Cai et al., 2021 - Google Patents
Data-driven early fault diagnostic methodology of permanent magnet synchronous motorCai et al., 2021
View PDF- Document ID
- 16583756891778164900
- Author
- Cai B
- Hao K
- Wang Z
- Yang C
- Kong X
- Liu Z
- Ji R
- Liu Y
- Publication year
- Publication venue
- Expert Systems with Applications
External Links
Snippet
Permanent magnet synchronous motor (PMSM) is one of the common core power components in modern industrial systems. Early fault diagnosis can avoid major accidents and plan maintenance in advance. However, the features of early faults are weak, and the …
- 238000000034 method 0 title abstract description 35
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
- 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
- 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
- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
-
- 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
- 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
- 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
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
- G05B23/0243—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model
-
- 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
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Cai et al. | Data-driven early fault diagnostic methodology of permanent magnet synchronous motor | |
Li et al. | Data alignments in machinery remaining useful life prediction using deep adversarial neural networks | |
Lang et al. | Artificial intelligence-based technique for fault detection and diagnosis of EV motors: A review | |
CN112655004B (en) | Computer-implemented method for anomaly detection and/or predictive maintenance | |
Yan et al. | Deep order-wavelet convolutional variational autoencoder for fault identification of rolling bearing under fluctuating speed conditions | |
Ma et al. | Predicting the remaining useful life of an aircraft engine using a stacked sparse autoencoder with multilayer self‐learning | |
Zhou et al. | Remaining Useful Life Prediction and Fault Diagnosis of Rolling Bearings Based on Short‐Time Fourier Transform and Convolutional Neural Network | |
Zhang et al. | Deep learning algorithms for bearing fault diagnostics-a review | |
Han et al. | Cross‐machine intelligent fault diagnosis of gearbox based on deep learning and parameter transfer | |
Liang et al. | A deep learning method for motor fault diagnosis based on a capsule network with gate-structure dilated convolutions | |
Wu et al. | Layer-wise relevance propagation for interpreting LSTM-RNN decisions in predictive maintenance | |
You et al. | A Fault Diagnosis Model for Rotating Machinery Using VWC and MSFLA‐SVM Based on Vibration Signal Analysis | |
Zhao et al. | A robust construction of normalized CNN for online intelligent condition monitoring of rolling bearings considering variable working conditions and sources | |
Zhang et al. | A novel assessable data augmentation method for mechanical fault diagnosis under noisy labels | |
Dong et al. | A fault diagnosis method for rotating machinery based on PCA and Morlet kernel SVM | |
Shi et al. | Incipient Fault Detection of Rolling Element Bearings Based on Deep EMD‐PCA Algorithm | |
Yan et al. | Fault diagnosis of rolling‐element bearing using multiscale pattern gradient spectrum entropy coupled with Laplacian score | |
Zemouri et al. | Hydrogenerator early fault detection: Sparse dictionary learning jointly with the variational autoencoder | |
Kumar et al. | The Importance of Feature Processing in Deep‐Learning‐Based Condition Monitoring of Motors | |
Kumar et al. | Fault diagnosis of bearings through vibration signal using Bayes classifiers | |
Zhang et al. | Graph neural network-based bearing fault diagnosis using Granger causality test | |
Qin et al. | The fault diagnosis of rolling bearing based on improved deep forest | |
Xu et al. | A multi-sensor fused incremental broad learning with DS theory for online fault diagnosis of rotating machinery | |
Wang et al. | A hybrid 3DSE-CNN-2DLSTM model for compound fault detection of wind turbines | |
You et al. | Intelligent Fault Diagnosis of Bearing Based on Convolutional Neural Network and Bidirectional Long Short‐Term Memory |