TY - GEN
T1 - Prognosis of failure precursor in complex electrical systems using symbolic dynamics
AU - Patankar, Ravindra
AU - Rajagopalan, Venkatesh
AU - Tolani, Devendra
AU - Ray, Asok
AU - Begin, Michael
PY - 2007
Y1 - 2007
N2 - Failures in a plant's electrical components are a major source of performance degradation and plant unavailability, In order to detect and monitor failure precursors and anomalies early in electrical systems, we have developed signal processing capabilities that can detect and map patterns in already existing and available signals to an anomaly measure. Toward this end, the language measure theory based on real analysis, finite state automaton, symbolic dynamics and information theory has been deployed. Application of this theory for electronic circuit failure precursor detection resulted in a robust statistical pattern recognition technique. This technique was observed to be superior to conventional pattern recognition techniques such as neural networks and principal component analysis for anomaly detection because it exploits a common physical fact underling most anomalies which conventional techniques do not. Symbolic dynamic technique resulted in a monotonically increasing smooth anomaly plot which was experimentally repeatable to a remarkable accuracy. For the Van der Pol oscillator circuit board experiment, this lead to consistently accurate predictions for the anomaly parameter and its range.
AB - Failures in a plant's electrical components are a major source of performance degradation and plant unavailability, In order to detect and monitor failure precursors and anomalies early in electrical systems, we have developed signal processing capabilities that can detect and map patterns in already existing and available signals to an anomaly measure. Toward this end, the language measure theory based on real analysis, finite state automaton, symbolic dynamics and information theory has been deployed. Application of this theory for electronic circuit failure precursor detection resulted in a robust statistical pattern recognition technique. This technique was observed to be superior to conventional pattern recognition techniques such as neural networks and principal component analysis for anomaly detection because it exploits a common physical fact underling most anomalies which conventional techniques do not. Symbolic dynamic technique resulted in a monotonically increasing smooth anomaly plot which was experimentally repeatable to a remarkable accuracy. For the Van der Pol oscillator circuit board experiment, this lead to consistently accurate predictions for the anomaly parameter and its range.
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U2 - 10.1109/ACC.2007.4282219
DO - 10.1109/ACC.2007.4282219
M3 - Conference contribution
AN - SCOPUS:46449121925
SN - 1424409888
SN - 9781424409884
T3 - Proceedings of the American Control Conference
SP - 1846
EP - 1851
BT - Proceedings of the 2007 American Control Conference, ACC
T2 - 2007 American Control Conference, ACC
Y2 - 9 July 2007 through 13 July 2007
ER -