Reddy et al., 1998 - Google Patents
An input-training neural network approach for gross error detection and sensor replacementReddy et al., 1998
- Document ID
- 9383948287666773140
- Author
- Reddy V
- Mavrovouniotis M
- Publication year
- Publication venue
- Chemical Engineering Research and Design
External Links
Snippet
Input-Training Neural Networks (IT-nets) are a nonlinear method for data dimensionality reduction. Starting from a large set of original variables (sensor measurements), an IT-net is trained to determine a smaller set of latent variables and a (neural-network based) model for …
- 230000001537 neural 0 title abstract description 32
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
- G05B23/0224—Process history based detection method, e.g. whereby history implies the availability of large amounts of data
- G05B23/024—Quantitative history assessment, e.g. mathematical relationships between available data; Functions therefor; Principal component analysis [PCA]; Partial least square [PLS]; Statistical classifiers, e.g. Bayesian networks, linear regression or correlation analysis; Neural networks
-
- 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
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
-
- 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
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM]
-
- 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
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B17/00—Systems involving the use of models or simulators of said systems
- G05B17/02—Systems involving the use of models or simulators of said systems electric
-
- 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
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
Similar Documents
Publication | Publication Date | Title |
---|---|---|
US11853899B2 (en) | Methods and apparatus for data analysis | |
US7356430B2 (en) | Methods and apparatus for data analysis | |
Kramer | Autoassociative neural networks | |
US7225107B2 (en) | Methods and apparatus for data analysis | |
US5548528A (en) | Virtual continuous emission monitoring system | |
US8000928B2 (en) | Methods and apparatus for data analysis | |
US8041541B2 (en) | Methods and apparatus for data analysis | |
US20100088054A1 (en) | Methods and apparatus for data analysis | |
US20110178967A1 (en) | Methods and apparatus for data analysis | |
IL177293A (en) | Methods and apparatus for data analysis | |
Lu et al. | Application of autoassociative neural network on gas-path sensor data validation | |
JP2001526418A (en) | Processing unit monitoring method | |
Reddy et al. | An input-training neural network approach for gross error detection and sensor replacement | |
Liu et al. | Knowledge transfer in board-level functional fault diagnosis enabled by domain adaptation | |
Simani et al. | Application of a neural network in gas turbine control sensor fault detection | |
Hines et al. | Process and equipment monitoring methodologies applied to sensor calibration monitoring | |
Osigwe et al. | Integrated gas turbine system diagnostics: components and sensor faults quantification using artificial neural networks | |
Acilan et al. | Novel Parameter Error Identification Method for Power Plant Dynamic Models | |
Peck et al. | Genetic algorithm based input selection for a neural network function approximator with applications to SSME health monitoring | |
Reddy et al. | Analysis of plant measurements through input-training neural networks | |
KR20070018880A (en) | Method and apparatus for data analysis | |
Simani et al. | Neural networks for fault diagnosis and identification of industrial processes. | |
Gertler et al. | Diagnosis of plant failures using orthogonal parity equations | |
Rajan et al. | Machine learning algorithms for fault diagnosis in analog circuits | |
Marseguerra et al. | A soft-computing based classification procedure for the identification of transients in the steam generator of a pressurized water reactor |