Actuators of a robot that physically interacts with objects or human require backdrivability to obtain force controllability, impact resistance and compactness. An electro-hydrostatic actuator (EHA) ...is a servo pump type hydraulic system that has a potential to satisfy backdrivability. However, the previous mechanical design of a revolute EHA did not achieve compact design compared to the other actuators. The hydraulic pump and vane motor were separately located, which made the size of EHA system larger. To solve this problem, we propose a design methodology of a revolute EHA using a frameless motor. Commercially available frameless motor is suitable for actuating the hydraulic pump. Moreover, the pump can be built in the hollow space of the frameless motor, and this design makes the EHA downsized. We present a prototype design based on the formulation of the output power. Through the experimental validations, we show that developed EHA has higher backdrivability than the comparable harmonic drive, and obtain the knowledges on design factors that are crucial to the EHA with high power-to-weight ratio.
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•An experimental setup was built up to collect the VILAES.•The wavelet scattering coefficients are selected as the features of the VILAES.•Relationship between scattering coefficients ...and leakage rate is determined.•Optimal wavelet scattering coefficients are extracted automatically.•A new multi-variable classification model for small leakage and small sample sizes.
In order to achieve acoustic emission detection in valve internal leakage, it is essential to extract features, and establish an accurate mathematical model. Current valve internal leakage acoustic emission signal (VILAES) classification models mostly rely on human experience for selecting features, resulting in low accuracy for small leakage conditions. This paper combines the wavelet scattering transform (WST), Relief-F algorithm and AdaBoost.M1 algorithm, and proposes to use the optimal wavelet scattering coefficients as features to establish the VILAES classification model accurately for small leakage. Firstly, the first three order wavelet scattering coefficients of the VILAES are automatically extracted using WST and are transformed into one-dimensional features. Secondly, the Relief-F algorithm is employed to select the optimal feature subset. Finally, the optimal wavelet scattering coefficients and pressures are used as inputs to establish a classification model for VILAES and achieve an accuracy over 96.80% for small leakage.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•Degradation characteristics of a micro turbine by internal leakages are analyzed.•An off-design analysis was carried out after validation using actual operating data.•Three types of leakages were ...comparatively analyzed using parameter sensitivities.•The leakage from combustor inlet to turbine exit showed the largest efficiency loss.•Performance losses were compared to those by compressor fouling and turbine erosion.
Micro gas turbines are manufactured to have a compact structure and small volume, which makes them more vulnerable to internal leakages compared to heavy-duty gas turbines. Accordingly, precise diagnosis of the performance degradation in a micro gas turbine is important. This study investigates the characteristics of degradation. Performance maps were used for the compressor and turbine, and a multi-segment counter-flow heat exchanger model was used for the recuperator. The component models were refined using actual operation data, resulting in precise simulation of the reference operation without leakage. A performance analysis was carried out, and the results were analyzed for three types of leakage with the following paths: from the compressor outlet to the recuperator’s cold-side outlet, from the compressor outlet to the combustor outlet, and from the combustor inlet to the turbine outlet. The third path produced the largest reduction in engine efficiency. The degradations were also compared with those related to compressor fouling and turbine erosion, which are the most common causes of degradation in a gas turbine. Even when qualitatively similar performance changes were observed, the root cause could be determined by analyzing differences in performance parameters such as the fuel flow.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
This work studies the effect of the electronic conductivity of a commercially used yttria-stabilised zirconia (YSZ) electrolyte on solid oxide fuel cell performance. For the first time, an ...experimental method is used to measure the electronic conductivity of YSZ in commercially manufactured and operated single cells. An inert-gas step addition (ISA) method is employed to measure the electronic conductivity in the form of voltage changes at the open-circuit state. Since the electronic conductivity allows oxide ion transfer and water generation at the anode, a reduction in open-circuit voltage (EOCV) occurs due to the increased water partial pressure. The step increase in the anode flow rate in the ISA method reduces water partial pressure and raises EOCV. The EOCV change is then converted into internal leakage currents and electronic resistivities. The YSZ's electronic resistivity obtained in this work is comparable to the results obtained using the Hebb-Wagner polarisation technique and the oxygen permeation method.
