A Geometric Interpretation of Diagnostic Data from a Digital Machine: Based on a Study of the Morris, Illinois Electronic Central Office Illinois Electronic Central Office

Using the diagnostic data collected for the Morris Central Control malfunction dictionary, a natural concept of “distances” between malfunctions is devised, which suggests a technique for locating malfunctions and also suggests some longer-range possibilities.

Abstract

Using the diagnostic data collected for the Morris Central Control malfunction dictionary, we devise a natural concept of “distances” between malfunctions. Ten thousand malfunctions were placed as points in six-dimensional space in such a way that the Euclidean interpoint distances approximately equaled the diagnostic “distances“. The remarkable fact that this is possible has many implications. By finding circuit characteristics common to a cluster of neighboring malfunctions, we are able to associate these characteristics with the region of the six-dimensional space which holds these malfunctions. By this means, we characterize various regions of space according to functional troubles. This suggests a technique for locating malfunctions and also suggests some longer-range possibilities.