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Full-Text Articles in Engineering
Fault Diagnosis Of Hydraulic Power System For Coal Mine Tunnel Drilling Rig Based On T-S Fuzzy Fault Tree, Liu Ruojun, Zhang Youzhen, Yao Ke
Fault Diagnosis Of Hydraulic Power System For Coal Mine Tunnel Drilling Rig Based On T-S Fuzzy Fault Tree, Liu Ruojun, Zhang Youzhen, Yao Ke
Coal Geology & Exploration
In view of the problems of fuzzy logic relation between the elements of the system, the polymorphism of fault mode, and the difficulty in obtaining the fault probability as a result of the complication of the hydraulic power system for coal mine tunnel drilling rig, a fault diagnosis method of hydraulic power system for coal mine tunnel drilling rig based on Takagi-Sugeno (T-S) fuzzy tree was proposed to overcome the limitations of traditional fault tree in fault diagnosis and analysis of the complex electromechanical hydraulic equipment. The method could timely and accurately obtain the fault information of equipment, and to …
A Wind Turbine Fault Diagnosis Method Based On Siamese Deep Neural Network, Jiarui Liu, Guotian Yang, Xiaowei Wang
A Wind Turbine Fault Diagnosis Method Based On Siamese Deep Neural Network, Jiarui Liu, Guotian Yang, Xiaowei Wang
Journal of System Simulation
Abstract: In order to effectively extract the fault features of time series data in supervisory control and data acquisition (SCADA), considering the advantages of one-dimensional convolutional neural network (1-D CNN) for extracting local time series features and the advantages of long-term memory (LSTM) which can extract long-term dependent features, a method for fault diagnosis of wind turbines based on 1-D CNN-LSTM is proposed. To solve the problem of the scarcity of fault samples of wind turbines based on the siamese network architecture, a wind fault diagnosis method based on siamese 1-D CNN-LSTM is proposed. The proposed siamese 1-D CNN-LSTM …
Engine Wear Fault Diagnosis Based On Supervised Kernel Entropy Component Analysis, Zhichao Zhu, Dinghui Wu, Yuanchang Yue
Engine Wear Fault Diagnosis Based On Supervised Kernel Entropy Component Analysis, Zhichao Zhu, Dinghui Wu, Yuanchang Yue
Journal of System Simulation
Abstract: Focus on the influence of environment on engine operation, which leads to a large amount of redundant information and nonlinear structure in oil spectral data that affects the engine fault diagnosis results, the feature extraction method of SKECA (supervised kernel entropy component analysis) is proposed. A supervised learning algorithm is adopted on the basis of Kernel Entropy Component Analysis, which extracts the inherent geometric features of oil spectrum data to make the extracted fault features include the discriminative information. GA (genetic algorithm) is used to find parameters to optimize the results of feature extraction, and SVM (support vector machine) …