Tian et al., 2024 - Google Patents
A novel data augmentation approach to fault diagnosis with class-imbalance problemTian et al., 2024
- Document ID
- 8035746773345809069
- Author
- Tian J
- Jiang Y
- Zhang J
- Luo H
- Yin S
- Publication year
- Publication venue
- Reliability Engineering & System Safety
External Links
Snippet
Data-driven fault diagnosis techniques are frequently applied to ensure the reliability and safety of industrial systems. However, as a common challenge, the class-imbalance problem reduces the performance of data-driven methods due to the lack of data information. We …
- 238000003745 diagnosis 0 title abstract description 44
Classifications
-
- 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
- G06N3/04—Architectures, e.g. interconnection topology
- G06N3/0472—Architectures, e.g. interconnection topology using probabilistic elements, e.g. p-rams, stochastic processors
-
- 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
- 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
-
- 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
- G06K9/6269—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches based on the distance between the decision surface and training patterns lying on the boundary of the class cluster, e.g. support vector machines
-
- 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
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/02—Knowledge representation
- G06N5/022—Knowledge engineering, knowledge acquisition
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computer systems based on specific mathematical models
- G06N7/005—Probabilistic networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/04—Inference methods or devices
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F15/00—Digital computers in general; Data processing equipment in general
- G06F15/18—Digital computers in general; Data processing equipment in general in which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Tian et al. | A novel data augmentation approach to fault diagnosis with class-imbalance problem | |
Zhao et al. | Normalized conditional variational auto-encoder with adaptive focal loss for imbalanced fault diagnosis of bearing-rotor system | |
Chen et al. | Anomaly detection and critical SCADA parameters identification for wind turbines based on LSTM-AE neural network | |
Mi et al. | Wind speed prediction based on singular spectrum analysis and neural network structural learning | |
Li et al. | Deep learning based on Transformer architecture for power system short-term voltage stability assessment with class imbalance | |
González-Muñiz et al. | Health indicator for machine condition monitoring built in the latent space of a deep autoencoder | |
Liu et al. | Intelligent fault diagnosis under small sample size conditions via Bidirectional InfoMax GAN with unsupervised representation learning | |
Liu et al. | A two-stage deep autoencoder-based missing data imputation method for wind farm SCADA data | |
de Paula Monteiro et al. | A hybrid prototype selection-based deep learning approach for anomaly detection in industrial machines | |
Sun et al. | Matching contrastive learning: an effective and intelligent method for wind turbine fault diagnosis with imbalanced SCADA data | |
Peng et al. | Towards robust and understandable fault detection and diagnosis using denoising sparse autoencoder and smooth integrated gradients | |
Zhang et al. | A novel assessable data augmentation method for mechanical fault diagnosis under noisy labels | |
Zhang et al. | A hybrid forecasting system with complexity identification and improved optimization for short-term wind speed prediction | |
Chatterjee et al. | Highly imbalanced fault classification of wind turbines using data resampling and hybrid ensemble method approach | |
Cheng et al. | A novel planetary gearbox fault diagnosis method for nuclear circulating water pump with class imbalance and data distribution shift | |
Cao et al. | A two-stage domain alignment method for multi-source domain fault diagnosis | |
Li et al. | Source-free domain adaptation framework for fault diagnosis of rotation machinery under data privacy | |
Vo et al. | Harnessing attention mechanisms in a comprehensive deep learning approach for induction motor fault diagnosis using raw electrical signals | |
Ding et al. | Machinery cross domain degradation prognostics considering compound domain shifts | |
Wang et al. | Feature fusion based ensemble method for remaining useful life prediction of machinery | |
Li et al. | A lightgbm-based multi-scale weighted ensemble model for few-shot fault diagnosis | |
Ma et al. | A multirate sensor information fusion strategy for multitask fault diagnosis based on convolutional neural network | |
Ayodeji et al. | An empirical evaluation of attention-based multi-head models for improved turbofan engine remaining useful life prediction | |
Li et al. | Nonlinear industrial process fault diagnosis with latent label consistency and sparse Gaussian feature learning | |
Yin et al. | Bi-level binary coded fully connected classifier based on residual network 50 with bottom and deep level features for bearing fault diagnosis |