Optimum Sensor Localization/Selection In A Diagnostic/Prognostic Architecture

• Main Author: Zhang, Guangfan
• Format: Others
• Language: en_US
• Published: Georgia Institute of Technology 2005
• Subjects: Sensor localization; Sensor selection; Diagnostics; Prognostics; System failures (Engineering); Automatic test equipment; Detectors; Fault location (Engineering)
• Online Access: http://hdl.handle.net/1853/6846

Optimum Sensor Localization/Selection in A Diagnostic/Prognostic Architecture Guangfan Zhang 107 Pages Directed by Dr. George J. Vachtsevanos This 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.