Lee et al., 2021 - Google Patents
Development of the machine learning-based safety significant factor inference model for diagnosis in autonomous control systemLee et al., 2021
View PDF- Document ID
- 9677451936063186856
- Author
- Lee J
- Lin L
- Athe P
- Dinh N
- Publication year
- Publication venue
- Annals of Nuclear Energy
External Links
Snippet
As a critical component to the autonomous control system, Digital Twin for Diagnosis (DT-D) is a virtual replica of physical systems for an accurate understanding of reactor states. Since the physical damage state cannot be measured directly in transient or accident conditions …
- 238000010801 machine learning 0 title abstract description 70
Classifications
-
- 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
-
- 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
- G06N3/086—Learning methods using evolutionary programming, e.g. genetic algorithms
-
- 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
- G06N3/04—Architectures, e.g. interconnection topology
-
- 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
- G06N5/04—Inference methods or devices
-
- 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
- G06F17/50—Computer-aided design
-
- 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
- G06F11/00—Error detection; Error correction; Monitoring
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Bae et al. | Real-time prediction of nuclear power plant parameter trends following operator actions | |
| Huang et al. | A review of the application of artificial intelligence to nuclear reactors: Where we are and what's next | |
| Lee et al. | Development of the machine learning-based safety significant factor inference model for diagnosis in autonomous control system | |
| Mamdikar et al. | Dynamic reliability analysis framework using fault tree and dynamic Bayesian network: A case study of NPP | |
| Mo et al. | A dynamic neural network aggregation model for transient diagnosis in nuclear power plants | |
| Evsukoff et al. | Recurrent neuro-fuzzy system for fault detection and isolation in nuclear reactors | |
| Choi et al. | RNN-based integrated system for real-time sensor fault detection and fault-informed accident diagnosis in nuclear power plant accidents | |
| Zio et al. | A data-driven approach for predicting failure scenarios in nuclear systems | |
| Kim et al. | Conceptual design of autonomous emergency operation system for nuclear power plants and its prototype | |
| Tripathi et al. | Dynamic reliability analysis framework for passive safety systems of Nuclear Power Plant | |
| Yong-kuo et al. | A cascade intelligent fault diagnostic technique for nuclear power plants | |
| Jyotish et al. | Reliability and performance evaluation of safety-critical instrumentation and control systems of nuclear power plant | |
| Tripathi et al. | A comparative study on reliability analysis methods for safety critical systems using Petri-nets and dynamic flowgraph methodology: A case study of nuclear power plant | |
| Zhao et al. | Pilot study of dynamic Bayesian networks approach for fault diagnostics and accident progression prediction in HTR-PM | |
| Jyotish et al. | Reliability and performance measurement of safety-critical systems based on petri nets: a case study of nuclear power plant | |
| Basher | Autonomous control of nuclear power plants | |
| Saeed et al. | Autonomous control model for emergency operation of small modular reactor | |
| Kaminski et al. | Time‐Series Forecasting of a Typical PWR Undergoing Large Break LOCA | |
| Elbordany et al. | An efficient AI algorithm for fault diagnosis in nuclear power plants based on machine deep learning techniques | |
| Kwag et al. | Development of network-based probabilistic safety assessment: A tool for risk analyst for nuclear facilities | |
| Yang et al. | Bidirectional implementation of Markov/CCMT for dynamic reliability analysis with application to digital I&C systems | |
| Bae et al. | Current Progress in the Application of Artificial Intelligence for Nuclear Power Plant Operation | |
| Wapachi et al. | Time-series forecasting of a typical PWR system response under control element assembly withdrawal at full power | |
| Groth et al. | " Smart Procedures": Using dynamic PRA to develop dynamic context-specific severe accident management guidelines (SAMGs). | |
| Tripathi et al. | Dynamic reliability framework for a Nuclear Power Plant using dynamic flowgraph methodology |