This paper presents analysis, design, development, and experimental verification of a non-destructive monitoring system for diagnostics of electric conductors based on the concept of Electro-Magnetic-Acoustic Transducers (EMAT). Electric conductors, in general, are exposed to harsh environments. Such conductors include electric transmission lines and ground mat risers. For automatic failure detection and assessment of mechanical integrity of these conductors, in addition to an effective transducer, feature extraction and pattern recognition techniques have to be employed. Details of the sensor design, neural-based signature analysis, feature extraction, and results of fault detection techniques are presented. Design of an experimental testbed for performance verification of the sensor and successful implementation of fault classifications are described and results are delineated.

This paper presents analysis, design, development, and experimental verification of a non-destructive monitoring system for diagnosis of mechanical integrity of electric conductors based on the concept of Electro-Magnetic-Acoustic Transducers (EMAT). Electric conductors, in general, are exposed to harsh environments. Such conductors include electric transmission lines, anchor rods, and ground mat risers. For automatic failure detection and assessment of mechanical integrity of these conductors, in addition to an effective transducer, feature extraction and pattern recognition techniques have to be employed. Details of the sensor design, neural-based signature analysis, feature extraction, and experimental results of fault detection techniques are presented.

No abstract

This paper presents a reconfigurable control design technique that integrates a robust feedback and an iterative learning control (ILC) scheme. This technique is applied to develop vehicle control systems that are tolerant to failures due to malfunctions or damages. The design procedure includes solving the robust performance condition for a feedback controller through the use of μ-synthesis that also satisfies the convergence condition for the iterative learning control rule. The effectiveness of the proposed approach is verified by simulation experiments using a radio-controlled (R/C) model airplane. The methods presented in this paper can be applied to design of global intelligent control systems to improve the operating characteristics of a vehicle and increase safety and reliability.

In order to optimize the use of fault tolerant controllers for unmanned or autonomous aerial vehicles, a health diagnostics system is being developed. To autonomously determine the effect of damage on global vehicle health, a feature-based neural-symbolic network is utilized to infer vehicle health using historical data. Our current system is able to accurately characterize the extent of vehicle damage with 99.2% accuracy when tested on prior incident data. Based on the results of this work, neural-symbolic networks appear to be a useful tool for diagnosis of global vehicle health based on features of subsystem diagnostic information.