A novel data augmentation approach to fault diagnosis with class-imbalance problem

被引:49
作者
Tian, Jilun [1 ]
Jiang, Yuchen [1 ]
Zhang, Jiusi [1 ]
Luo, Hao [1 ]
Yin, Shen [2 ]
机构
[1] Harbin Inst Technol, Sch Astronaut, Dept Control Sci & Engn, Harbin 150000, Peoples R China
[2] Norwegian Univ Sci & Technol, Fac Engn, Dept Mech & Ind Engn, N-7034 Trondheim, Norway
基金
黑龙江省自然科学基金; 中国国家自然科学基金;
关键词
Class imbalance; Fault diagnosis; Conditional variational auto-encoder; Kullback-Leibler Divergence Vanishing; Kernel mean matching;
D O I
10.1016/j.ress.2023.109832
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Data-driven fault diagnosis techniques are frequently applied to ensure the reliability and safety of industrial systems. However, as a common challenge, the class-imbalance problem reduces the performance of data -driven methods due to the lack of data information. We propose a weighted modified conditional variational auto-encoder (WM-CVAE) as a novel data augmentation technique to tackle the issue. The modified structure can alleviate the existing Kullback-Leibler (KL) divergence vanishing by an adaptive loss. Meanwhile, kernel mean matching (KMM) is proposed on weight computation to reduce the negative effect of dissimilar generated samples. Constructing the WM-CVAE data augmentation framework can effectively improve the data quality and learning capability in class-imbalance fault diagnosis. To validate the proposed WM-CVAE model, three real-world industrial datasets are used as study objects, and the random forest is used as the base learner in the fault classification tasks. The diagnostic results demonstrate that the proposed WM-CVAE data augmentation framework can improve learning results in class-imbalance fault diagnosis.
引用
收藏
页数:11
相关论文
共 47 条
[1]  
Asuncion A., 2007, Uci Machine Learning Repository
[2]   Fault Diagnosis using eXplainable AI: A transfer learning-based approach for rotating machinery exploiting augmented synthetic data [J].
Brito, Lucas Costa ;
Susto, Gian Antonio ;
Brito, Jorge Nei ;
Duarte, Marcus Antonio Viana .
EXPERT SYSTEMS WITH APPLICATIONS, 2023, 232
[3]   A systematic study of the class imbalance problem in convolutional neural networks [J].
Buda, Mateusz ;
Maki, Atsuto ;
Mazurowski, Maciej A. .
NEURAL NETWORKS, 2018, 106 :249-259
[4]   A Deep Probabilistic Transfer Learning Framework for Soft Sensor Modeling With Missing Data [J].
Chai, Zheng ;
Zhao, Chunhui ;
Huang, Biao ;
Chen, Hongtian .
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2022, 33 (12) :7598-7609
[5]   A novel bearing fault diagnosis approach using the Gaussian mixture model and the weighted principal component analysis [J].
Chaleshtori, Amir Eshaghi ;
Aghaie, Abdollah .
RELIABILITY ENGINEERING & SYSTEM SAFETY, 2024, 242
[6]   A framework to automate fault detection and diagnosis based on moving window principal component analysis and Bayesian network [J].
de Andrade Melani, Arthur Henrique ;
de Carvalho Michalski, Miguel Angelo ;
da Silva, Renan Favarao ;
Martha de Souza, Gilberto Francisco .
RELIABILITY ENGINEERING & SYSTEM SAFETY, 2021, 215
[7]   Fault Diagnosis of Machines Using Deep Convolutional Beta-Variational Autoencoder [J].
Dewangan G. ;
Maurya S. .
IEEE Transactions on Artificial Intelligence, 2022, 3 (02) :287-296
[8]   Deep imbalanced domain adaptation for transfer learning fault diagnosis of bearings under multiple working conditions [J].
Ding, Yifei ;
Jia, Minping ;
Zhuang, Jichao ;
Cao, Yudong ;
Zhao, Xiaoli ;
Lee, Chi-Guhn .
RELIABILITY ENGINEERING & SYSTEM SAFETY, 2023, 230
[9]  
Fu Hao, 2019, arXiv
[10]   Learning from Imbalanced Data [J].
He, Haibo ;
Garcia, Edwardo A. .
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2009, 21 (09) :1263-1284