Missing Sensors and Rare Failures in Production Evidence
Abstract
Rare failures make ordinary accuracy a weak guide to inspection decisions. A secondary analysis of the public SECOM production data uses a small, prespecified Logit model to examine failure associations with sensor measurements, missingness, and record order. The file contains 104 failures among 1,567 production records. The first three sensor columns show imprecise adjusted associations, while missingness and record order are associated with the outcome. These findings are descriptive and do not identify controllable process mechanisms. A methodological comparison of constrained selection, partial inspection, decision loss, and adaptive learning explains why apparent fit must be complemented by explicit joint feasibility and prospective coverage.
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