Diagnosing Systems through Approximated Information

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Published Nov 24, 2021
Alexander Diedrich Oliver Niggemann

Abstract

This article presents a novel approach to diagnose faults in production machinery. A novel data-driven approach is presented to learn an approximation of dependencies between variables using Spearman correlation. It is further shown, how the approximation of the dependencies are used to create propositional logic rules for fault diagnosis. The article presents two novel algorithms: 1) to estimate dependencies from process data and 2) to create propositional logic diagnosis rules from those connections and perform consistency based fault diagnosis. The presented approach was validated using three experiments. The first two show that the presented approach works well for injection molding machines and a simulation of a four-tank system. The limits of the presented method are shown with the third experiment containing sets of highly correlated signals.

How to Cite

Diedrich, A., & Niggemann, O. . (2021). Diagnosing Systems through Approximated Information. Annual Conference of the PHM Society, 13(1). https://doi.org/10.36001/phmconf.2021.v13i1.2983
Abstract 57 | PDF Downloads 28

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Keywords

Fault Diagnosis, Spearman, Propositional Logic, GDE

Section
Technical Papers