufficient sample data is difficult to achieve in the current situation. So there is still a good need for the fault diagnosis method based on traditional data preprocessing. Principal component analysis(PCA) is widely used in fault diagnosis. Because traditional data preprocessing methods use the absolute distance between samples as the criterion for fault detection and fault diagnosis, the feature extraction is not accurate. For this reason, this paper proposes a data preprocessing method based on rate-of-change(ROC) transformation to improve the performance index of PCA in fault diagnosis. After the original data set is preprocessed by the change rate transformation, it can effectively detect the minor faults in the system variables. Finally, the feasibility and effectiveness of the PCA fault diagnosis method based on the rate of data change are verified by simulation.

Key words: fault diagnosis, data driven, data preprocessing, gap metric, principal component analysis, rate of change transformation, feature extraction

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