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

BAO Zhong-xin; WEN Cheng-lin; MA Xue

doi:10.12263/DZXB.20201225

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

PAPERS | 更新时间：2025-12-08

- Data Preprocessing and PCA Fault Diagnosis Method Based on Rate of Change Transformation
- ACTA ELECTRONICA SINICA Vol. 49, Issue 11, Pages: 2234-2240(2021)
- 作者机构：
  
  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
- 作者简介：
- 基金信息：
- DOI：10.12263/DZXB.20201225
  CLC： TP273;
- Received：01 November 2020，
  
  Revised：2021-03-31，
  
  Published：25 November 2021
- 稿件说明：
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鲍中新,文成林,马雪.一种基于数据变化率的预处理及主元分析故障诊断方法[J].电子学报,2021,49(11):2234-2240.

BAO Zhong-xin,WEN Cheng-lin,MA Xue.Data Preprocessing and PCA Fault Diagnosis Method Based on Rate of Change Transformation[J].ACTA ELECTRONICA SINICA,2021,49(11):2234-2240.
鲍中新,文成林,马雪.一种基于数据变化率的预处理及主元分析故障诊断方法[J].电子学报,2021,49(11):2234-2240. DOI： 10.12263/DZXB.20201225.

BAO Zhong-xin,WEN Cheng-lin,MA Xue.Data Preprocessing and PCA Fault Diagnosis Method Based on Rate of Change Transformation[J].ACTA ELECTRONICA SINICA,2021,49(11):2234-2240. DOI： 10.12263/DZXB.20201225.

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.

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.

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references

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