Variable-displacement pumps (VDPs) are widely used as core power components of high-pressure hydraulic systems due to their superior power density. The loose slipper fault is a typical failure type of the VDP, which can cause premature damage or even unexpected shutdown. The on-site VDPs usually work under time-varying operating conditions (TVOCs), and their pressure varies with the changing speed. These unique characteristics pose a new challenge to the fault diagnosis (FD) of the VDPs. This study introduces an innovative FD method named the multi-scale attention mechanism residual network (MSARN). The MSARN is equipped with three well-chosen components: multi-scale convolution module (MSCM), attention mechanism (AM), and residual connection. The integration of these three components facilitates the efficient extraction and fusion of meaningful multi-scale fault features, thereby preventing the overfitting problem. The experimental results obtained on the VDP validate the effectiveness and accuracy of the MSARN. The suggested method trains the model using data collected under partial constant and representative operating conditions, enabling the achievement of FD under TVOCs.

Online wear state recognition of key friction pairs in the axial piston pump is of great significance for stable operation and predictive maintenance of the whole hydraulic system. Edge computing (EC) meets the real-time and low-cost requirements of online wear state recognition whereas two challenges limit its application. One is that current fault diagnosis methods only focus on local fault information, causing inaccuracy and poor generalization ability in different working conditions. The other is that the computing power and storage of EC devices are limited. Therefore, a lightweight knowledge-distilled vision transformer (ViT) is proposed for online wear state recognition. A novel time-frequency domain stacked and channel-weighted pooling structure is proposed to directly process raw time series. To realize high accuracy and high generalization ability, a ViT-based teacher model is pretrained to learn local and global information. To narrow model capacity gap and adapt to the limited resource of the edge node, a novel student model with a simplified self-attention mechanism is proposed to mimic the structure of the ViT and learn from the pretrained teacher model through knowledge distillation. An edge node with functions of signal acquisition, data preprocessing, and wear state recognition is designed and the distilled student model is deployed into it. Comparison with other state-of-the-art methods, ablation experiment, and online verification experiment demonstrate that the proposed method trades off wear state recognition performance and hardware limitations.

Deep learning-assisted fault diagnosis has achieved significant success in recent years due to its capability of automatic feature learning and intelligent decision-making. Nonetheless, supervised methods are limited by their demands for annotations and fail to explore the growing unlabeled data generated by monitoring devices. An urgent need arises for an efficient approach to utilizing massive unlabeled data to facilitate fault diagnosis. However, existing unsupervised approaches usually rely on a single task for representation learning and lack the synergistic consideration of the cooperation of multiple tasks. To this end, a multi-task self-supervised approach is proposed to comprehensively mine diagnostic knowledge from unlabeled data. Three self-supervised tasks, namely Contrastive similarity matching, Pseudo Label predicting, and Intra-sample temporal relation reasoning (CPLI), are designed to learn representations of vibration signals at inter-instance, instance, and inner-instance levels, respectively. They are meticulously designed and combined to work corporately. The first task focuses on estimating similarities among pairs of augmented samples, while the second task helps this process by guiding the model to identify augmentation methods. As an important complement, the third task delves into the temporal relations among pieces of a time series. Three case studies demonstrate the superiority of the CPLI over state-of-the-art methods in terms of domain adaptability and diagnostic accuracy. These findings highlight its potential for leveraging unlabeled monitoring data to benefit fault diagnosis.

The piston pump is one of the key components of the hydraulic system. The valve plate, one of the key friction pairs of piston pumps, contributes most to the dynamical stability and operational reliability of the pump. Existing research on the valve plate of the piston pump does not mention the outer dead center (ODC) damage, which is necessary when considering the dynamic characteristics of the piston pump facing failure. This paper establishes a flow area model and a fluid-domain simulation model of the valve plate of the piston pump under normal and damaged conditions. The dynamic characteristics in the pressure and vibration signals under ODC damage are analyzed. A piston pump testing with ODC damage is carried on to verify the proposed models. The simulation and the experimental signals are compared by extracting the fault features of the valve plate ODC damage.

