Synthesizing high-fidelity time-series sensor signals to facilitate machine-learning innovations
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United States of America Patent
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Jul 19, 2022
Grant Date -
Aug 8, 2019
app pub date -
Feb 2, 2018
filing date -
Feb 2, 2018
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Abstract
The disclosed embodiments relate to a system that facilitates development of machine-learning techniques to perform prognostic-surveillance operations on time-series data from a monitored system, such as a power plant and associated power-distribution system. During operation, the system receives original time-series signals comprising sequences of observations obtained from sensors in the monitored system. Next, the system decomposes the original time-series signals into deterministic and stochastic components. The system then uses the deterministic and stochastic components to produce synthetic time-series signals, which are statistically indistinguishable from the original time-series signals. Finally, the system enables a developer to use the synthetic time-series signals to develop machine-learning (ML) techniques to perform prognostic-surveillance operations on subsequently received time-series signals from the monitored system.
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