Nothing Special   »   [go: up one dir, main page]

Cai et al., 2021 - Google Patents

Data-driven early fault diagnostic methodology of permanent magnet synchronous motor

Cai 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 …
Continue reading at www.sciencedirect.com (PDF) (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • G06N99/005Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models
    • G06N5/02Knowledge representation
    • G06N5/022Knowledge engineering, knowledge acquisition
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • G06N3/08Learning methods
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6267Classification techniques
    • G06K9/6268Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/3061Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F17/30705Clustering or classification
    • G06F17/3071Clustering or classification including class or cluster creation or modification
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models
    • G06N5/04Inference methods or devices
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6267Classification techniques
    • G06K9/6279Classification techniques relating to the number of classes
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6217Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00Testing or monitoring of control systems or parts thereof
    • G05B23/02Electric testing or monitoring
    • G05B23/0205Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0218Electric 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
    • G05B23/0243Electric 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
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00Computer systems based on specific mathematical models
    • G06N7/005Probabilistic networks
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F15/00Digital computers in general; Data processing equipment in general
    • G06F15/18Digital 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
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F11/00Error 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