Dsmt Based Three Layer Method Using Multi Classifier To Detect Faults In Hydraulic Systems
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Dsmt Based Three Layer Method Using Multi Classifier To Detect Faults In Hydraulic Systems
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Author : Xiancheng Ji
language : en
Publisher: Infinite Study
Release Date :
Dsmt Based Three Layer Method Using Multi Classifier To Detect Faults In Hydraulic Systems written by Xiancheng Ji and has been published by Infinite Study this book supported file pdf, txt, epub, kindle and other format this book has been release on with Education categories.
Fault identification in hydraulic valves is essential in maintaining the reliability and security of hydraulic systems. Due to the nonlinear characteristics of hydraulic systems under noisy working conditions, it is difficult to extract fault features from vibration signals collected from the surface of the valve body. Therefore, a DSmT-based three-layer method using multi-classifier is proposed to detect multiple faults occurred in hydraulic valves
Classification Of Wear State For A Positive Displacement Pump Using Deep Machine Learning
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Author : Jarosław Konieczny
language : en
Publisher: Infinite Study
Release Date : 2023-01-01
Classification Of Wear State For A Positive Displacement Pump Using Deep Machine Learning written by Jarosław Konieczny and has been published by Infinite Study this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-01 with Computers categories.
Hydraulic power systems are commonly used in heavy industry (usually highly energy intensive and are often associated with high power losses. Designing a suitable system to allow an early assessment of the wear conditions of components in a hydraulic system (e.g., an axial piston pump) can effectively contribute to reducing energy losses during use. This paper presents the application of a deep machine learning system to determine the efficiency state of a multi-piston positive displacement pump. Such pumps are significant in high-power hydraulic systems. The correct operation of the entire hydraulic system often depends on its proper functioning. The wear and tear of individual pump components usually leads to a decrease in the pump’s operating pressure and volumetric losses, subsequently resulting in a decrease in overall pump efficiency and increases in vibration and pump noise. This in turn leads to an increase in energy losses throughout the hydraulic system, which releases excess heat. Typical failures of the discussed pumps and their causes are described after reviewing current research work using deep machine learning. Next, the test bench on which the diagnostic experiment was conducted and the selected operating signals that were recorded are described. The measured signals were subjected to a time–frequency analysis, and their features, calculated in terms of the time and frequency domains, underwent a significance ranking using the minimum redundancy maximum relevance (MRMR) algorithm. The next step was to design a neural network structure to classify the wear state of the pump and to test and evaluate the effectiveness of the network’s recognition of the pump’s condition. The whole study was summarized with conclusions.
Multi Sensor Based Fault Detection And Classification Method For Radial Power Distribution Systems
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Author : Nan Wang
language : en
Publisher:
Release Date : 2014
Multi Sensor Based Fault Detection And Classification Method For Radial Power Distribution Systems written by Nan Wang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with Electronic dissertations categories.
A novel multi-sensor-based method to detect and classify short circuit faults in radial power distribution systems, including the effects of regulators and distribution transformers, is proposed in this work. This new scheme first calculates the correlation among the data of different sensors and uses principal component analysis (PCA) to reduce the data dimensions. Then, kernel support vector machine (SVM) classifiers are applied to detect faults and identify faulty phases. The proposed method is simulated and tested for normal operations like load switching and different types of faults under different signal-to-noise ratio (SNR) scenarios with various fault durations and fault impedances. The Gaussian mixture noise model is used in the simulations to test the robustness of the algorithm. Two distribution system models (an unbalanced feeder and a balanced feeder) are used in this work to determine the impacts of system configuration on the proposed method. Finally, relationships among the number of sensors, sampling rate of the sensors, and detection performance are discussed.