Fault prediction of combine harvesters based on stacked denoising autoencoders
Keywords:
fault prediction, combine harvester, stacked denoising autoencoders, support vector machinesAbstract
Accurate fault prediction is essential to ensure the safety and reliability of combine harvester operation. In this study, a combine harvester fault prediction method based on a combination of stacked denoising autoencoders (SDAE) and multi-classification support vector machines (SVM) is proposed to predict combine harvester faults by extracting operational features of key combine components. In general, SDAE contains autoencoders and uses a deep network architecture to learn complex non-linear input-output relationships in a hierarchical manner. Selected features are fed into the SDAE network, deep-level features of the input parameters are extracted by SDAE, and an SVM classifier is then added to its top layer to achieve combine harvester fault prediction. The experimental results show that the method can achieve accurate and efficient combine harvester fault prediction. In particular, the experiments uses Gaussian noise with a distribution center of 0.05 to corrupt the test data samples obtained by random sampling of the whole population, and the results showed that the prediction accuracy of the method is 95.31%, which has better robustness and generalization ability compared to SVM (77.03%), BP (74.61%), and SAE (90.86%). Keywords: fault prediction, combine harvester, stacked denoising autoencoders, support vector machines DOI: 10.25165/j.ijabe.20221502.6963 Citation: Qiu Z M, Shi G X, Zhao B, Jin X, Zhou L M, Ma T F. Fault prediction of combine harvesters based on stacked denoising autoencoders. Int J Agric & Biol Eng, 2022; 15(2): 189–196.References
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[2] Ylmaz D, Gkduman ME. Development of a measurement system for noise and vibration of combine harvester. Int J Agric & Biol Eng, 2020; 13(6): 104–108.
[3] Luo X W, Liao J, Hu l, Zang Y, Zhou Z Y. Improving agricultural mechanization level to promote agricultural sustainable development. Editorial Office of Transactions of the CSAE, 2016; 32(1): 1–11. (in Chinese)
[4] Gao Z, Cecati C, Ding S X. A survey of fault diagnosis and fault-tolerant techniques—Part I: fault diagnosis with model-based and signal-based approaches. IEEE Transactions on Industrial Electronics, 2015; 62(6): 3757–3767.
[5] Cecchini M, Piccioni F, Ferri S, Coltrinari G, Colantoni A. Preliminary investigation on systems for the preventive diagnosis of faults on agricultural operating machines. Sensors, 2021; 21(4): 1547. https://doi.org/10.3390/s21041547.
[6] Kamilaris A, Prenafeta-Boldu F X. Deep learning in agriculture: A survey. Computers and Electronics in Agriculture, 2018; 147: 70–90.
[7] Cheng L, Zhang Y. Analysis of intelligent agricultural system and control mode based on fuzzy control and sensor network. Journal of Intelligent and Fuzzy Systems, 2019; 37(2): 1–12.
[8] Zhang Z W, Chen H H, Li S M, Wang J R. A novel sparse filtering approach based on time-frequency feature extraction and softmax regression for intelligent fault diagnosis under different speeds. Journal of Central South University, 2019; 26(6): 1607–1618.
[9] Zheng J X, Li M, Hu S K, Xiao X W, Li H, Li W F. Research on optimization of agricultural machinery fault monitoring system based on artificial neural network algorithm. INMATEH-Agricultural Engineering, 2021; 64: 297-306.
[10] Gupta S, Khosravy M, Gupta N, D Ar B Ar I H, Patel N. Hydraulic system onboard monitoring and fault diagnostic in agricultural machine. Brazilian Archives of Biology and Technology, 2019; 62(3): e19180363. https://doi.org/10.1590/1678-4324-2019180363.
