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A data indicator-based deep belief networks to detect multiple faults in axial piston pumps

机译：基于数据指标的深度置信网络，可检测轴向柱塞泵中的多个故障

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摘要

Detecting faults in axial piston pumps is of significance to enhance the reliability and security of hydraulic systems. However, it is difficult to detect multiple faults in the hydraulic electromechanical coupling systems because the fault mechanism of some faults is unclear. In this paper, a method using deep belief networks (DBNs) is proposed to detect multiple faults in axial piston pumps. Firstly, for each individual fault, all the data indicators extracted from the raw signals in time domain, frequency domain and time-frequency domain are calculated to construct training and testing samples. Then, the constructed samples are fed into DBNs to classify the multiple faults in axial piston pumps. With restricted Boltzmann machine (RBM) stacked layer by layer, DBNs can automatically learn fault features. Numerical simulations using the benchmark data of five faults in rolling bearings are classified by the present method to select the relative optimal combination of indicators. The classification results are also compared with those commonly used support vector machine (SVM) and artificial neural network (ANN) to manifest the classification accuracy of the present method. Experimental investigations are performed to classify four faults in an axial piston pump. The classification accuracy ratio is 97.40%, which confirms the feasibility and effectiveness of multiple faults detection in axial piston pumps using DBNs.
机译：检测轴向柱塞泵中的故障对于提高液压系统的可靠性和安全性具有重要意义。然而，由于某些故障的故障机理尚不清楚，因此很难检测液压机电耦合系统中的多个故障。在本文中，提出了一种使用深度置信网络（DBN）的方法来检测轴向柱塞泵中的多个故障。首先，针对每个单独的故障，计算从时域，频域和时频域中的原始信号中提取的所有数据指标，以构造训练和测试样本。然后，将构造的样本输入到DBN中，以对轴向柱塞泵中的多个故障进行分类。使用受限的Boltzmann机器（RBM）逐层堆叠，DBN可以自动了解故障特征。利用本方法对滚动轴承五个故障的基准数据进行了数值模拟，选择了相对最优的指标组合。还将分类结果与常用的支持向量机（SVM）和人工神经网络（ANN）进行比较，以证明本方法的分类准确性。进行实验研究以对轴向柱塞泵中的四个故障进行分类。分类准确率为97.40％，证实了使用DBN进行轴向柱塞泵多故障检测的可行性和有效性。

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