[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Zhu et al., 2023 - Google Patents

Hybrid scheme through read-first-LSTM encoder-decoder and broad learning system for bearings degradation monitoring and remaining useful life estimation

Zhu et al., 2023

View HTML
Document ID
2076524992811755414
Author
Zhu Y
Wu J
Liu X
Wu J
Chai K
Hao G
Liu S
Publication year
Publication venue
Advanced Engineering Informatics

External Links

Snippet

This paper proposes a novel hybrid scheme through read-first-LSTM (RLSTM) encoder- decoder and broad learning system (BLS) for bearings degradation monitoring and remaining useful life (RUL) estimation, which aims to describe the nonlinear characteristics …
Continue reading at www.sciencedirect.com (HTML) (other versions)

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
    • 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
    • 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
    • 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
    • G06K9/6217—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
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G06F19/34—Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
    • G06F19/345—Medical expert systems, neural networks or other automated diagnosis
    • 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
    • G06K9/36—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
    • 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
    • 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
Chen et al. Anomaly detection and critical SCADA parameters identification for wind turbines based on LSTM-AE neural network
Huang et al. A novel deep convolutional neural network-bootstrap integrated method for RUL prediction of rolling bearing
Buchaiah et al. Bearing fault diagnosis and prognosis using data fusion based feature extraction and feature selection
Zhang et al. An integrated multi-head dual sparse self-attention network for remaining useful life prediction
Wang et al. Deep heterogeneous GRU model for predictive analytics in smart manufacturing: Application to tool wear prediction
Cheng et al. Machine health monitoring using adaptive kernel spectral clustering and deep long short-term memory recurrent neural networks
Wang et al. Variational transformer-based anomaly detection approach for multivariate time series
Lin et al. Deep diagnostics and prognostics: An integrated hierarchical learning framework in PHM applications
Zhu et al. Hybrid scheme through read-first-LSTM encoder-decoder and broad learning system for bearings degradation monitoring and remaining useful life estimation
Yu et al. Improved Butterfly Optimizer‐Configured Extreme Learning Machine for Fault Diagnosis
Ji et al. A novel deep learning approach for anomaly detection of time series data
Xu et al. An intelligent fault diagnosis for machine maintenance using weighted soft-voting rule based multi-attention module with multi-scale information fusion
Tang et al. A wind turbine bearing fault diagnosis method based on fused depth features in time–frequency​ domain
Shi et al. A dual attention LSTM lightweight model based on exponential smoothing for remaining useful life prediction
Wu et al. Layer-wise relevance propagation for interpreting LSTM-RNN decisions in predictive maintenance
Zhang et al. An attention-based temporal convolutional network method for predicting remaining useful life of aero-engine
Yu et al. Multiscale intelligent fault detection system based on agglomerative hierarchical clustering using stacked denoising autoencoder with temporal information
Li et al. A novel unsupervised anomaly detection method for rotating machinery based on memory augmented temporal convolutional autoencoder
Chang et al. Temporal convolution-based sorting feature repeat-explore network combining with multi-band information for remaining useful life estimation of equipment
Wang et al. Memory-enhanced hybrid deep learning networks for remaining useful life prognostics of mechanical equipment
Pan et al. Symplectic interactive support matrix machine and its application in roller bearing condition monitoring
Zhang et al. A framework for predicting the remaining useful life of machinery working under time-varying operational conditions
Panjapornpon et al. Deep learning approach for energy efficiency prediction with signal monitoring reliability for a vinyl chloride monomer process
Pan et al. Pinball transfer support matrix machine for roller bearing fault diagnosis under limited annotation data
Barraza et al. FS-SCF network: Neural network interpretability based on counterfactual generation and feature selection for fault diagnosis