Sinossi
There has been considerable interest in fault detection and identification recently due to the increasing complexity of automation processes. A more suitable strategy of using knowledge-based techniques instead of traditional linearization techniques is used to produce a model of a non-linear system. A method to generate the training data is presented. A fuzzy relational sliding mode observer (FRSMO) and proportional integral observer (FRPIO) are proposed to estimate the magnitude of incipient faults in information-poor and non-linear systems. In the fuzzy PIO, fault size can be obtained from the error passing the PI feedback compensation. In the fuzzy SMO, the equivalent injection is used to compensate for the fault thus obtaining the fault magnitude. To reduce modelling errors, an on-line learning fault identification scheme is used to update the model and identify the fault in a periodical mode with different time intervals during the whole procedure. The performance of the proposed methods is evaluated using a cooling-coil subsystem of an air-conditioning plant to identify the typical actuator fault and flow reduction fault in a simulation environment.
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L'autore
Yimin Zhou, DPhil, graduated from University of Oxford, UK at 2008. Currently, she is an Associate Professor working at Shenzhen Institutes of Advanced Technology, Chinese Academy Sciences, China. Her research interest includes: Fault diagnosis, Fuzzy modelling, Sensor optimization.
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Fault Identification in Non-linear Dynamic Systems
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Scholars' Press Jun 2014, 2014
ISBN 10: 3639718941
ISBN 13: 9783639718942
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -There has been considerable interest in fault detection and identification recently due to the increasing complexity of automation processes. A more suitable strategy of using knowledge-based techniques instead of traditional linearization techniques is used to produce a model of a non-linear system. A method to generate the training data is presented. A fuzzy relational sliding mode observer (FRSMO) and proportional integral observer (FRPIO) are proposed to estimate the magnitude of incipient faults in information-poor and non-linear systems. In the fuzzy PIO, fault size can be obtained from the error passing the PI feedback compensation. In the fuzzy SMO, the equivalent injection is used to compensate for the fault thus obtaining the fault magnitude. To reduce modelling errors, an on-line learning fault identification scheme is used to update the model and identify the fault in a periodical mode with different time intervals during the whole procedure. The performance of the proposed methods is evaluated using a cooling-coil subsystem of an air-conditioning plant to identify the typical actuator fault and flow reduction fault in a simulation environment. 208 pp. Englisch. Codice articolo 9783639718942
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Fault Identification in Non-linear Dynamic Systems
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Zhou YiminYimin Zhou, DPhil, graduated from University of Oxford, UK at 2008. Currently, she is an Associate Professor working at Shenzhen Institutes of Advanced Technology, Chinese Academy Sciences, China. Her research interest incl. Codice articolo 4999839
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Fault Identification in Non-linear Dynamic Systems
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ISBN 13: 9783639718942
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Taschenbuch. Condizione: Neu. Fault Identification in Non-linear Dynamic Systems | Yimin Zhou | Taschenbuch | 208 S. | Englisch | 2014 | Scholars' Press | EAN 9783639718942 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand. Codice articolo 105211824
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Fault Identification in Non-linear Dynamic Systems
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Scholars' Press Jun 2014, 2014
ISBN 10: 3639718941
ISBN 13: 9783639718942
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -There has been considerable interest in fault detection and identification recently due to the increasing complexity of automation processes. A more suitable strategy of using knowledge-based techniques instead of traditional linearization techniques is used to produce a model of a non-linear system. A method to generate the training data is presented. A fuzzy relational sliding mode observer (FRSMO) and proportional integral observer (FRPIO) are proposed to estimate the magnitude of incipient faults in information-poor and non-linear systems. In the fuzzy PIO, fault size can be obtained from the error passing the PI feedback compensation. In the fuzzy SMO, the equivalent injection is used to compensate for the fault thus obtaining the fault magnitude. To reduce modelling errors, an on-line learning fault identification scheme is used to update the model and identify the fault in a periodical mode with different time intervals during the whole procedure. The performance of the proposed methods is evaluated using a cooling-coil subsystem of an air-conditioning plant to identify the typical actuator fault and flow reduction fault in a simulation environment.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 208 pp. Englisch. Codice articolo 9783639718942
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Fault Identification in Non-linear Dynamic Systems
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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - There has been considerable interest in fault detection and identification recently due to the increasing complexity of automation processes. A more suitable strategy of using knowledge-based techniques instead of traditional linearization techniques is used to produce a model of a non-linear system. A method to generate the training data is presented. A fuzzy relational sliding mode observer (FRSMO) and proportional integral observer (FRPIO) are proposed to estimate the magnitude of incipient faults in information-poor and non-linear systems. In the fuzzy PIO, fault size can be obtained from the error passing the PI feedback compensation. In the fuzzy SMO, the equivalent injection is used to compensate for the fault thus obtaining the fault magnitude. To reduce modelling errors, an on-line learning fault identification scheme is used to update the model and identify the fault in a periodical mode with different time intervals during the whole procedure. The performance of the proposed methods is evaluated using a cooling-coil subsystem of an air-conditioning plant to identify the typical actuator fault and flow reduction fault in a simulation environment. Codice articolo 9783639718942
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Fault Identification in Non-linear Dynamic Systems
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VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2014
ISBN 10: 3639718941
ISBN 13: 9783639718942
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Fault Identification in Non-linear Dynamic Systems
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VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2014
ISBN 10: 3639718941
ISBN 13: 9783639718942
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Condizione: New. PRINT ON DEMAND pp. 208. Codice articolo 18128410277
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