Fault prediction of combine harvesters based on stacked denoising autoencoders

作     者：Zhaomei Qiu Gaoxiang Shi Bo Zhao Xin Jin Liming Zhou Tengfei Ma 

作者机构：College of Agricultural Equipment EngineeringHenan University of Science and TechnologyLuoyang 471003HenanChina China Academy of Agricultural Mechanization ScienceBeijing 100083China 

出 版 物：《International Journal of Agricultural and Biological Engineering》 (国际农业与生物工程学报（英文）)

年 卷 期：2022年第15卷第2期

页      面：189-196页

核心收录：

学科分类：0828[工学-农业工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：The work was sponsored by the Intelligent Manufacturing Comprehensive Standardization Project(No.2018GXZ1101011) the National Key Research and Development Program of China Sub-project(No.2016YFD0701802) the Natural Science Foundation of Henan(No.202300410124) 

主　　题：fault prediction combine harvester stacked denoising autoencoders support vector machines 

摘      要：Accurate fault prediction is essential to ensure the safety and reliability of combine harvester *** 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 *** general,SDAE contains autoencoders and uses a deep network architecture to learn complex non-linear input-output relationships in a hierarchical *** 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 *** experimental results show that the method can achieve accurate and efficient combine harvester fault *** particular,the experiments used 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 was 95.31%,which has better robustness and generalization ability compared to SVM(77.03%),BP(74.61%),and SAE(90.86%).