•The internal leak current of YSZ electrolyte is experimentally measured with a commercial SOFC.•The electronic resistivity of YSZ can be obtained by the internal leak current.•The ISA method provides successful results of internal leak current through the YSZ.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Valves are indispensable fluid control devices widely employed in Nuclear Power Plant (NPP) structures. Due to prolonged exposure to high-temperature environments, the internal sealing components ...become susceptible to thermal deformation or wear, leading to compromised sealing and potential leakage accidents in the valves. Therefore, detecting internal leakage in valves holds great significance. We propose an effective acoustic emission detection device designed for identifying internal leakage in valves within high-temperature environments. A valve internal leakage monitoring system based on the Acoustic Emission (AE) method is developed, utilizing high-temperature piezoelectric transducers as AE sensors. To efficiently and rapidly identify leakage states, an artificial intelligence (AI) algorithm incorporating a Convolutional Neural Network (CNN) with the Convolutional Block Attention Module (CBAM) is employed. The approach integrates both a channel attention module and a spatial attention module to capture global and local features, respectively. Normalized spectral data is used as the training set to optimize the CNN parameters. The performance of this method is then compared with BAM and SENET. The results indicate that CBAM can effectively enhance and suppress features by allocating weights among channels according to task requirements. Moreover, it can more efficiently identify multiple damage features under the constraints of limited computing resources.
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•The Pz46 material is used as the sensor to withstand high temperatures in NPPs.•The internal leakage detection device suitable for valve in NPPs is proposed.•The CBAM is used to recognize and classify the AE signals of internal leakage events.•The CBAM method outperformed other methods in classification accuracy and stability.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Large-diameter ball valves are primarily applied in oil and gas long-distance pipelines for emergency shutdown. Due to the problems of friction between the transmission medium and the valve cavity, ...as well as corrosion and ageing, the ball valves are prone to internal leakage, failing emergency shutdown. Therefore, it is essential to accurately detect whether the internal leak occurs in the valve. This work adopts acoustic emission(AE) technology to detect the leaking ball valve and proposes a novel method for leakage rate prediction, which uses a convolutional neural network(CNN) combined with a deep belief network(DBN) for feature learning and a genetic algorithm to optimize DBN, building a prediction model based on CNN-GA-DBN for internal leakage rate. Combined with the built experimental platform, the internal leakage signals of ball valves under different conditions were acquired, and the experimental verification was operated. The results show that the optimal values of mean absolute error(MAE), mean absolute percentage error(MAPE), root mean square error(RMSE), and correlation coefficient(CORR) obtained using CNN-GA-DBN are 0.8668, 14.2879, 1.0681, and 0.9963, respectively. It indicates that the proposed method can provide powerful support for the internal leakage rate prediction of ball valves.
Mobile hydraulic machines are used in various operations like construction, material handling and mining. High powers to weight ratio and manoeuvrability in rough terrains are striking features that ...help in edging out their electrical and mechanical counterparts. Internal leakage in hydraulic actuators is a faulty condition frequently observed in hydraulic machines. Internal leakage in the actuator affects the system’s dynamic performance and decreases its energy efficiency. Also, internal leakage is apparent only when the leakage is extreme and the actuator stops responding to command signals. Thus, detecting internal leakage in its early stages is a difficult task. Early detection and corrective action save energy, reduce component degradation, and reduce machine downtime. There are many existing techniques for internal leakage detection of hydraulic actuators, but they are intended for actuators working in a laboratory environment. The main focus of this paper is to present a practical method for early detection of internal leakage fault present in boom actuator of mobile hydraulic machines by analysing the machine work-cycle data with minimum hardware. The method trains and validates a Support Vector Machine (SVM) classifier using pressure and boom angle displacement signals. The time-series signals are processed using ’event-based’ feature extraction method. The binary version of Particle Swarm Optimisation is used for feature selection. The trained classifier can detect and classify internal leakage faults with more than 95% accuracy, which is sufficient for taking appropriate preventive maintenance steps on time.