The fault diagnosis of variable displacement axial piston pumps has attracted huge attention since they are the power source of the hydraulic system. A notable superiority of the variable displacement pump is the capability of changing the swashplate angle to meet the requirements of different system loads. In most current research on the fault diagnosis of the variable displacement pump, however, the fixed displacement is usually postulated in a single test. Actually, time-variant displacement arouses the complex dynamic response, as well as unanticipated system state variations, which may confuse the classifier. In this paper, the fault detection of a variable displacement pump under random time-variant working conditions is investigated for the first time. The unscented Kalman filter with unknown input is utilized to estimate the system state and calculate the residual. The residual dynamic is then modelled by the sparse variational Gaussian process regression model. The extreme function theory gives a suitable threshold for determining whether the sample is faulty or not. The experimental investigation examines five fault types while tracking the random time-variant signal. Results with different fault sizes and noise levels validate the effectiveness of the proposed method. The comparative study demonstrates the proposed method achieves superior classification performance by an optimal trade-off between fault sensitivity and false alarm rate.

Multi-source fault patterns usually occur simultaneously in the axial piston pump. The coupled fault problem makes different fault patterns affect each other on the failure mechanism, leading to the aggravation of each fault pattern. Meanwhile, the features of different fault patterns will be aliased on the monitored signals, making some relatively weak fault patterns hard to be diagnosed. In this paper, a novel framework named temporal-spatial attention network (TSAN) is proposed to solve this problem. The key of the proposed method is to effectively extract fault-sensitive features from multi-source sensors on both temporal and spatial scales. First, to extract periodic fault-sensitive features of each sensor on the temporal scale, the multi-head attention-based temporal feature extraction model is constructed. Then, to fuse the extracted temporal features of each sensor and extract fault-sensitive features between different sensors on spatial scale, the multi-head attention-based spatial feature extraction model is constructed. The proposed learnable temporal feature token and spatial feature tokens effectively transmit the temporal features and spatial features, and improve the efficiency of the fault-sensitive temporal-spatial feature extraction. Compared with other state-of-the-art methods, comparison experiments demonstrate that the proposed framework improves the fault diagnosis accuracy under coupled fault problem by at least 22.06%.

During signal sampling process of steam turbine digital electric hydraulic control system（DEH） tests,situations that the instrument can not meet the requirements of Nyquist sampling due to the frequency of the primary components is over high may occur. To solve this problem, the envelope of the signal is calculated through Hilbert transform, and the valve closing time is calculated according to the envelope signal. However, the calculation accuracy is limited due to the time interval of the peak points. To improve the calculation accuracy, the undersampled signal is reconstructed, the primary frequency is analyzed by using fast Fourie transform（FFT）, and the primary frequency of the signal is determined based on the nature of the frequency domain. On this basis, the initial phase of the AC signals is calculated using the initial value, and the undersampled signals is reconstructed according to the frequency and initial phase. The closing time is calculated according to the difference between the reconstructed and sampled signals based on Akaike information criterion（AIC）. In comparison with the Hilbert transform method,the reconstruction method can improve the calculation accuracy. The reconstruction and analysis method can be used in all kinds of undersampled periodic signals, which can make up the shortcomings of hardware in DEH tests.

墙体抗震耐久性加载试验台需施加恒定纵向载荷,根据试验台技术要求,设计力恒定泵控差动缸,建立泵控缸控制系统数学及仿真模型,提出一种基于差值补偿的分段PID控制方法.结果表明:该控制方法可实现差动缸随墙体偏摆保持输出力恒定,响应时间可达0.035 s,输出力误差可控制在35 N以内,满足建筑墙体抗震试验加载标准.

The wear condition of the piston/cylinder pair is crucial to the performance and reliabil-ity of the axial piston pump.The hard piston surface,the soft cylinder bore surface,and the inter-face oil film affects each other during the wear process.Specifically,in the mixed lubrication region,the geometry of the hard piston surface asperity directly affects the wear of soft cylinder bore sur-face,while the asperities may deform or even degrade when penetrating and sliding against the cylinder bore.So far,there is no suitable method to simulate their coupled evolution.This paper proposed a wear process simulation model considering the real-time interaction between the elasto-plastic deformation of the piston surface asperity,the wear contour of the cylinder bore,and the lubrication condition of the interface.An offline library of the elasto-plastic constitutive behavior of the asperity based on the finite element method(FEM)is established as a part of the simulation model to precisely analyze the deformation and degradation of the asperity and quickly invoke them in the numerical wear process simulation.The simulation and experimental results show that the piston asperity and the cylinder bore contour converge to a steady state after running-in for about 0.5 h.The distribution of the simulated asperity degradation and wear depth is also verified by the experiment.

