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Abstract
A successful program of aircraft health maintenance should elevate complex bus parameters to be as visually persuasive as the condition indicators (green, yellow, red) and as statistically relevant as the corresponding vibration thresholds. This work examines UH-60M avionics and introduces five visualization methods appropriate for displaying large, multi-attribute datasets: 1) panel plots for multi-attribute data on a single time axis, 2) horizon plots to color-code important regions in a compact view, 3) box plots to isolate factors that may be obscured by simple trending; 4) correlation matrices to rigorously group common views; and 5) parallel coordinates to illustrate these multi-attribute dependencies over time. The research identifies horizon plots as particularly insightful to extend engine and flight analysis beyond the traditional strip chart.

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  Visual Exploration of Complex Avionics’ Time Series

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