Hydraulic axial piston pump is broadly-used in aerospace, ocean engineering and construction machinery since it is the vital component of fluid power systems. In the light of the undiscoverability of ...its fault and the potential serious losses, it is valuable and challenging to complete the fault identification of a hydraulic pump accurately and effectively. Owing to the limitations of shallow machine learning methods in the intelligent fault diagnosis, more attention has been paid to deep learning methods. Hyperparameter plays an important role in a deep learning model. Although some manual tuning methods may represent good results in some cases, it is hard to reproduce due to the differences of datasets and other factors. Hence, Bayesian optimization (BO) algorithm is adopted to automatically select the hyperparameters. Firstly, the time–frequency images of vibration signals by continuous wavelet transform are taken as input data. Secondly, by setting some hyperparameters, a preliminary convolutional neural network (CNN) model is established. Thirdly, by identifying the range of each hyperparameter, BO based on Gaussian process is employed to construct an adaptive CNN model named CNN-BO. The performance of CNN-BO is verified by comparing with traditional LeNet 5 and improved LeNet 5 with manual optimization. The results indicate that CNN-BO can accomplish the intelligent fault diagnosis of a hydraulic pump accurately.
•An adaptive CNN model is constructed for fault diagnosis of hydraulic piston pump.•Using pump ontology information realizes the non-destructive condition monitoring.•The selection of model hyperparameters is completed by employing Bayesian theory.•Intelligent fault diagnosis is based on the time–frequency images of vibration signals.•CNN-BO represents high fault diagnosis accuracy and good generalization ability.
•A normalized CNN is constructed for fault diagnosis of hydraulic piston pump.•Multiple signals are analyzed and used for intelligent fault diagnosis.•Bayesian algorithm is introduced for automatic ...selection of hyperparameters.•Severity level of different failure and changeable conditions are discussed.•The BNCNN presents high accuracy and stability by experimental verification.
Hydraulic piston pump is known as one of the most critical parts in a typical hydraulic transmission system. It is imperative to probe into an accurate fault diagnosis method to guarantee the stability and reliability of the system. Due to the shortcomings of traditional methods, the development of artificial intelligence enlightens the intensive exploration for machinery fault diagnosis. In this research, a normalized convolutional neural network (NCNN) framework with batch normalization strategy is developed for feature extraction and fault identification. First, the batch normalization technology is introduced in the modeling to resolve the change of data distribution. Second, inspired by the intelligent algorithms, Bayesian algorithm is employed to automatically tune the model hyperparameters. The improved model is named BNCNN. Third, BNCNN is used for fault diagnosis based on synchrosqueesed wavelet transform. The experiments in a hydraulic piston pump are employed for the demonstration of the method. Moreover, the superior performance of the proposed method is validated by the contrastive analysis. The results reveal that BNCNN can accurately and steadily complete the fault classification of hydraulic pump.
This paper presents a novel vibration signal fusion algorithm using improved empirical wavelet transform and variance contribution rate to fuse three-channel vibration signals for weak fault ...detection of hydraulic pumps. Firstly, empirical wavelet transform (EWT) is utilized to decompose the three-channel signals into several AM–FM components. Then in accordance with the statistical characteristics of these component data, variance contribution rate is defined to measure the weight of component data points. A series of fusion coefficients are computed and assigned to every component point. Finally, these component points are fused into one single signal and Hilbert transform is conducted to demodulate the fault characteristic frequency for weak fault detection. Moreover, to address the issue of improper EWT spectrum segmentation, we introduce Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to improve EWT in the full space and the frequencies corresponding to outlier points are taken as the boundaries of spectrum segmentation. Therefore, the number of boundaries is more reasonable and the AM–FM components are more consistent with inherent components existing in the vibration signals of pumps. Results of simulation and experiment analysis demonstrate the good performance of the exhibited fusion algorithm in weak fault detection of hydraulic pumps.
•An improved EWT based on the full scale space and DBSCAN is proposed.•Variance contribution rate is applied to the fusion of vibration signals of pumps.•A novel signal fusion framework is proposed for weak fault detection of pumps.
•Summary of common gerotor gear profiles (epitrochoidal, hypotrochoidal, cycloidal).•Optimization presented to identify the optimal design space for each profile type.•Optimal design space of each ...profile type is compared.•No profile type is universally superior to the others.
Gerotors are positive displacement machines known for being cost-effective, durable, compact, and quiet and are used in many low-pressure applications. Nearly any smooth curve can define a gerotor gearset, yet three conventional profile types that are based on either epitrochoids, hypotrochoids, or cycloids are used almost exclusively in industry. Although each of the profile types has been known since the early 20th century, no extensive comparison has been made between them. In the present work a multi-objective optimization strategy using a genetic algorithm is used to find the Pareto front for each profile type when considering seven performance goals. The optimal designs of each profile type were combined, and a new set of Pareto designs was identified. The results showed that no single profile type can be considered universally better than the others. However, some observations about the general trade-offs for each profile type are presented, and the work serves as a baseline for development of novel gerotor profiles.
•This paper develops empirical mode reconstruction to augment faulty samples.•Empirical mode reconstruction can preserve intrinsic components in the original real samples.•The faulty and healthy ...classes are balanced through data augmentation.•The usefulness of the developed method has been validated in fault diagnosis of aviation hydraulic pumps.