Increasing the rotating speed is considered as an efficient approach to upgrade the power-to-weight ratio in an axial piston pump, but penalized by more leakage and more severe wear resulting from the adverse cylinder block tilt. Previous studies mainly focused on the bearing characteristic of the valve plate/cylinder block pair, but the spline coupling also plays a key role in the undesired cylinder block tilt, which has been little studied. A theoretical model for the rotating assembly is presented to investigate the effect of the spline coupling length on the cylinder block tilt and the performance of the valve plate/cylinder block pair. A typical high-speed axial piston pump with the displacement of 5.2 mL/r at 10,000 r/min was studied by simulation and experiment. It shows that the optimal spline coupling length is one value increased by 2 mm from the original, bringing a remarkable leakage reduction under the high-speed condition by decreasing the cylinder block tilting angle. The experiment result matches well with the simulation. The influences of the spline coupling on the cylinder block tilt and the leakage were demonstrated.

The deterioration of the wear state in the key friction pairs will degenerate the performance of the axial piston pump. The wear rates differ under different wear states in practical applications. Therefore, the amount of monitoring data is significantly different under different wear states, which causes data imbalance problem. In addition, the distribution characteristics of the external signals are often drowned by environmental noise. To address these issues, a novel cylinder block dynamic characteristics-based data augmentation method is presented for wear state identification. Firstly, a cylinder block’s dynamic characteristics state perception method is proposed to obtain the time domain data. Then, to reduce the computing costs, the raw data is fused into multi-channel time–frequency domain data by the short-time Fourier transform (STFT) based method. Finally, the minority time–frequency data is augmented by the designed deep convolutional generative adversarial network (DCGAN). To verify the wear state identification performance of the proposed method, a convolutional neural network (CNN) is designed. Wear states injection experiments are adopted to verify the feasibility of the proposed method. The advantage of the developed method is that it obtains prominent data distribution characteristics from internal signals to enhance the performance of the data augmentation process.

Deep learning has become a popular approach for fault diagnosis due to its powerful feature extraction and adaptability. However, its reliance on extensive annotations poses challenges in real-world applications. To confront this issue, this article proposes the CLTrans, a contrastive learning-based knowledge transfer method for semi-supervised fault diagnosis. CLTrans utilizes large-scale unlabeled data to benefit downstream tasks by simply performing unsupervised similarity matching. A feature encoder pre-trained by CLTrans can extract discriminative representations of vibration signals and can efficiently adapt to various tasks, even with data under different distributions. Experimental results of inner-dataset and inter-dataset knowledge transfer demonstrate that CLTrans outperforms conventional deep learning and state-of-the-art semi-supervised fault diagnosis approaches in terms of accuracy and domain adaptability, especially under limited labels. The capability of unsupervised knowledge mining and transfer allows for reducing the burden of data collection and annotation.

松靴故障会引起轴向柱塞泵的振动和噪声增大,甚至演变为脱靴导致泵与系统严重损坏.为检测松靴故障,需提取故障特征,但松靴故障特征尚不明确.为此,分析了松靴故障机理,搭建了柱塞泵机液耦合仿真模型,建立了松靴与壳体轴向振动信号间的映射关系.采用倒频谱分析方法,提取了正常与两种松靴程度下的信号特征.搭建了轴向柱塞泵单松靴故障试验台,通过实际跑合得到了两种松靴量的柱塞滑靴组件,在不同转速和压力工况下采集壳体轴向振动信号.基于倒频谱方法分析松靴的故障特征,并验证仿真结果.结果表明:倒频谱对松靴故障振动信号敏感,其中转轴基倒频率对应频谱中单松靴故障产生的冲击频率,是松靴的故障特征;倒频谱转轴基倒频率一次谐波处的幅值,随着松靴故障程度的增大而增大.

Electro-hydraulic controlled variable-displacement pump (EHVDP) is a power source of the hydraulic system, which is extensively used in the electro-hydraulic system due to its high efficiency and controllability. Thus, for a good operation of the hydraulic system, effective fault diagnosis techniques are crucial to avoid unexpected breakdown and failure. However, the data-driven fault diagnosis methods, which are commonly adopted in axial piston pumps, suffer from a lack of considerably effective data. Modeling uncertainty due to the swashplate moment has not been adequately considered, though some attempts have been made for the model-based methods. In this article, the pressure transition is assumed to simplify the swashplate moment. A Kalman filter with unknown input for the EHVDP is used to estimate the additional uncertainty due to the simplification of the swashplate moment. A cumulative sum (CUSUM) based residual evaluation is utilized to detect the fault of the EHVDP. The test bench has been established and three typical failure modes of the EHVDP, including the control valve, displacement regulation mechanism, and rotating group are investigated by the experiments. Experimental results under different working conditions validate the effectiveness of the proposed fault detection method. Comparative results show the superiority of the proposed method in sensitivity and rapidity over the general method not considering unknown swashplate moment.

