A fault diagnosis method for nuclear power plants rotating machinery based on deep learning under imbalanced samples

被引:10
作者
Yin, Wenzhe [1 ]
Xia, Hong [1 ]
Huang, Xueying [1 ]
Wang, Zhichao [1 ]
机构
[1] Harbin Engn Univ, Fundamental Sci Nucl Safety & Simulat Technol Lab, Harbin 150001, Peoples R China
基金
中国国家自然科学基金;
关键词
Nuclear power plant; Rotating machinery; Imbalanced samples; Fault diagnosis; Deep learning;
D O I
10.1016/j.anucene.2024.110340
中图分类号
TL [原子能技术]; O571 [原子核物理学];
学科分类号
0827 ; 082701 ;
摘要
Rotating machinery is a critical equipment widely used in nuclear power plants (NPPs). Effective fault diagnosis technology can provide reliable operation and maintenance support for rotating machinery. Data-driven intelligent fault diagnosis technology has attracted much attention in recent years. However, in practical situations, the available fault data is limited, and the imbalanced samples will make the intelligent fault diagnosis model prone to problems such as poor generalization performance and low diagnostic accuracy. Therefore, a fault diagnosis method based on deep learning is proposed to alleviate the impact of imbalanced samples on fault diagnosis of rotating machinery. First, the adaptive synthetic sampling (ADASYN) approach is used to synthesize the multi-channel vibration data to expand the unbalanced samples. Subsequently, ensemble empirical mode decomposition (EEMD) and continuous wavelet transform (CWT) are used to convert the vibration data into time-frequency images to highlight the fault features of the samples. Then, the deep residual neural network is constructed to extract the features of mixed samples and implement fault diagnosis. Fault simulation experiments are carried out based on motors and bearings, and various imbalance degree datasets are formed to provide data support for verifying the effectiveness of the proposed method. The proposed method can achieve good diagnostic performance under various degrees of imbalanced samples. In addition, the comparison results with other methods show that the proposed method has the best comprehensive performance, demonstrating the potential application value in the fault diagnosis of NPPs rotating machinery.
引用
收藏
页数:21
相关论文
共 38 条
[1]   Medical image retrieval using ResNet-18 [J].
Ayyachamy, Swarnambiga ;
Alex, Varghese ;
Khened, Mahendra ;
Krishnamurthi, Ganapathy .
MEDICAL IMAGING 2019: IMAGING INFORMATICS FOR HEALTHCARE, RESEARCH, AND APPLICATIONS, 2019, 10954
[2]   Rotor Faults Diagnosis Using Feature Selection and Nearest Neighbors Rule: Application to a Turbogenerator [J].
Biet, Melisande .
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2013, 60 (09) :4063-4073
[3]   A tutorial survey of architectures, algorithms, and applications for deep learning [J].
Deng, Li .
APSIPA TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING, 2014, 3
[4]   Intelligent Condition-Based Monitoring of Rotary Machines With Few Samples [J].
Dixit, Sonal ;
Verma, Nishchal K. .
IEEE SENSORS JOURNAL, 2020, 20 (23) :14337-14346
[5]  
Dosovitskiy A, 2021, Arxiv, DOI arXiv:2010.11929
[6]   Swarm-LSTM: Condition Monitoring of Gearbox Fault Diagnosis Based on Hybrid LSTM Deep Neural Network Optimized by Swarm Intelligence Algorithms [J].
Durbhaka, Gopi Krishna ;
Selvaraj, Barani ;
Mittal, Mamta ;
Saba, Tanzila ;
Rehman, Amjad ;
Goyal, Lalit Mohan .
CMC-COMPUTERS MATERIALS & CONTINUA, 2021, 66 (02) :2041-2059
[7]   Detection of Deterioration of Three-phase Induction Motor using Vibration Signals [J].
Glowacz, Adam ;
Glowacz, Witold ;
Kozik, Jaroslaw ;
Piech, Krzysztof ;
Gutten, Miroslav ;
Caesarendra, Wahyu ;
Liu, Hui ;
Brumercik, Frantisek ;
Irfan, Muhammad ;
Khan, Z. Faizal .
MEASUREMENT SCIENCE REVIEW, 2019, 19 (06) :241-249
[8]   A Novel Deep Learning Method for Intelligent Fault Diagnosis of Rotating Machinery Based on Improved CNN-SVM and Multichannel Data Fusion [J].
Gong, Wenfeng ;
Chen, Hui ;
Zhang, Zehui ;
Zhang, Meiling ;
Wang, Ruihan ;
Guan, Cong ;
Wang, Qin .
SENSORS, 2019, 19 (07)
[9]   Aeroengine Control System Sensor Fault Diagnosis Based on CWT and CNN [J].
Gou, Linfeng ;
Li, Huihui ;
Zheng, Hua ;
Li, Huacong ;
Pei, Xiaoning .
MATHEMATICAL PROBLEMS IN ENGINEERING, 2020, 2020
[10]   A novel fault diagnosis method of rotating machinery via VMD, CWT and improved CNN [J].
Gu, Jun ;
Peng, Yuxing ;
Lu, Hao ;
Chang, Xiangdong ;
Chen, Guoan .
MEASUREMENT, 2022, 200