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• W2893981408 abstract "Model-based diagnosis methods rely on a model that defines nominal behavior of a dynamic system to detect abnormal behaviors and isolate faults. On the other hand, data-driven diagnosis algorithms detect and isolate system faults by operating exclusively on system measurements and using very little knowledge about the system. Recently, several researchers have combined model-based diagnosis techniques with datadriven approaches to propose hybrid1solutions for fault diagnosis. Many researchers have proposed methods to integrate specific approaches. In this paper, we demonstrate that data-driven and model-based diagnosis methods follow a similar procedure and can be represented by a general unifying framework. This unifying framework for fault detection and isolation can be used to integrate different methodologies developed by two communities. As a case study, we use the proposed framework to build a crossover solution for fault diagnosis in a wind turbine benchmark. In this case study, we show that it is possible to achieve a better diagnosis performance by using a hybrid method that follows the proposed framework." @default.
• W2893981408 created "2018-10-05" @default.
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• W2893981408 date "2018-09-24" @default.
• W2893981408 modified "2023-10-18" @default.
• W2893981408 title "Framework for Unifying Model-based and Data-driven Fault Diagnosis" @default.
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• W2893981408 doi "https://doi.org/10.36001/phmconf.2018.v10i1.530" @default.
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