Authors

Abstract

The corn grain harvester serves as an example of complex farming machinery with a condition monitoring system that collects a lot of working condition data, making it challenging to identify the true change pattern due to the data coming from the equipment in various states. Firstly, the overall structure of the corn grain harvester is analyzed, and the common causes and mechanisms of corn grain harvester failures are analyzed, leading to the cutting table as the main research object; Secondly, by collecting historical failure data of corn grain harvester as well as real-time failure information for collation and pre-processing, eliminating interference such as noise and missing data, establishing a failure matrix, extracting internal characteristics between failure causes and establishing a mapping between failure causes and failure phenomena; Finally, the future failure phenomena of the corn grain harvester are predicted according to different failure causes. The simulation analysis results show that the self-coding neural network fault prediction model can better predict the occurrence probability and types of faults and provide data support for fault maintenance and decision making of agricultural machinery.

Abstract in Chinese

以玉米籽粒收获机为代表的复杂农机设备状态监测系统记录了大量的工作状态数据，由于数据来源于不同状态的设备导致其真实变化规律难以发现造成了传统故障预测模型准确率较低，因此我们提出了一种基于自编码神经网络的玉米籽粒收获机故障预测模型。首先分析了玉米籽粒收获机的整体结构，对玉米籽粒收获机故障的常见成因与机理进行分析，引出割台为主要研究对象；其次通过收集玉米籽粒收获机的历史故障数据以及实时故障信息进行整理和预处理，消除噪音和缺失数据等干扰，建立故障矩阵，提取故障原因之间的内部特征，建立故障原因与故障现象之间的映射；最后根据不同故障原因预测未来玉米籽粒收获机的故障现象。仿真分析结果表明，自编码神经网络故障预测模型能够更好的预测发生的故障概率及种类，为农业机械的故障维修与决策提供数据支持。