Working under a wide range of conditions, especially extreme conditions, causes significant energy dissipation and thermal deformation that affect the micro-scale oil film clearances between the piston/cylinder interface of electro-hydrostatic actuator (EHA) pumps. This study aims to provide a tolerance design guideline for the piston/cylinder interface under a wide range of working conditions, including extreme speed, pressure, and temperature domains. The guideline is developed based on the comprehensive consideration of friction force and leakage and the constraint of sticking threshold through a Thermal-Fluid-Structure model. The optimized clearance is presented in the form of tolerance to account for realistic manufacturing precision. The accuracy of the optimal results is further validated through experiments.

为了提高斜轴式柱塞马达轴承的使用寿命,研究和分析缸体摆角、轴承运行温度和柱塞合力偏载对轴承寿命的影响规律.首先,利用Romax软件建立斜轴式柱塞马达轴承的受力模型;然后,根据载荷、主轴转速和润滑油黏度,计算了不同缸体摆角对轴承寿命的影响,进一步分析轴承运行温度和柱塞合力偏载对轴承最小油膜厚度与轴承寿命的影响;最后,以轴承寿命大于2000 h为目标,确定合理的柱塞球窝中心分布圆半径.结果表明:缸体摆角、轴承运行温度和柱塞合力偏载都对轴承寿命有较大影响,由于承载力与力矩的能力不同,两轴承的寿命变化趋势并不相同;为了满足该型号斜轴式柱塞马达轴承寿命的设计要求,柱塞球窝中心分布圆半径应不超过72 mm,同时马达工作过程中需要避免轴承运行温度过高.

Increasing the rotating speed of the axial piston pump is effective in improving the power-to-weight ratio. However, the cylinder block tilts severely at high speed, which causes significant leakage. In this paper, a dynamic model of the rotating assembly in a high-speed axial piston pump is established to investigate the tilt behavior of the cylinder block when fully considering the relevant factors within the whole rotating assembly, such as the tilt moments due to the inertia of piston-slipper assemblies and the periodic pressure in piston chambers, the elastic deformation of the shaft, and the nonlinear bearing characteristics of the oil film. Furthermore, the cylinder block tilt behavior is measured to validate the established dynamic model. The theoretical and experimental results show that the tilt angle of the cylinder block increases with the increasing rotating speed. And at high rotating speed, the cylinder block tilts much more severely under low outlet pressure. Finally, the bearing capacities of the oil film and the spline coupling are analyzed to find out the dominant factors affecting the tilt behavior of the cylinder block.

Wear state identification of the axial piston pump is of great importance to secure the modern hydraulic system. Offline intelligent fault diagnosis methods are significant to solve the wear state identification problems. Nevertheless, these methods cannot meet the demand of real-time wear state identification. In this paper, an accurate and online wear state identification method based on edge computing using feature selected artificial neural network (FSANN) is proposed for the axial piston pump key friction pair. To reduce latency, an edge end node including data collection, signal pre-processing, feature extraction, fault classification is established. To cut down the amount of calculation and transmission while retaining accuracy, features sensitive to the fault are selected. The embedding performance of one-against-all support vector machine (OAA-SVM), artificial neural network (ANN), deep belief network (DBN) is compared and ANN is chosen as the embedded diagnostic model. The experimentally verified accuracy of this method is 99.0%. The single diagnosis time(SDT) is about 0.24s. Compared to transmitting raw data to the host computer, this method cut down the amount of data by about 200 times. The proposed method could diagnose the slipper wear state accurately and quickly and provide a potential way for real-time fault diagnosis for axial piston pump.

The surface modification technology affects the friction and wear resistance of the piston/cylinder pair in axial piston pumps. However, in the early operating stages, the bearing surface cannot provide optimal load-bearing and lubrication conditions, resulting in a high wear rate and rapid loss of the surface modification layer. In this paper, the variation in wear of the piston/cylinder pair with the operating time is obtained by a wear degradation model. The wear contour at the lowest wear rate is derived to guide the design of a pre-machined surface contour. The experimental results show that the pre-machined contour reduces the wear rate during the initial operating stage, with the surface modification layer covering the pre-machined surface playing a greater role.