Abstract
In order to meet the requirements of multi-parameter and real-time fault diagnosis of China VI vehicle emission standards diesel engine air system, this paper focuses on the research of the diesel engine air system fault detection and diagnosis method based on deep learning to improve the operational safety and fault diagnosis efficiency. In this paper, the air system fault detection and diagnosis are completed based on the AE model and the CNN model, combined with the actual operation of the diesel engine. Among them, the AE model successfully detected all real-time operation faults, and the false detection rate on the health data was 0.1162%; the CNN model obtained a 90.77% fault diagnosis accuracy rate on the test set of the real-time operation data set. The results show that the model has high accuracy in the diagnosis of diesel engine faults, which is of great significance to the application of deep learning based on big data processing.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Islam, S.S., Rahman, S., Rahman, M.M., et al.: Application of deep learning to computer vision: a comprehensive study. In: 2016 5th International Conference on ICIEV, pp. 592–597. IEEE (2016)
Mohanty, S.P., Hughes, D.P., Marcel, S.: Using deep learning for image-based plant disease detection. Front. Plant Sci. 7, 1419 (2016)
Zhang, Z., Geigere, J., Pohjalainen, J., et al.: Deep learning for environmentally robust speech recognition: an overview of recent developments. ACM Trans. Intell. Syst. Technol. 9(5), 1–28 (2017)
Majumder, N., Poria, S., Gelbukh, A., et al.: Deep learning-based document modeling for personality detection from text. Intell. Syst. IEEE. 32(2), 74–79 (2017)
Liang, H., Sun, X., Sun, Y., et al.: Correction to: text feature extraction based on deep learning: a review. EURASIP J. Wirel. Commun. Netw. 2018(1), 42 (2018). https://doi.org/10.1186/s13638-018-1056-y
Saufi S R, Ahmad Z A B, Lenog M S, et al.: Challenges and opportunities of deep learning models for machinery fault detection and diagnosis: a review. IEEE Access (2019)
Zhang, Q., Yang, L.T., Chen, Z., et al.: A survey on deep learning for big data. Inf. Fusion 42, 146–157 (2018)
Junbo, T., Weining, L., Juneng, A., et al.: Fault diagnosis method study in roller bearing based on wavelet transform and stacked auto-encoder, pp. 4608–4613. IEEE (2015)
Han, J., Choi, D., Hong, S., et al.: Motor fault diagnosis using CNN based deep learning algorithm considering motor rotating speed, pp. 440–445. IEEE (2019)
Verstraete, D., Ferrada, A., Drouuett, E.L., et al.: Deep Learning Enabled Fault Diagnosis Using Time-Frequency Image Analysis of Rolling Element Bearings. Shock and Vibration, pp. 1–17 (2017)
Li, Y.: A Research and application for wind turbine health condition monitoring based on data-driven. University of Electronic Science and Technology of China, pp. 1523–1631 (2019)
Liu, W., Hu, Z: Aero-engine sensor fault diagnosis based on convolutional neural network. In: 2019 Chinese Control and Decision Conference (CCDC), pp. 42–47 (2019)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Wang, Y., Ren, N., Li, J., Liu, B., Si, Q., Zhang, R. (2021). Research on Fault Detection and Diagnosis Method of Diesel Engine Air System Based on Deep Learning. In: Tan, Y., Shi, Y., Zomaya, A., Yan, H., Cai, J. (eds) Data Mining and Big Data. DMBD 2021. Communications in Computer and Information Science, vol 1454. Springer, Singapore. https://doi.org/10.1007/978-981-16-7502-7_33
Download citation
DOI: https://doi.org/10.1007/978-981-16-7502-7_33
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-7501-0
Online ISBN: 978-981-16-7502-7
eBook Packages: Computer ScienceComputer Science (R0)