Data processing: measuring – calibrating – or testing – Measurement system – Statistical measurement
Reexamination Certificate
2008-08-07
2011-10-18
Bhat, Aditya (Department: 2857)
Data processing: measuring, calibrating, or testing
Measurement system
Statistical measurement
Reexamination Certificate
active
08041539
ABSTRACT:
Principle Component Analysis (PCA) is used to model a process, and clustering techniques are used to group excursions representative of events based on sensor residuals of the PCA model. The PCA model is trained on normal data, and then run on historical data that includes both normal data, and data that contains events. Bad actor data for the events is identified by excursions in Q (residual error) and T2 (unusual variance) statistics from the normal model, resulting in a temporal sequence of bad actor vectors. Clusters of bad actor patterns that resemble one another are formed and then associated with events.
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Foslien Graber Wendy
Guralnik Valerie
Bhat Aditya
Honeywell International , Inc.
Schwegman Lundberg & Woessner, P.A.
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