Autonomous quality assurance and troubleshooting

Abstract

ABSTRACT To improve operational availability (the proportion of time that a telescope is able to accomplish what a visiting observer wants at the time the observation is scheduled), response time to faults must be minimized. One way this can be accomplished is by characterizing the relationships and interdependencies between components in a control system, developing algorithms to identify the root cause of a problem, and capturing expert knowledge of a system to simplify the process of troubleshooting. Results from a prototype development are explained, along with deployment issues. Implications for the future, such as effective knowledge representation and management, and learning processes which integrate autonomous and interactive components, are discussed. Keywords: expert systems, data quality, troubleshooting, rules engine, autonomous (autonomic) computing 1. INTRODUCTION Efficient operation of a complex, dynamic system like a radio telescope requires rapid identification of and recovery from system failures and an efficient means of evaluating th e quality of data from observations. Each hour of observing on a radio telescope costs thousands of dollars, therefore it is important to ensure that the hardware and software are in good shape for observations. However, the nature of research presents a major challenge to this goal. Research pushes the limits of the telescope, making it very difficult to monitor the ongoing health of the system and the quality of its data. The Robert C. Byrd Green Bank Telescope (GBT) at the National Radio Astronomy Observatory (NRAO) in Green Bank, W.Va., is a highly dynamic system. An observer can configure the GBT in thousands of different modes, and the telescope continuously receives new system s and features. The emerging quality pr ogram in Green Bank seeks to assure that, regardless of how the GBT is configured , the telescope has proper “fitness for use,”