Research on Fault Pattern Recognition Model of Nuclear Power Plant Water Pump Based on Frequency-Domain Data Attention Mechanism

Liu Ziming; Luo Neng; Ai Qiong

doi:10.13832/j.jnpe.2021.06.0203

Volume 42 Issue 6

Dec. 2021

Turn off MathJax

Article Contents

Article Navigation > Nuclear Power Engineering > 2021 > 42(6): 203-208

Liu Ziming, Luo Neng, Ai Qiong. Research on Fault Pattern Recognition Model of Nuclear Power Plant Water Pump Based on Frequency-Domain Data Attention Mechanism[J]. Nuclear Power Engineering, 2021, 42(6): 203-208. doi: 10.13832/j.jnpe.2021.06.0203

Citation:

Liu Ziming, Luo Neng, Ai Qiong. Research on Fault Pattern Recognition Model of Nuclear Power Plant Water Pump Based on Frequency-Domain Data Attention Mechanism[J]. Nuclear Power Engineering, 2021, 42(6): 203-208. doi: 10.13832/j.jnpe.2021.06.0203

Citation:

PDF( 2961 KB)

Research on Fault Pattern Recognition Model of Nuclear Power Plant Water Pump Based on Frequency-Domain Data Attention Mechanism

doi: 10.13832/j.jnpe.2021.06.0203

1.
Chengdu University of Information Technology, Chengdu, 610225, China
2.
Nuclear Power Institute of China, Chengdu, 610213, China

Received Date: 2021-09-22
Rev Recd Date: 2021-10-23
Publish Date: 2021-12-09

Abstract

Abstract

In view of the common fault modes of nuclear power plant pump, such as abnormal vibration, friction and abrasion of rotor parts, etc., this paper uses frequency domain data of the acceleration signal on pump shell which is easiest to be obtained as input, proposes a new method for frequency-domain data attention mechanism which combines convolutional neural network and attention network, and establishes the recognition model of fault mode of nuclear power plant water pump. The results show that: Compared with the traditional methods, the water pump fault pattern recognition model based on frequency domain data as input and based on frequency domain data attention network algorithm has a shorter input data length and can effectively improve the efficiency of model training. The fault pattern recognition accuracy of the fault pattern recognition model on the test set is 100%, which is better than other fault diagnosis models based on deep learning algorithm, which proves the advantages of the method proposed in this paper.
- Nuclear power plant water pump,
- Frequency domain data,
- Attention mechanism,
- Fault pattern recognition

FullText(HTML)

References(8)

References

[1]	XIA M, LI T, XU L, et al. Fault diagnosis for rotating machinery using multiple sensors and convolutional neural networks[J]. IEEE/ASME Transactions on Mechatronics, 2018, 23(1): 101-110. doi: 10.1109/TMECH.2017.2728371
[2]	ZHAO M H, ZHONG S S, FU X Y, et al. Deep residual shrinkage networks for fault diagnosis[J]. IEEE Transactions on Industrial Informatics, 2020, 16(7): 4681-4690. doi: 10.1109/TII.2019.2943898
[3]	SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[C]//3rd International Conference on Learning Representations. San Diego, CA, USA: ICLR, 2015: 1-14.
[4]	ZHANG W, PENG G L, LI C H, et al. A new deep learning model for fault diagnosis with good anti-noise and domain adaptation ability on raw vibration signals[J]. Sensors, 2017, 17(2): 425. doi: 10.3390/s17020425
[5]	VAN DER MAATEN L, HINTON G. Visualizing data using t-SNE[J]. Journal of Machine Learning Research, 2008, 9(86): 2579-2605.
[6]	LI X, ZHANG W, DING Q. Understanding and improving deep learning-based rolling bearing fault diagnosis with attention mechanism[J]. Signal Processing, 2019(161): 136-154. doi: 10.1016/j.sigpro.2019.03.019
[7]	YANG Z B, ZHANG J P, ZHAO Z B, et al. Interpreting network knowledge with attention mechanism for bearing fault diagnosis[J]. Applied Soft Computing Journal, 2020(97): 106829. doi: 10.1016/j.asoc.2020.106829
[8]	VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems. Long Beach, CA, USA: NIPS, 2017: 5998-6008.