Machine Learning-Based Fault Diagnosis for Industrial Engineering Systems

Machine Learning-Based Fault Diagnosis for Industrial Engineering Systems

Yang, Rui; Zhong, Maiying

Taylor & Francis Ltd






15 a 20 dias


Descrição não disponível.
1. Background and Related Methods. 2. Fault Diagnosis Method Based on Recurrent Convolutional Neural Network. 3. Fault Diagnosis of Rotating Machinery Gear Based on Random Forest Algorithm. 4. Bearing Fault Diagnosis under Different Working Conditions Based on Generative Adversarial Networks. 5. Rotating Machinery Gearbox Fault Diagnosis Based on One-Dimensional Convolutional Neural Network and Random Forest. 6. Fault Diagnosis for Rotating Machinery Gearbox Based on Improved Random Forest Algorithm. 7. Imbalanced Data Fault Diagnosis Based on Hybrid Feature Dimensionality Reduction and Varied Density Based Safe-Level Synthetic Minority Oversampling Technique.
CNC Machine;Computer Numerical Control;Precision Motion;Fault Detection;Industry 4.0;Deep Learning Methods;Fault Diagnosis;Random Forest;Data Set;Random Forest Algorithm;Rotating Machinery;Convolutional Layer;RNN;Fault Diagnosis Method;Pooling Layer;Random Subspace Method;DBSCAN Algorithm;Unlabeled Samples;Imbalanced Data;Fault Diagnosis Model;Semi-supervised Learning;Feature Extraction Results;Back Propagation;Fault Diagnosis Procedures;Wasserstein Distance;Real Data Distribution;Support Vector Machine Method;Random Forest Classifier;Back Propagation Neural Network;Feature Dimension Reduction;Random Forest Model