Deng et al., 2024 - Google Patents
An intelligent hybrid deep learning model for rolling bearing remaining useful life predictionDeng et al., 2024
- Document ID
- 10121827044779583015
- Author
- Deng L
- Li W
- Yan X
- Publication year
- Publication venue
- Nondestructive Testing and Evaluation
External Links
Snippet
ABSTRACT Remaining Useful Life (RUL) prediction of rolling bearings is one of the intricate and important issues for equipment intelligent maintenance and health management. Various machine learning models and methods have been applied to rolling bearing RUL …
- 238000005096 rolling process 0 title abstract description 58
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
- 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
- G06F17/30861—Retrieval from the Internet, e.g. browsers
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
- G06Q10/063—Operations research or analysis
-
- 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
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
-
- 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
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
-
- 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
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce, e.g. shopping or e-commerce
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Wu et al. | Layer-wise relevance propagation for interpreting LSTM-RNN decisions in predictive maintenance | |
Zhang et al. | Energy theft detection in an edge data center using threshold-based abnormality detector | |
He et al. | MTAD‐TF: Multivariate Time Series Anomaly Detection Using the Combination of Temporal Pattern and Feature Pattern | |
Deng et al. | An intelligent hybrid deep learning model for rolling bearing remaining useful life prediction | |
Gendeel et al. | Deterministic and probabilistic interval prediction for wind farm based on VMD and weighted LS-SVM | |
Kumar et al. | The Importance of Feature Processing in Deep‐Learning‐Based Condition Monitoring of Motors | |
CN115994630B (en) | Multi-scale self-attention-based equipment residual service life prediction method and system | |
Zhao et al. | Early fault diagnosis based on reinforcement learning optimized-SVM model with vibration-monitored signals | |
Liang | Optimization of quantitative financial data analysis system based on deep learning | |
Yu et al. | DTAAD: Dual TCN-attention networks for anomaly detection in multivariate time series data | |
Zhang et al. | A novel anomaly detection method for multimodal WSN data flow via a dynamic graph neural network | |
Liu et al. | A CNN-LSTM-based domain adaptation model for remaining useful life prediction | |
Deng et al. | Intelligent prediction of rolling bearing remaining useful life based on probabilistic DeepAR-Transformer model | |
Lin | Intelligent fault diagnosis of consumer electronics sensor in IoE via transformer | |
Shan et al. | Multiscale self-attention architecture in temporal neural network for nonintrusive load monitoring | |
Pang et al. | Discrete Cosine Transformation and Temporal Adjacent Convolutional Neural Network‐Based Remaining Useful Life Estimation of Bearings | |
Wu et al. | Intelligent fault diagnosis of rolling bearings based on clustering algorithm of fast search and find of density peaks | |
Zhao et al. | A multiple conditions dual inputs attention network remaining useful life prediction method | |
Wang et al. | Distilling the knowledge of multiscale densely connected deep networks in mechanical intelligent diagnosis | |
Gawali et al. | Fault prediction model in wind turbines using deep learning structure with enhanced optimisation algorithm | |
Shen et al. | Long-term multivariate time series forecasting in data centers based on multi-factor separation evolutionary spatial–temporal graph neural networks | |
Lin et al. | Edge-based RNN anomaly detection platform in machine tools | |
Xie et al. | Multi-scale deep neural network for fault diagnosis method of rotating machinery | |
Wang et al. | Time series anomaly detection with reconstruction-based state-space models | |
Lu et al. | Relation-aware attentive neural processes model for remaining useful life prediction |