Macalou, A. | Search NASA

NASA NTRS1993

Real-time expert system diagnostics and monitoring for the High Resolution Microwave Survey Targeted Search

An automated monitoring and diagnostics system (MDS) using virtual real-time software was developed for NASA's High Resolution Microwave Survey (HRMS) Targeted Search System (TSS). The four main tasks required of the MDS were monitoring and recording system health, alerting operators of problems, diagnosing poor system performance, and performing an emergency system shutdown. The MDS was implemented using commercial expert system software tools in addition to interface hardware and software developed on site. The expert system used objects, rules, and schematics in its TSS knowledge representation. The MDS was successfully integrated into the HRMS computer environment, and its performance met or exceeded its requirements.

Macalou, A.↗

NASA NTRS1991

Search-based model identification of smart-structure damage

This paper describes the use of a combined model and parameter identification approach, based on modal analysis and artificial intelligence (AI) techniques, for identifying damage or flaws in a rotating truss structure incorporating embedded piezoceramic sensors. This smart structure example is representative of a class of structures commonly found in aerospace systems and next generation space structures. Artificial intelligence techniques of classification, heuristic search, and an object-oriented knowledge base are used in an AI-based model identification approach. A finite model space is classified into a search tree, over which a variant of best-first search is used to identify the model whose stored response most closely matches that of the input. Newly-encountered models can be incorporated into the model space. This adaptativeness demonstrates the potential for learning control. Following this output-error model identification, numerical parameter identification is used to further refine the identified model. Given the rotating truss example in this paper, noisy data corresponding to various damage configurations are input to both this approach and a conventional parameter identification method. The combination of the AI-based model identification with parameter identification is shown to lead to smaller parameter corrections than required by the use of parameter identification alone.

Glass, B. J.↗