[go: up one dir, main page]

Dorr et al., 1997 - Google Patents

Detection, isolation, and identification of sensor faults in nuclear power plants

Dorr et al., 1997

Document ID
15469652554629953850
Author
Dorr R
Kratz F
Ragot J
Loisy F
Germain J
Publication year
Publication venue
IEEE Transactions on Control Systems Technology

External Links

Snippet

This paper presents methods to detect, locate, and identify sensor degradations. The first method is based on simple redundancy and consists in generating residuals by comparison of measurements provided by physically redundant sensors. The second uses analytical …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • 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
    • G05B23/0224—Process history based detection method, e.g. whereby history implies the availability of large amounts of data
    • G05B23/0227—Qualitative history assessment, whereby the type of data acted upon, e.g. waveforms, images or patterns, is not relevant, e.g. rule based assessment; if-then decisions
    • G05B23/0229—Qualitative history assessment, whereby the type of data acted upon, e.g. waveforms, images or patterns, is not relevant, e.g. rule based assessment; if-then decisions knowledge based, e.g. expert systems; genetic algorithms
    • 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
    • G05B23/0254—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 based on a quantitative model, e.g. mathematical relationships between inputs and outputs; functions: observer, Kalman filter, residual calculation, Neural Networks

Similar Documents

Publication Publication Date Title
Dorr et al. Detection, isolation, and identification of sensor faults in nuclear power plants
Patton et al. Optimal unknown input distribution matrix selection in robust fault diagnosis
Fantoni et al. Multiple-failure signal validation in nuclear power plants using artificial neural networks
Li et al. Fault identification in PCA method during sensor condition monitoring in a nuclear power plant
US5265035A (en) System diagnostics using qualitative analysis and component functional classification
Fry Experience in reactor malfunction diagnosis using on-line noise analysis
Celis et al. Steady state detection in industrial gas turbines for condition monitoring and diagnostics applications
Holbert et al. Redundant sensor validation by using fuzzy logic
Defreitas et al. Anomaly detection in wind tunnel experiments by principal component analysis
Holbert et al. Nuclear power plant instrumentation fault detection using fuzzy logic
Guo et al. Identification and isolability of multiple gross errors in measured data for power plants
Sunde et al. Data reconciliation and fault detection by means of plant-wide mass and energy balances
Fantoni et al. Applications of autoassociative neural networks for signal validation in accident management
Deckert et al. Signal validation for nuclear power plants
Ma et al. Applications of fault diagnosis in nuclear power plants: an introductory survey
Hashemian Predictive maintenance in nuclear power plants through online monitoring
Zarate et al. Computation and monitoring of the deviations of gas turbine unmeasured parameters
Simani et al. Improved observer for sensor fault diagnosis of a power plant
Black et al. Inferential Neural Networks for Nuclear Power Plant Sensor Channel Drift Monitoring
Hashemian Applying online monitoring for nuclear power plant instrumentation and control
Holbert Process hypercube comparison for signal validation
Katona Possibility of use of noise analysis for identification of reactor conditions during accidents
Uhrig Potential Use Of Neural Networks In Nuclear Power Plants
Upadhyaya et al. Signature monitoring of nuclear power plant dynamics-stochastic modeling and case studies
Uhrig et al. Application of neural networks and fuzzy systems to power plants