习晨博, 杨光友, 刘浪, 刘景, 陈学海, 马志艳. 基于SDAE-BP的联合收割机作业故障监测[J]. 农业工程学报, 2020, 36(17): 46-53. DOI: 10.11975/j.issn.1002-6819.2020.17.006
    引用本文: 习晨博, 杨光友, 刘浪, 刘景, 陈学海, 马志艳. 基于SDAE-BP的联合收割机作业故障监测[J]. 农业工程学报, 2020, 36(17): 46-53. DOI: 10.11975/j.issn.1002-6819.2020.17.006
    Xi Chenbo, Yang Guangyou, Liu Lang, Liu Jing, Chen Xuehai, Ma Zhiyan. Operation faults monitoring of combine harvester based on SDAE-BP[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2020, 36(17): 46-53. DOI: 10.11975/j.issn.1002-6819.2020.17.006
    Citation: Xi Chenbo, Yang Guangyou, Liu Lang, Liu Jing, Chen Xuehai, Ma Zhiyan. Operation faults monitoring of combine harvester based on SDAE-BP[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2020, 36(17): 46-53. DOI: 10.11975/j.issn.1002-6819.2020.17.006

    基于SDAE-BP的联合收割机作业故障监测

    Operation faults monitoring of combine harvester based on SDAE-BP

    • 摘要: 为了解决联合收割机作业故障的非线性特征信号难以提取的问题,该研究提出了一种基于堆叠去噪自动编码器(Stack Denoising Auto Encoder, SDAE)和BP神经网络(Back Propagation,BP)融合的联合收割机作业故障监测及诊断的方法(SDAE-BP)。以转速传感器采集联合收割机脱粒滚筒转速、籽粒搅龙转速、喂入搅龙转速、杂余搅龙转速、风机转速、输送链耙转速、割刀频率以及逐稿器振动频率,并将采集的数据集作为系统的输入。利用SDAE提取输入信号的深层次特征,并由BP神经网络辨识收割机作业状态,实现联合收割机故障监测。在SDAE-BP模型训练过程中,去噪自动编码器(Denoising Auto Encode, DAE)依次经带有不同分布中心噪声的原始数据进行训练,然后将其堆叠,并通过误差反向传播算法对模型参数进行优化,以提升模型识别故障性能和泛化能力。试验结果表明,对于2018年联合收割机田间试验数据,模型的故障诊断准确率达到99.00%,与SDAE和BP神经网络相比,分别提高了1.5和4.5个百分点。将SDAE-BP故障诊断模型用2019年的试验数据进行更新,并用2018年和2019年试验数据进行测试,结果表明,更新后的模型对2018年试验数据的故障识别准确率为99.25%,对2019年试验数据的故障识别准确率为98.74%,更新后模型在2019试验数据集上的故障识别准确率较未更新模型提高了6.52个百分点。该文所建模型能够准确识别联合收割机的故障类型,且具有较好的鲁棒性,对旋转型机械故障监测及预警具有参考价值。

       

      Abstract: In order to solve the problem of deep feature extraction of nonlinear feature signal of operation faults of combine harvester and improve the diagnosis accuracy of fault recognition, a method based on Stack Denoising Auto Encoder -Back Propagation neural network(SDAE-BP) model was proposed in this study. Lovol RG50 combine harvester was used as the test prototype, according to the analysis of the working procedure and failure mechanism of each component of combine harvester, NJK-5002C speed sensor was used to collect the rotation speed signal of feeding auger , impurity auger , grain beat auger, fan, threshing cylinder and conveyor chain harrow, and the frequency signal of sickles and straw walker, and the collected data sets were used as the input of the monitoring system. The monitoring system was consist of IPC-610L embedded industrial computer, USB-4711 data acquisition module, EYOYO interactive display screen and LTE-1101J sound and light alarm device. The SDAE model was used to extract the deep feature of the input signal, the extracted deep feature was sent to the BP neural network and then the operation status of combine harvester was classified. During the training process, the first step was to train the DAEs (Denoising Auto Encoder) under different gaussian noises distribution center respectively, after all the DAEs was trained, stacking the DAEs all together and fine-tuning the model's parameters through the error back propagation algorithm. The noise center of DAE was far away from 0, which meaned that the original data was seriously damaged, the model could learn global coarse grained features, the noise center of DAE was close to 0, which indicateds that the damage degree of original data was low, and the model could learn local coarse grained features, in other words, training each DAE with different gaussian noise centers, the SDAE model would learn both global and local coarse grained characteristics which was of great significant to improve the model's expressive ability of deep feature. The experiments were carried out in 2018 to verify the proposed method, and the results showed the diagnostic accuracy rate reached 99.00%, which improved by 1.50 and 4.50 percentage points respectively compared with SDAE and BP neural network. The DAE-BP model was updated with the test data of 2019, and tested with the data of 2018 and 2019. The results show that the fault identification accuracy rate of the updated model for the test data in 2018 was 99.25%, and that for the test data in 2019 was 98.74%,which increased by 6.52 percentage points than that of the unupdated model. The model established in this paper can accurately identify the fault type of combine harvester, and has good robustness, which has reference value for the fault monitoring and early warning of rotating machinery.

       

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