Optimum Sensor Localization/Selection In A Diagnostic/Prognostic Architecture

Sensor localization;Sensor selection;Diagnostics;Prognostics

Zhang, Guangfan ; Electrical and Computer Engineering

University:Georgia Institute of Technology

Department:Electrical and Computer Engineering

关键词: Sensor localization;    Sensor selection;    Diagnostics;    Prognostics;   

Others  :  https://smartech.gatech.edu/bitstream/1853/6846/1/Zhang_Guangfan_200505_phd.pdf

美国|英语

来源: SMARTech Repository

【 摘 要 】

Optimum Sensor Localization/Selection inA Diagnostic/Prognostic ArchitectureGuangfan Zhang107 PagesDirected by Dr. George J. VachtsevanosThis research addresses the problem of sensor localization/selection for fault diagnostic purposes in Prognostics and Health Management (PHM)/Condition-Based Maintenance (CBM) systems. The performance of PHM/CBM systems relies not only on the diagnostic/prognostic algorithms used, but also on the types, location, and number of sensors selected. Most of the research reported in the area of sensor localization/selection for fault diagnosis focuses on qualitative analysis and lacks a uniform figure of merit. Moreover, sensor localization/selection is mainly studied as an open-loop problem without considering the performance feedback from the on-line diagnostic/prognostic system. In this research, a novel approach for sensor localization/selection is proposed in an integrated diagnostic/prognostic architecture to achieve maximum diagnostic performance.First, a fault detectability metric is defined quantitatively. A novel graph-based approach, the Quantified-Directed Model, is called upon to model fault propagation in complex systems and an appropriate figure-of-merit is defined to maximize fault detectability and minimize the required number of sensors while achieving optimum performance.Secondly, the proposed sensor localization/selection strategy is integrated into a diagnostic/prognostic system architecture while exhibiting attributes of flexibility and scalability. Moreover, the performance is validated and verified in the integrated diagnostic/prognostic architecture, and the performance of the integrated diagnostic/prognostic architecture acts as useful feedback for further optimizing the sensors considered. The approach is tested and validated through a five-tank simulation system.This research has led to the following major contributions:??generalized methodology for sensor localization/selection for fault diagnostic purposes.??quantitative definition of fault detection ability of a sensor, a novel Quantified-Directed Model (QDG) method for fault propagation modeling purposes, and a generalized figure of merit to maximize fault detectability and minimize the required number of sensors while achieving optimum diagnostic performance at the system level.??novel, integrated architecture for a diagnostic/prognostic system.??lidation of the proposed sensor localization/selection approach in the integrated diagnostic/prognostic architecture.