•CSIAM is developed to focus on both local channel correlation and global scale correlation.•Global important features are further highlighted while useless features are further suppressed.•DCSIAN is ...developed for achieving accurate fault diagnosis under various noisy environment.•DCSIAN can learning complementary and rich diagnostic information at different scales.
Channel attention (CA) has been wildly applied to enhance the diagnosis performance of multiscale convolution (MSC)-based diagnosis methods. Nevertheless, most of the existing CA modules only consider the internal local correlation among different channels within each scale feature, but ignore the global correlation among different scales, restricting further improvement. To address this issue, a novel deep cross-scale interactive attention network (DCSIAN) is developed to achieve accurate fault diagnosis for aviation hydraulic pumps under high-noise environments. Specifically, a novel cross-scale interactive attention module (CSIAM) is developed and introduced into MSC to learn complementary and rich multiscale features from original vibration signals. CSIAM adopts two cascaded submodules to focus on local channel correlation and global scale correlation simultaneously. Local channel correlation is used to adaptively measure the importance of different channel feature within each scale, while global scale correlation is used to dynamically determine the contribution of each scale feature to the final diagnostic result. In this way, the fault-related information at different scales can be fully captured and utilized. Finally, the effectiveness of DCSIAN is validated by a series of experimental comparisons on an aviation hydraulic pump dataset and a bearing dataset with various types noise.
Aiming at the problem of poor consistency between the enhanced samples and the original samples in the current data enhancement methods. In this paper, we propose a data enhancement method with ...improved symplectic geometry reconstruction. The method expands a sufficient number of augmented samples while ensuring high similarity to real fault samples. First, the components to be augmented and their weights are selected from the decomposed symplectic geometric modal components using a random sampling method. Second, a data-weighted scaling criterion is formulated to augment the modal component amplitudes. Then, the summation and reconstruction of all amplitude enhancement components are performed on the basis of ensuring that the statistical characteristics of the enhanced samples remain consistent with the original samples. Finally, the proposed method is combined with the deep residual network, and the experimental dataset of hydraulic pump failure simulation is utilized to verify the effectiveness of the proposed method in obtaining the enhanced fault sample information. The diagnostic results show that the fault diagnosis model established by the method has higher accuracy and convergence ability in hydraulic pump data imbalance fault diagnosis compared with other comparative models.
Performance degradation assessment (PDA) of hydraulic pumps is of great importance to preserve operational reliability and ensure safety of hydraulic systems. PDA of hydraulic pumps relies heavily on ...degradation monitoring data such as vibration data, acoustic emission data and oil data. However, besides the inherent degradation of pumps, the time-varying load conditions (e.g. output pressure) have a significant influence on the behavior of degradation data. This makes PDA more difficult under varying conditions. To address this issue, this paper proposes a conditional factor variational auto-encoder (CFVAE) model whereby variational theory is firstly applied to degradation data decoupling and degradation characteristics extraction. In the training phase, degradation data decoupling is realized by punishing the total correlation term of the model with a hyper-parameter γ and the degradation characteristics can be extracted from latent code units of the model. However, the punishment can result in large reconstruction losses of the model which is not conducive to degradation assessment. Then the degradation label information is integrated into the decoder of the model to decrease reconstruction loss caused by data decoupling. Moreover, a new metric based on inter-class distance and intra-class divergence is introduced to optimize the hyper-parameter and select the best latent code units for degradation characteristics extraction and description. Using these techniques, the CFVAE models corresponding to all degradation states can be well trained. In the testing phase, degradation data from unknown degradation states is fed into all of the trained CFVAE models respectively and minimum distance criterion is introduced to predict the actual degradation state. Taking return oil flow as degradation data, the proposed CFVAE model along with several advanced methodologies is applied to degradation assessment for hydraulic pumps under varying conditions. The assessment results of ten experimental cases verify that the proposed CFVAE model can adapt better to change of conditions and has higher assessment accuracy than other methodologies. The proposed model also shows good robustness in multiple cases, making it likely to apply this model to other mechanical components.
•Firstly apply the variational theory to extract degradation characteristics of hydraulic pumps.•Construct a novel CFVAE model to assess performance degradation of hydraulic pumps.•A new metric is introduced for hyper-parameter optimization.•An effective assessment strategy based on minimum distance criteria is proposed.•The proposed degradation assessment method has high accuracy and good robustness.
