Robust fault diagnosis using a data-based approach and structural analysis
This paper presents a fault diagnosis approach that combines structural and data-driven techniques. The proposed method involves two phases. As a first step, the residuals structure is obtained from the structural model of the system by using structural analysis without considering mathematical mode...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2022 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/384248 |
| Acceso en línea: | https://hdl.handle.net/2117/384248 https://dx.doi.org/10.1016/j.ifacol.2022.07.131 |
| Access Level: | acceso abierto |
| Palabra clave: | Predictive control Fault diagnosis Structural analysis ANFIS Robust identification Bayesian reasoning Control predictiu Àrees temàtiques de la UPC::Informàtica::Automàtica i control |
| Sumario: | This paper presents a fault diagnosis approach that combines structural and data-driven techniques. The proposed method involves two phases. As a first step, the residuals structure is obtained from the structural model of the system by using structural analysis without considering mathematical models (only the component description of the system). Secondly, the analytical expressions for residuals are derived from available historical data using a robust identification approach. Through adaptive nets, residuals are adjusted by determining an interval model that takes into account the uncertainties and noises affecting the system. In the diagnosis part, residuals are tracked and evaluated. The presence of inconsistent residuals can be regarded as a fault, therefore thresholds for each residual are introduced. In addition to detecting faulty scenarios, it is also possible to determine which is the most likely fault that occurred in the system. To accomplish such classification, the proposed approach implements a Bayesian reasoning that uses the FSM (Fault Signature Matrix) that is obtained from the structural analysis of the system and residual activation signals. A brushless DC motor (BLDC) is used as a case study to illustrate the proposed approach. Simulation experiments illustrate the overall performance. |
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