A problem in data-driven fault diagnosis of civil aviation hydraulic pumps is that the faulty samples are much fewer than the healthy samples. To solve this problem, this paper develops a data augmentation method, namely empirical mode reconstruction (EMR), to augment faulty samples which preserve the intrinsic components in the original real samples. A significant property of the developed EMR is that the augmented samples are different but share very similar characteristics and category with the corresponding real samples, to properly guide the training of deep learning models, with the ultimate goal of yielding high diagnostic accuracies. First, the faulty training samples are converted to a series of intrinsic mode functions using empirical mode decomposition. Second, an intrinsic mode function is randomly selected and re-scaled with a weight randomly selected from a properly predefined range. Third, these intrinsic mode functions are used to reconstruct the 1-dimensional samples, which serve as the augmented samples. Besides, the mean values and standard deviations of the augmented samples are kept the same with the corresponding original sample. Finally, the efficacy of the developed EMR in imbalanced fault diagnosis of civil aviation hydraulic pumps is validated through a group of experimental comparisons.
•A HYPES system prototype using a reversible centrifugal pump was implemented.•An innovative multiphysics dynamic model was validated by comparison to experiments.•Variable pressure operation can be ...managed by the machine angular speed control.•The system performance was evaluated for efficiency and power control strategies.
The increasing development of storage systems connected to electrical networks is stimulated by network management issues related to recent energetic landscape evolutions such as the increasing integration of renewable production sources. Hydro-pneumatic systems seem to offer a clean and cheap energy storage solution among the set of existing storage techniques. The present study analyses an air–water direct contact accumulation system, in closed cycle, using a rotodynamic reversible pump/turbine. The use of a unique energy conversion machine and easy-to-recycle materials could lead to cost-effective, environmentally friendly storage technique with long service life. The paper is focused on the experimental implementation and analysis of the system in a Lab environment, and the modeling of its multi-physic dynamic behavior. To deal with the variable operating conditions of the system, two different real time control strategies of the hydraulic machine were successfully tested. Finally, the global system efficiency is discussed. The efficiency control strategy was achieved with a 31% round trip efficiency and the power control strategy lead to 5% and 23% precision on exchanged power in charge and discharge modes respectively. The multi-physic dynamic model led to a 4% error of turbine mode acceleration prediction showing the interest of such a modeling method for such transient systems.
The series electro-hydraulic hybrid powertrain has advantages in improving the dynamic characteristics and increasing the cruising range of battery rail vehicles. In order to reduce the large peak ...starting current of electric motor, an energy-saving starting method is proposed, which is using the hydraulic pump/motor to reversely drive the electric motor to restart at a speed, based on the energy reverse transfer characteristics between electric motor and hydraulic pump/motor. Firstly, the principle of restarting the electric motor at a speed is proposed based on the designed EH3. Then, PID is adopted to control the starting of electric motor which is driven by hydraulic powertrain. And a speed sensorless control algorithm is designed to control the restarting at a speed. Finally, aiming at the problem that the traditional simulation models of electro-hydraulic hybrid powertrain components are too simple and ideal. The estimation model of battery and SOC, dynamic efficiency model of hydraulic motor and dynamic iron loss model of PMSM are improved and optimized to improve the accuracy of powertrain simulation. The results both of co-simulation and experiments shows that the starting current can be reduced by 69.36 % at most and 64.91 % of battery consumption can be saved by restarting the electric motor at a speed.
•The principle of restarting the electric motor at a speed is proposed based on the designed EH3.•PID controller without speed sensor in hydraulic system.•The estimation model of battery and SOC.•Method of restarting the motor by hydraulic pump/motor.
As an important component of the hydraulic system, the performance of the hydraulic pump and motor greatly influences the performance of the whole hydraulic system, so it is necessary to study the ...hydraulic pump and motor test system. In this paper, the design of the mechanical power recovery hydraulic pump motor test system is completed through theoretical analysis and calculation; and the AMEsim simulation software is used to numerically simulate the test process of the hydraulic pump and motor test system, analyze the system loading method of the test system, recovery efficiency and simulation of typical working conditions; meanwhile, the control strategy of adjusting the single variable motor displacement to control the minimum overflow of the system is simulated and analyzed Simulation analysis is also performed for the control strategy of adjusting the univariate motor displacement to control the minimum overflow. Simulation results show that: the recovery efficiency of the mechanical power recovery hydraulic pump motor test system varies with the size of the overflow; when the system is the minimum overflow value, the optimal recovery efficiency of 75% is achieved, and the recovery performance is improved by 32% compared with other power methods. The mechanical power recovery hydraulic pump motor test system designed in this paper solves the shortcomings of high energy consumption, low recovery efficiency, and small application range of the previous single way of loading; at the same time, based on meeting the function and performance, the hydraulic pump and motor test system are integrated into one system, and the applicability and scalability are significantly improved.
•Propose a DCS algorithm to fuse the detail components for feature extraction.•DCS power entropy and singular entropy are extracted as initial features.•Present an algorithm based on Relative Entropy ...for the fusion of initial features.•A hydraulic pump degradation experiment is conducted to verify the proposed method.
Feature extraction is a key step of Prognostics and Health Management (PHM). To improve the feature performance, a method based upon the modified composite spectrum and relative entropy is proposed. The DCS algorithm is firstly presented by the modification of earlier composite spectrum for making fusion of multi-channel vibration signals. Considering Shannon entropy and Tsallis entropy, the DCS power entropy and singular entropy are initially extracted. According to max relation entropy criterion and gradual fusion strategy, the relative entropy algorithm is built to fuse the initial features into a new one, which is considered to be the degradation feature. Result of the application in hydraulic pump degradation experiment demonstrates that the proposed algorithm is feasible and the fused feature is effective to measure the performance degradation of pump.