[11] Wattanajitsiri V, Kanchana R, Triwanapong S, Kimapong K. Identifying preventive maintenance guideline for a combine harvester with application of fault mode and effect analysis technique. MATEC Web of Conferences, 2020; 319: 01004. https://schlr.cnki.net/Detail/doi/WWMERGEJLAST/ SJEQ5AFDF8B9341F9D89610B1F7A244685D7
[12] Janotta R, Podsdek S, Bartoszuk M. The concept of a mechatronic system for monitoring the temperature of bearings in a combine harvester. 2nd International Conference on Chemistry, Chemical Process and Engineering (IC3PE). 2018. https://doi.org/10.1063/1.5066485.
[13] Xiao M H, Wang W C, Wang K X, Zhang W, Zhang H. Fault diagnosis of high-power tractor engine based on competitive multiswarm cooperative particle swarm optimizer algorithm. Shock and Vibration, 2020; 2020: 1–13.
[14] Mohammed A, Djurovic S. Electric machine bearing health monitoring and ball fault detection by simultaneous thermo-mechanical fiber optic sensing. IEEE Transactions on Energy Conversion, 2021; 36(1): 71–80.
[15] Shi J, Wu X, Liu T. Bearing compound fault diagnosis based on HHT algorithm and convolution neural network. Transactions of the CSAE, 2020; 36(4): 34–43. (in Chinese)
[16] Trinh H C, Kwon Y K. A data-independent genetic algorithm framework for fault-type classification and remaining useful life prediction. Applied Sciences, 2020; 10(1): 368. doi: 10.3390/app10010368
[17] Chang L K, Wang S H, Tsai M C. Demagnetization fault diagnosis of a PMSM using auto-encoder and K-means clustering. Energies, 2020; 13(17): 4467. https://www.mdpi.com/1996-1073/13/17/4467
[18] Liu Y, Duan L, Yuan Z, Wang N, Zhao J. An intelligent fault diagnosis method for reciprocating compressors based on LMD and SDAE. Sensors, 2019; 19(5): 1041. https://doi.org/10.3390/s19051041
[19] Li S, He H, Li J. Big data driven lithium-ion battery modeling method based on SDAE-ELM algorithm and data pre-processing technology. Applied Energy, 2019; 242: 1259–1273.
[20] Lu N, Chen C, Shi W, Zhang J, Ma, J. Weakly supervised change detection based on edge mapping and SDAE network in high-resolution remote sensing images. Remote Sensing, 2020; 12(23): 3907. https://doi.org/10.3390/rs12233907.
[21] Rizwan-ul-Hassan, Li C, Liu Y. Online dynamic security assessment of wind integrated power system using SDAE with SVM ensemble boosting learner. International Journal of Electrical Power & Energy Systems, 2021; 125: 106429. https://doi.org/10.1016/j.ijepes.2020.106429.
[22] Chen L, Wang Z Y, Qin W L, Ma J. Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification. Signal Processing, 2017; 130: 377–388.
[23] Vincent P, Larochelle H, Lajoie I. Stacked de-noising auto-encoders: learning useful representations in a deep network with a local de-noising criterion. J Mach Learn Res 2010; 11: 3371–408.
[24] Vincent P, Larochelle H, Lajoie I, Bengio Y, Manzagol P A. Extracting and composing robust features with de-noising auto-encoders. Proceedings of the Twenty-Fifth Int Conference on Machine Learning, 2008; pp. 1096–103. https://doi.org/10.1145/1390156.1390294.
[25] Cai Z, Long Y, Shao L. Classification complexity assessment for hyper-parameter optimization. Pattern Recognition Letters, 2019; 125(7): 396–403.
[26] Yang L, Shami A. On Hyperparameter optimization of machine learning algorithms: Theory and practice. Neurocomputing, 2020; 415: 295–316.
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Published
2022-04-23
How to Cite
Qiu, Z., Shi, G., Zhao, B., Jin, X., Zhou, L., & Ma, T. (2022). Fault prediction of combine harvesters based on stacked denoising autoencoders. International Journal of Agricultural and Biological Engineering, 15(2), 189–196. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/6963
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Information Technology, Sensors and Control Systems
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