基于数据驱动的微小故障诊断方法综述

文成林; 吕菲亚; 包哲静; 刘妹琴

doi:10.16383/j.aas.2016.c160105

基于数据驱动的微小故障诊断方法综述

doi: 10.16383/j.aas.2016.c160105

1.
杭州电子科技大学自动化学院杭州 310018
2.
浙江大学电气工程学院杭州 310027

基金项目:

国家自然科学基金 61273170

浙江省自然科学基金 LZ15F030001

国家自然科学基金 U1509203

国家自然科学基金 61490701

国家自然科学基金 61333005

详细信息

作者简介:
吕菲亚浙江大学电气工程学院博士研究生.主要研究方向为故障诊断,智能控制,大数据分析.E-mail:lvfeiya0215@126.com

包哲静浙江大学电气工程学院副教授.主要研究方向为智能控制,故障诊断,大数据分析和微电网规划.E-mail:zjbao@zju.edu.cn

刘妹琴浙江大学电气工程学院教授.主要研究方向为鲁棒控制,多传感器网络,信息融合.E-mail:liumeiqin@zju.edu.cn

通讯作者:
文成林杭州电子科技大学自动化学院教授.主要研究方向为信息融合,多目标跟踪,故障诊断.本文通信作者.E-mail:wencl@hdu.edu.cn

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出版历程
- 收稿日期: 2016-02-29
- 录用日期: 2016-06-06
- 刊出日期: 2016-09-01

A Review of Data Driven-based Incipient Fault Diagnosis

1.
School of Automation, Hangzhou Dianzi University, Hangzhou 310018
2.
College of Electrical Engineering, Zhejiang University, Hangzhou 310027

Funds:

National Natural Science Foundation of China 61273170

Zhejiang Provincial Natural Science Foundation of China LZ15F030001

National Natural Science Foundation of China U1509203

National Natural Science Foundation of China 61490701

National Natural Science Foundation of China 61333005

More Information

Author Bio:
Ph.D. candidate at the College of Electrical Engineering, Zhejiang University. Her research interest covers fault diagnosis, intelligent control, and big data analysis.

Associate professor at the College of Electrical Engineering, Zhejiang University. Her research interest covers intelligent control, fault diagnosis, big data analysis, and planning of microgrid.

Professor at the College of Electrical Engineering, Zhejiang University. Her research interest covers robust control, multi-sensor networks, and information fusion.

Corresponding author: WEN Cheng-Lin Professor at the School of Automation, Hangzhou Dianzi University. His research interest covers information fusion, multi-target tracking, and fault diagnosis. Corresponding author of this paper.

摘要

摘要: 能否及时诊断出微小故障是保障系统安全运行并抑制故障恶化的关键，本文针对微小故障幅值低、易被系统扰动和噪声掩盖等特点，从数据驱动的角度对现有研究进行综述.并将其分为三大类: 基于统计分析的微小故障诊断技术、基于信号处理的微小故障诊断技术和基于人工智能的微小故障诊断技术，进而对不同方法的基本研究思想、研究进展、应用以及局限性予以介绍.最后不仅指出复杂系统微小故障诊断研究中的现存问题，而且从增加新的信息、挖掘未利用的隐含信息和采用新的数学工具三个角度进行展望，提出基于关联性分析、基于多源信息融合、基于机器学习和基于时频分析四个值得探究的微小故障诊断思想.
- 微小故障诊断 /
- 数据驱动 /
- 统计分析 /
- 信号处理 /
- 人工智能
Abstract: As timely incipient fault diagnosis is the key to guarantee operation safety and suppress fault deterioration, this paper gives a review of data driven-based researches for incipient faults, which have low amplitude and may be covered by system disturbance and noise easily. Data driven-based incipient fault diagnosis can be divided into three parts, i.e., statistical analysis-based technology, signal processing-based technology, artificial intelligence-based technology. Their basic ideas, research progresses, application and limitations are discussed in detail. Furthermore, this paper not only points out the existing problems about complex systems, but also looks forward to the advance of this area by means of adding new information, mining unused implied information, using new mathematical tools. Finally, four thoughts worth exploring are proposed: diagnosis based on correlation analysis, multi-source information fusion, machine learning and time-frequency transform.
- Incipient fault diagnosis /
- data driven /
- statistical analysis /
- signal processing /
- artificial intelligence

HTML全文

图 1 基于数据驱动的微小故障诊断方法分类

Fig. 1 The classification of data-driven based incipient fault diagnosis

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