Displacement control of positive displacement machines has been a part of fluid power since the early days. Some of the early hydraulic presses already used two different displacement settings, ...though this was realised by using two different pumps rather than changing the displacement. Later, radial motors with variable stroke length appeared, followed by other designs of variable machines, such as swashplate machines, bent-axis machines, and variable vane machines. All these solutions control the displacement by varying the volume difference of the displacement element – but there are other ways of achieving this. Most have not passed the research state, but some are commercially available. In this paper, different ways of varying the displacement are presented and classified. The classification divides concepts into either control of displaced fluid or control of usage of displaced fluid. In turn, these concepts can be either on system level or displacement element level. This results in four main classes, which to some extent can describe the characteristics of the control.
In general, deep learning-based fault diagnosis methods need a large number of training samples, which are often not available in real applications. Aiming at this problem, this article develops a ...new data augmentation method, i.e., randomized wavelet expansion (RWE), to generate a set of synthesis samples that share similar characteristics with the original sample. The first key point is that the amplitudes of wavelet coefficients at a randomly selected frequency band are enlarged through random expansion. Another key point is that the synthesis samples are processed to have the same mean values and standard deviations as their corresponding original sample. Afterward, the synthesis samples are used as the training dataset to train a deep convolutional neural network (CNN) for implementing the few-shot fault diagnosis of aviation hydraulic pumps. Finally, the performance has been validated through a series of experiments.
Hydraulic drive can be defined as a set of technical functions through which the transmission of mechanical energy is carried out, from a driver element to a driven one, using a hydraulic ...environment. Hydraulic pumps are hydrostatic, flow-generating, wide-use power generation machines that receive mechanical energy produced by a force machine and convert it into hydrostatic energy, which imprints it to the hydraulic working environment. In the paper, the graphic modeling of two different types of hydraulic pumps is performed, a gear pump and an axial piston pump, as well as a calculation that highlights the value differences of flow, moment and hydraulic power of the two types of pumps.
The structure of sintered steel pores and their distribution is described in terms of breaking model. The found correspondence between pore distribution and crack is examined. The most important ...theoretical problem is to find the correspondence between macro- and mesoscopic picture of fatigue.
The geometrical structure of sintered steel pores and their distribution is described in terms of fractals. The found correspondence between pore distribution and crack is examined. The fractal ...dimensions of crack generated from pores model (due to the simple proposed geometrical rule) and real fatigue crack are compared.
The lubrication characteristics of fuel pumps with spiral grooves are investigated by numerical analysis. The two-dimensional Reynolds equation is used to evaluate lubrication characteristics with ...variations in grooves and viscosity. Moreover, the equilibrium equation of moment and forces in the horizontal and vertical directions is used to determine the motion of the plunger. The lubrication characteristics of the fuel pump with spiral grooves are compared to those without spiral grooves. The lubrication characteristics of the pump are investigated by comparing the film parameters. The fuel pump with spiral grooves is effective for relieving any uneven pressure distribution surrounding the plunger and can improve the lubrication characteristics. The application of spiral grooves is shown to be more effective in a low-viscosity condition compared to in a high-viscosity condition.
The modelling of positive displacement machines has to deal with the complexity of solving the flow through the unit in presence of simultaneous macro and micro-motions of the moving parts. In this ...paper, the main phenomena characterizing the operation of the spur external gear units are successfully analyzed by means of a numerical model developed by the authors. The model, here referred as
HYdraulic GEar machines Simulator (HYGESim), consists of different modules: a fluid dynamic model, a model for the evaluation of the movements of the gears’ axes of rotation and a geometrical model. When performing a HYGESim simulation, these models are executed in a co-operative simulation. Starting directly from the CAD drawing of the unit as input, the simulation tool permits to describe the main features of the flow though the machine and to evaluate the possible wear of the casing wear accounting for a detailed description of the geometry of the internal components (i.e. teeth’s profile, design of lateral bushes).
The paper describes the modelling approach and the main potentials of the simulation tool, pointing out how it can be utilized for design purposes. As a matter of fact, HYGESim permits to analyse the effects of the main design parameters on important aspects like efficiency, internal pressure peaks, local cavitation and fluctuations of flow at unit’s ports (associated with fluid borne noise). The presented tool also allows the evaluation of the radial movements of the gears’ axes of rotation resulting from the forces exerted on both gears, thus permitting to study the balancing feature related to a certain design.
The paper also presents several comparisons between simulation results and experimental data coming from an experimental activity specifically performed for validation of the presented tool.
