Lu et al., 2020 - Google Patents
Condition monitoring based on partial discharge diagnostics using machine learning methods: A comprehensive state-of-the-art reviewLu et al., 2020
View PDF- Document ID
- 16753149753158056223
- Author
- Lu S
- Chai H
- Sahoo A
- Phung B
- Publication year
- Publication venue
- IEEE Transactions on Dielectrics and Electrical Insulation
External Links
Snippet
This paper presents a state-of-the-art review on machine learning (ML) based intelligent diagnostics that have been applied for partial discharge (PD) detection, localization, and pattern recognition. ML techniques, particularly those developed in the last five years, are …
- 238000010801 machine learning 0 title abstract description 121
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
- G06K9/6267—Classification techniques
- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- 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
- G06N3/086—Learning methods using evolutionary programming, e.g. genetic algorithms
-
- 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
- G06K9/6284—Single class perspective, e.g. one-against-all classification; Novelty detection; Outlier detection
-
- 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
- G06K9/6261—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation partitioning the feature space
-
- 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
- G06N5/025—Extracting rules from data
-
- 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
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/12—Computer systems based on biological models using genetic models
- G06N3/126—Genetic algorithms, i.e. information processing using digital simulations of the genetic system
-
- 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
- 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
- G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
- G06K9/46—Extraction of features or characteristics of the image
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Lu et al. | Condition monitoring based on partial discharge diagnostics using machine learning methods: A comprehensive state-of-the-art review | |
Kong et al. | Deep learning hybrid method for islanding detection in distributed generation | |
Yang et al. | BA-PNN-based methods for power transformer fault diagnosis | |
Taha et al. | Power transformer fault diagnosis based on DGA using a convolutional neural network with noise in measurements | |
Ma et al. | High-voltage circuit breaker fault diagnosis using a hybrid feature transformation approach based on random forest and stacked autoencoder | |
Basharan et al. | Recognition of multiple partial discharge patterns by multi‐class support vector machine using fractal image processing technique | |
Raymond et al. | Partial discharge classifications: Review of recent progress | |
Zheng et al. | A real-time transformer discharge pattern recognition method based on CNN-LSTM driven by few-shot learning | |
Yang et al. | Combined wireless network intrusion detection model based on deep learning | |
Jee Keen Raymond et al. | Classification of partial discharge measured under different levels of noise contamination | |
Shukla et al. | Power quality disturbances classification based on Gramian angular summation field method and convolutional neural networks | |
Khan et al. | End-to-end partial discharge detection in power cables via time-domain convolutional neural networks | |
Kumar et al. | The Importance of Feature Processing in Deep‐Learning‐Based Condition Monitoring of Motors | |
Wang et al. | Cable incipient fault identification using restricted Boltzmann machine and stacked autoencoder | |
Zhou et al. | Design of ensemble fuzzy-RBF neural networks based on feature extraction and multi-feature fusion for GIS partial discharge recognition and classification | |
Todeschini et al. | An image-based deep transfer learning approach to classify power quality disturbances | |
Thi et al. | Anomaly detection for partial discharge in gas-insulated switchgears using autoencoder | |
Wang et al. | Multi-source partial discharge diagnosis in gas-insulated switchgear via zero-shot learning | |
Long et al. | A comprehensive review of signal processing and machine learning technologies for UHF PD detection and diagnosis (II): Pattern recognition approaches | |
Iwata et al. | Phase-resolved partial discharge analysis of different types of electrode systems using machine learning classification | |
Bilski et al. | Analysis of the artificial intelligence methods applicability to the non-intrusive load monitoring | |
Gholaminejad et al. | A comparative case study between shallow and deep neural networks in induction motor's fault diagnosis | |
Xiao et al. | An Integrated Approach Fusing CEEMD Energy Entropy and Sparrow Search Algorithm‐Based PNN for Fault Diagnosis of Rolling Bearings | |
Zhang et al. | An adaptive fault diagnosis method of power transformers based on combining oversampling and cost‐sensitive learning | |
Zhang et al. | Fault line selection method based on transfer learning depthwise separable convolutional neural network |