Analyzing high-dimensional thresholds for fault detection and diagnosis using active learning and Bayesian statistical modeling

Dr. Yuning He
Submission Type: 
Full Paper
phmc_15_049.pdf1.39 MBAugust 20, 2015 - 4:44pm

Many Fault Detection and Diagnosis (FDD) systems use discrete models for fault detection and analysis. Complex industrial systems generally have hundreds of sensors, which are used to provide data to the FDD system. Usually, the FDD wrapper code discretizes each sensor value individually and ignores any non-linearities as well as correlations between different sensor signals. This can easily lead to overly conservative threshold settings potentially resulting in many false alarms.
In this paper, we describe an advanced statistical framework that uses Bayesian dynamic modeling and active learning techniques to detect and characterize a threshold surface and shape in a high-dimensional space. The use of active learning techniques can drastically reduce the effort to study threshold surfaces. Automated Bayesian modeling of complex threshold surfaces has the potential to improve quality and performance of traditional wrapper code, which often uses hypercube thresholds.

Publication Year: 
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Submission Keywords: 
bayesian statistics
high-dimensional threshold surface
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
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