Alauddin et al., 2024 - Google Patents
A robust neural network model for fault detection in the presence of mislabelled dataAlauddin et al., 2024
- Document ID
- 14222362286518159542
- Author
- Alauddin M
- Khan F
- Imtiaz S
- Ahmed S
- Amyotte P
- Publication year
- Publication venue
- The Canadian Journal of Chemical Engineering
External Links
Snippet
Several data‐driven methodologies for process monitoring and detection of faults or abnormalities have been developed for the safety of processing systems. The effectiveness of data‐based models, however, is impacted by the volume and quality of training data. This …
- 238000001514 detection method 0 title abstract description 38
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
- 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
- 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
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/07—Error detection; Error correction; Monitoring responding to the occurence of a fault, e.g. fault tolerance
-
- 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
- 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
- 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
- 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
-
- 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
-
- 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
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
-
- 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
- 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
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US20210334656A1 (en) | Computer-implemented method, computer program product and system for anomaly detection and/or predictive maintenance | |
| US9842302B2 (en) | Population-based learning with deep belief networks | |
| US20190370684A1 (en) | System for automatic, simultaneous feature selection and hyperparameter tuning for a machine learning model | |
| Galagedarage Don et al. | Process fault prognosis using hidden Markov model–bayesian networks hybrid model | |
| Alauddin et al. | A robust neural network model for fault detection in the presence of mislabelled data | |
| Entezami et al. | On continuous health monitoring of bridges under serious environmental variability by an innovative multi-task unsupervised learning method | |
| Kim et al. | RNN-Based online anomaly detection in nuclear reactors for highly imbalanced datasets with uncertainty | |
| Tian et al. | Anomaly detection using spatial and temporal information in multivariate time series | |
| Kumari et al. | Root cause analysis of key process variable deviation for rare events in the chemical process industry | |
| Bao et al. | Effect improved for high‐dimensional and unbalanced data anomaly detection model based on KNN‐SMOTE‐LSTM | |
| EP3183622B1 (en) | Population-based learning with deep belief networks | |
| Wang et al. | Semiparametric PCA and bayesian network based process fault diagnosis technique | |
| Lou et al. | Bayesian network based on an adaptive threshold scheme for fault detection and classification | |
| Liu et al. | An unsupervised anomaly detection approach using energy-based spatiotemporal graphical modeling | |
| CN110337640B (en) | Methods, systems, and media for problem alert aggregation and identification of suboptimal behavior | |
| Zhang et al. | Industrial text analytics for reliability with derivative-free optimization | |
| Hwang et al. | Shifting artificial data to detect system failures | |
| Chen et al. | An enhanced DPCA fault diagnosis method based on hierarchical cluster analysis | |
| Liang et al. | Self-organization comprehensive real-time state evaluation model for oil pump unit on the basis of operating condition classification and recognition | |
| Wu et al. | Physics-informed graph convolutional recurrent network for cyber-attack detection in chemical process networks | |
| Zhang et al. | A novel fault diagnosis framework for industrial production processes based on causal network inference | |
| Wang et al. | Leveraging large self-supervised time-series models for transferable diagnosis in cross-aircraft type Bleed Air System | |
| Zhou et al. | A transfer learning approach using improved copula subspace division for multi‐mode fault detection | |
| Xue et al. | An Advanced Broyden–Fletcher–Goldfarb–Shanno Algorithm for Prediction and Output‐Related Fault Monitoring in Case of Outliers | |
| Yu et al. | Center Loss Guided Prototypical Networks for Unbalance Few‐Shot Industrial Fault Diagnosis |