•A method for early detection of internal leakage in hydraulic excavator is presented.•Feature extraction from actuator pressure and boom angle displacement signals.•Feature selection using Binary particle swarm optimisation from the feature space.•Fault detection and classification is done using Support Vector Machine classifier.•Proposed method can be used for Condition Based Maintenance of hydraulic actuator.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•An intelligent fault diagnosis approach based on DS theory is proposed.•The conflict problem of the information source in the approach is solved.•The experimental results show effectiveness of ...proposed approach.
Detecting faults in hydraulic valves are of great significance to improve the reliability and security of the whole hydraulic system. However, it is difficult to detect multiple faults in hydraulic valves using existing approaches due to closed structural components and complex hydraulic system itself. Therefore, an intelligent fault diagnosis approach based on Dempster-Shafer (DS) theory is proposed specifically for detecting several faults occurred in hydraulic valves. Actually, it is classified in the ensemble learning in terms of the information fusion theory. In this approach, signal segments containing fault information are selected to structure sample sets firstly. Then sample sets are simultaneously fed into the single classifier including long short-term memory networks (LSTM), convolutional neural network (CNN) and random forests (RF). Through learning spontaneously in these intelligent classification approaches, fault features are concluded and the probability of each type fault is respectively revealed. All probabilities are constructed as basic probability assignment (BPA) functions, which are further calculated in the information fusion process in terms of DS theory. Finally, the fault types are identified by the final fusion results. Experimental investigations are performed to validate performance of the present approach (taken a solenoid controlled pilot operated directional valve as an example). It is shown that the average accuracy ratio of proposed intelligent fault diagnosis approach is 98.5% for six fault types detection. The study does provide an effective access to detect faults in hydraulic valves.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Poor working condition makes internal pump leakage become one of the most frequent faults of hydraulic systems in industrial and agricultural production. The faults detection of the internal pump ...leakage not only suffers from the feature engineering but also the data acquisition and transmission. The latter may lead to the highly (over 50%) incomplete data problem which is deadly for fault diagnosis. Hence, the highly incomplete data problem is defined in this study. Then a two-stage fault diagnosis method based on the flow data is proposed. In the first stage, a Denoising Auto-Encoder with the conditional mask is used to complement the incomplete flow data. The conditional mask helps the model get extra information at a high missing ratio. In the second stage, with the help of masked noise and the Mask Attention mechanism, a classifier orients to the completed data is trained. These modifications help the classifier pay more attention to the remaining parts of flow data. The proposed method achieves the accuracy of 97% and 96% on the flow data which is missing by 60% and 70%. All the improvement measures are justified by the ablation and comparison experiments.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The piston seal wear in hydraulic cylinder is one of the main factors that give rise to an internal leakage. This paper focuses on diagnosing piston seal wear and subsequent internal leakage from a ...double acting seal combination seal used in the support oil cylinder of a QY110 mobile crane. Wavelet transform is applied as a feature extractor to transform the raw oil pressure data into a feature vector consisting of wavelet packet subband energy, energy entropy, energy variance, and root mean square of the wavelet detailed coefficient <inline-formula> <tex-math notation="LaTeX">d_{4} </tex-math></inline-formula>. This feature vector feeds into the wavelet neural network serving as a pattern recognizer for automatically classifying the fault patterns. We demonstrate with the leakage experiment and simulation data that the proposed fault detection and identification (FDI) scheme is capable of effectively detecting and classifying the piston seal wear with excellent accuracy. Our comparison studies reveal that the proposed FDI tandem produces much more accurate result than that from back-propagation neural network. This paper is supplement to and enrichment of existing studies on fault simulation and diagnosis associated with hydraulic cylinder leakage problems.
