Abstract
Cooling water temperature of the combine harvester during operations can reflect the changes of its power consumption and even overloads caused by extreme workload. There is an existing problem when extracting water temperature information from harvesters: data redundancy and the loss of time series feature. To solve such problem, a Convolutional denoising autoencoder and Long-Short Term Memory Artificial Neural Network (CDAE-LSTM) hybrid model based on parameter migration is proposed to predict temperature trends. Firstly, the historical data of the combine harvester are taken into account to perform correlation analysis to verify the input rationality of the proposed model. Secondly, pre-training has been performed to determine the model’s initial migration parameters, along with the adoption of CDAE to denoise and reconstruct the input data. Finally, after the migration, the CNN-LSTM hybrid model was trained with a real dataset and was able to predict the cooling water temperature. The accuracy of the model has been verified by field test data gathered in June 2019. Results show that the root mean squared error (RMSE) of the model is 0.0817, and the mean absolute error (MAE) is 0.0989. Compared with the performance of LSTM on the prediction data, the RMSE improvement rate is 2.272 %, and the MAE improvement rate is 20.113 %. It is proven that the adoption of CDAE stabilizes the model, and the CDAE-LSTM hybrid model shows higher accuracy and lower uncertainty for time series prediction.
Highlights
Combine harvesters are the most widely used harvesting machinery in crop harvesting, and their performance often has a direct impact on harvest quality
Long-Short Term Memory Artificial Neural Network (LSTM) is an improved feedback neural network based on Recurrent Neural Network (RNN), which can effectively deal with the sensitive problems of time series and solve gradient disappearance’s problems on the premise of inheriting the characteristics of the RNN model [29]
A neural network based on Convolutional denoising autoencoder (CDAE) and Convolutional Neural Networks (CNN)-LSTM is proposed
Summary
Combine harvesters are the most widely used harvesting machinery in crop harvesting, and their performance often has a direct impact on harvest quality. The harvester load is not constant due to factors, such as crop density, surface undulation, and crop moisture content This makes the engine output power of the harvester show a trend of dynamic changes . A large number of noises and vibrations were detected in the combine harvester’s working situation [14]–[17]. They interfere with the feature extraction process, resulting in the final extracted key feature parameters showing nonlinearity, no marking, and mutual interference status. After viewing the above problems, this paper proposes an improved hybrid forecasting model to explore the relationship between the engine cooling water temperature and the combine harvester’s performance. The accuracy and feasibility of the model were verified through real experimental data
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