基于CBLRE模型的轴向柱塞泵空化状态检测研究

Cavitation state detection of axial piston pump based on CBLRE model

空化现象的产生严重制约了轴向柱塞泵向高速高压方向发展,需要对柱塞泵的空化状态检测与智能故障诊断展开研究,因此,结合深度学习网络与非线性分类器的优点,提出了一种基于CBLRE(CNN+BiLSTM+RELM)模型的柱塞泵空化状态识别方法(检测模型)。首先,对不同空化状态下柱塞泵的一维原始振动信号进行了数据增强,并对其进行了标准化处理;然后,利用卷积神经网络(CNN)自动提取信号的特征,并对其进行了特征降维处理;利用双向长短期记忆(Bi-LSTM)网络学习特征序列的时间依赖性,利用正则化极限学习机(RELM)的非线性分类器对特征进行了分类,实现了对柱塞泵的空化状态检测与智能故障诊断;最后,为测试CBLRE模型的性能,搭建了实验平台,在此之上将CBLRE模型与其他模型进行了对比,分析了该模型在不同工况下的性能表现。研究结果表明:该模型的结构稳定、训练时间短,且在不同负载下均可保持良好的泛化性能,空化状态识别率均达到99%以上,该结果验证了柱塞泵空化状态识别方法的有效性;此外,该模型还可有效识别空化现象与柱塞泵的其他故障。

Cavitation phenomenon seriously restricted the development of axial piston pump to the direction of high speed and high pressure. It was necessary to study the cavitation state detection and intelligent fault diagnosis of piston pump.Therefore, combining the advantages of deep learning network and nonlinear classifier, a cavitation state recognition method(detection model) of plunger pump based on CBLRE(CNN + BiLSTM + RELM) model was proposed. Firstly, the one-dimensional original vibration signals under different cavitation states of the piston pump were enhanced and standardized. Then, the convolutional neural networks(CNN) network was used to automatically extract the features from shallow to abstract signals and carry out feature dimension reduction. Bi-directional long short-term memory(Bi-LSTM) network was used to learn the time dependence of feature sequences. Regularized extreme learning machine(RELM)nonlinear classifier was used to classify, and the cavitation state detection and intelligent fault diagnosis of piston pump were realized. Finally, in order to test the performance of CBLRE model, an experimental platform was built. The CBLRE model was compared with other models, and its performance under different working conditions was also compared. The experimental results show that the model proposed has stable structure, short training time, and good generalization performance under different loads. The recognition rate of cavitation state can reach more than 99%.The results verify the effectiveness of the cavitation state identification method of piston pump. In addition, cavitation phenomenon and other faults of piston pump can be identified effectively.