Wang et al., 2022 - Google Patents
Research on fault diagnosis technology based on deep learningWang et al., 2022
View PDF- Document ID
- 5617341539308698216
- Author
- Wang H
- Wei J
- Li P
- Publication year
- Publication venue
- Journal of Physics: Conference Series
External Links
Snippet
Research on Fault Diagnosis Technology Based on Deep Learning Page 1 Journal of Physics:
Conference Series PAPER • OPEN ACCESS Research on Fault Diagnosis Technology Based
on Deep Learning To cite this article: Haisheng Wang et al 2022 J. Phys.: Conf. Ser. 2187 …
- 238000003745 diagnosis 0 title abstract description 79
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
- G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
- G06K9/6247—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/3061—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F17/30705—Clustering or classification
- G06F17/3071—Clustering or classification including class or cluster creation or modification
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
- G06F17/30587—Details of specialised database models
- G06F17/30592—Multi-dimensional databases and data warehouses, e.g. MOLAP, ROLAP
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
- G06F17/30587—Details of specialised database models
- G06F17/30595—Relational databases
- G06F17/30598—Clustering or classification
- G06F17/30601—Clustering or classification including cluster or class visualization or browsing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/02—Knowledge representation
- G06N5/022—Knowledge engineering, knowledge acquisition
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6279—Classification techniques relating to the number of classes
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Zhao et al. | Challenges and opportunities of AI-enabled monitoring, diagnosis & prognosis: A review | |
Yang et al. | Autoencoder-based representation learning and its application in intelligent fault diagnosis: A review | |
Cen et al. | A review of data-driven machinery fault diagnosis using machine learning algorithms | |
Yu et al. | Challenges and opportunities of deep learning-based process fault detection and diagnosis: a review | |
Wang et al. | Research on fault diagnosis technology based on deep learning | |
Liu et al. | LSTM-GAN-AE: A promising approach for fault diagnosis in machine health monitoring | |
Li et al. | Multiscale dynamic fusion prototypical cluster network for fault diagnosis of planetary gearbox under few labeled samples | |
CN116937579B (en) | Wind power interval prediction considering space-time correlation and interpretable method thereof | |
CN117290800B (en) | Timing sequence anomaly detection method and system based on hypergraph attention network | |
Zhu et al. | Condition monitoring of wind turbine based on deep learning networks and kernel principal component analysis | |
Wang et al. | A high-stability diagnosis model based on a multiscale feature fusion convolutional neural network | |
Wu et al. | Temporal multi-resolution hypergraph attention network for remaining useful life prediction of rolling bearings | |
Li et al. | A novel unsupervised anomaly detection method for rotating machinery based on memory augmented temporal convolutional autoencoder | |
Liu et al. | Wind turbine anomaly detection based on SCADA: A deep autoencoder enhanced by fault instances | |
Ma et al. | A collaborative central domain adaptation approach with multi-order graph embedding for bearing fault diagnosis under few-shot samples | |
Li et al. | A review on convolutional neural network in rolling bearing fault diagnosis | |
Ding et al. | Artificial intelligence based abnormal detection system and method for wind power equipment | |
Wang et al. | A hybrid 3DSE-CNN-2DLSTM model for compound fault detection of wind turbines | |
Zhang et al. | A dynamic threshold method for wind turbine fault detection based on spatial-temporal neural network | |
Yu et al. | A conditional factor VAE model for pump degradation assessment under varying conditions | |
Wang et al. | GWTSP: A multi-state prediction method for short-term wind turbines based on GAT and GL | |
Ma et al. | An improved multi-channels information fusion model with multi-scale signals for fault diagnosis | |
Zhang et al. | Fault Diagnosis for Wind Turbine Generators Using Normal Behavior Model Based on Multi-Task Learning | |
Zhan et al. | A novel method of health indicator construction and remaining useful life prediction based on deep learning | |
Zhou et al. | Servo Health Monitoring Based on Feature Learning via Deep Neural Network |