| 周浩,冯浩,周晨曦,殷晨波,曹东辉,马守磊.基于SSA-LSTM-Attention的典型液压系统压力数据高精度预测方法[J].电子测量与仪器学报,2025,39(8):241-249 |
| 基于SSA-LSTM-Attention的典型液压系统压力数据高精度预测方法 |
| High accuracy prediction method for typical hydraulic systempressure data based on SSA-LSTM-Attention |
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| DOI： |
| 中文关键词: 挖掘机 液压系统 压力预测 工程机械 LSTM |
| 英文关键词:excavators hydraulic system pressure prediction engineering machinery LSTM |
| 基金项目:中国博士后科学基金（2025T180363）、江苏省自然科学基金（BK20221342）、国家自然科学基金（52105064）项目资助 |
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| Author | Institution |
| Zhou Hao | School of Artificial Intelligence, Nanjing University of Information Science & Technology, Nanjing 210044，China |
| Feng Hao | School of Artificial Intelligence, Nanjing University of Information Science & Technology, Nanjing 210044，China |
| Zhou Chenxi | School of Artificial Intelligence, Nanjing University of Information Science & Technology, Nanjing 210044，China |
| Yin Chenbo | School of Mechanical and Power Engineering, Nanjing University of Technology,Nanjing 211816, China |
| Cao Donghui | Sany Heavy Machinery Co., Ltd., Suzhou 215334, China |
| Ma Shoulei | School of Mechanical and Power Engineering, Nanjing University of Technology,Nanjing 211816, China |
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| 中文摘要: |
| 液压系统压力传感器作为挖掘机自动控制系统的核心元件，其可靠性直接影响整机操控性能。针对复杂恶劣工况下压力传感器失效导致控制系统信号缺失的关键问题，提出一种基于深度学习的高精度压力数据实时预测方法。首先，基于37吨级挖掘机电液比例控制系统构建试验平台，采集实际挖装作业工况下多源传感器数据；其次，采用最大信息系数法进行特征相关性分析，将125维原始数据降维至10维有效特征，并通过卡尔曼滤波与标准化处理构建高质量数据集；进而设计基于注意力机制的特征权重分配模块，结合麻雀搜索算法（sparrow search algorithm，SSA）优化长短期记忆神经网络 （long short term memory，LSTM）的超参数配置，构建SSA-LSTM-Attention融合预测模型。通过对比卷积神经网络 （convolutional neural network，CNN）、循环神经网络（gate recurrent unit，GRU）、LSTM等典型预测模型的实验验证，该方法在关键压力数据预测中展现出显著优势。实验结果表明，相较于传统LSTM模型，SSA-LSTM-Attention模型的平均绝对误差和均方根误差分别降低54.45%和54.56%。研究证实所提方法能有效解决传感器失效工况下的数据补偿问题，为工程机械智能控制系统容错设计提供理论支撑。 |
| 英文摘要: |
| As the core component of automatic control system of hydraulic excavator, the reliability of hydraulic system pressure sensor directly affects the control performance of the whole excavator. To solve the key problem of loss of control system signal caused by pressure sensor failure under complex and severe working conditions, this study proposes a high-precision pressure data real-time prediction method based on depth learning. Firstly, based on the electro-hydraulic proportional control system of 37t hydraulic excavator, a test platform is established to collect the data of multi-source sensor under the actual excavation and loading operation condition; Secondly, the maximum information coefficient method is used to analyze the feature correlation, and the 125-dimensional original data is reduced to the 10-dimensional effective feature, and the high-quality data set is constructed by means of Kalman filtering and standardization; Then, the feature weight distribution module based on attention mechanism is designed, and the super-parameter configuration of long short term memory(LSTM) network is optimized by combining with sparrow search algorithm(SSA) to construct the SSA-LSTM-Attention fusion prediction model. Through the experimental verification of seven typical prediction models, such as convolutional neural network (CNN), gate recurrent unit (GRU) and LSTM, this method shows significant advantages in key pressure data prediction. The experimental results show that the mean absolute error and root mean square error of SSA-LSTM-Attention model are reduced by 54.45% and 54.56% respectively compared with the traditional LSTM model. The research proves that the proposed method can effectively solve the data compensation problem under sensor failure condition, and provide theoretical support for the fault tolerant design of intelligent control system of engineering machinery. |
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