Faulty-Trunk Detection Algorithms Using EADAS/ICUR Traffic Data

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Abstract
A class of algorithms for detecting abnormally short-holding-time trunks has been developed that utilizes individual trunk data available in eadas/icur (Engineering and Administrative Data Acquisition System/Individual Circuit Usage Recorder). This data consists of a two-dimensional statistic that compresses the raw trunk measurements–the state of the trunk (busy or idle) sampled every 100 or 200 seconds–into a manageable form. Because this data is essentially a sufficient statistic for the stochastic process used to model the (unobservable) trunk state measurements, one of the algorithms developed is Wald's sequential probability ratio test. Two of the algorithms developed have been implemented in ican (Individual Circuit Analysis Program), and are currently being used to test trunks associated with the No. 1 crossbar, No. 5 crossbar, crossbar tandem (1XB, 5XB, XBT), and step-by-step switching machines. The focus in this paper, however, is on the modeling and analysis aspects of the problem, and only slight attention is paid to the various trade-offs and real-world constraints encountered in implementing the algorithms.

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