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Automatic Drift Correction through Nonlinear Sensing

Conference ·
 [1];  [1];  [1]
  1. ORNL
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For successful design and operation of advanced monitoring and control systems, engineers rely on high quality sensor signals that are simultaneously accurate, representative, voluminous, and timely. Unfortunately, sensor faults are common and lead to short-lived symptoms, such as outliers and spikes as well as long-lived symptoms, such as sensor drift. Sensor drift belongs to the category of incipient faults. These are particularly challenging to detect, diagnose, and correct as the time scales of these faults are typically longer than the time scales of the system dynamics that are of interest. Moreover, if sensor drift occurs as a result of exposure to measured medium, then it is likely that multiple sensors will exhibit similar drift rates, thus challenging fault management strategies based on redundancy. In this contribution, we present a first method that can handle this unique challenge.

Research Organization:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Sponsoring Organization:
USDOE Office of Energy Efficiency and Renewable Energy (EERE)
DOE Contract Number:
AC05-00OR22725
OSTI ID:
1871123
Resource Relation:
Conference: Resilience Week (RWS) - Washington D.C., District of Columbia, United States of America - 10/18/2021 12:00:00 PM-10/21/2021 12:00:00 PM
Country of Publication:
United States
Language:
English

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