Key result
Advanced time-frequency algorithms including Synchrosqueezing Transform effectively monitor highly nonstationary signals.
Why the study?
Processing highly nonstationary signals in the time-frequency domain requires advanced non-parameterized representations to uncover important hidden signatures in condition monitoring applications.
The study evaluates advanced non-parameterized time-frequency algorithms for condition monitoring of nonstationary signals.
May enhance nonstationary signal monitoring in cardiology; leaves open clinical adoption pending validation studies.
Signals recorded in various condition monitoring applications represent important hidden signatures in time-frequency plane. In order to process highly nonstationary signals in time-frequency domain, advanced non-parameterized time-frequency representations are showing promising results. This paper investigates the applicability of non-parameterized time-frequency algorithms for condition monitoring by analyzing two distinct signal types. The signals studied encompass EEG epileptic seizures and electrical partial discharge signals. The analytical methods involve Synchrosqueezing Transform and its enhanced iterations, representing signals in their time-frequency domains with advanced sophistication. The outcomes offer a comparative assessment of various non-parameterized techniques, elucidating the preferred approach for effectively monitoring signals in such conditions.
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Ghosh et al. (2023) studied EEG epileptic seizures and electrical partial discharge signals. Synchrosqueezing Transform and enhanced iterations was evaluated on Comparative assessment of various non-parameterized techniques. Advanced non-parameterized time-frequency algorithms, including Synchrosqueezing Transform, offer a preferred approach for effectively monitoring highly nonstationary signals.
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