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- W4384788551 abstract "Fault diagnosis can utilise signal processing in various ways: informative time domain signals are separated or transformed from the source signals and used in feature extraction to find useful fault indicators. Frequency domain analyses focus on identifying interesting signal components. This research integrates different solutions together: differentiation is used for modifying the source signal and feature extraction is based on the generalised spectral norms which have been studied by the authors before. The concept of these norms is intended to give a method of spectral analysis with less human labour and, potentially, even entirely without it. We discuss the possibility of utilising the spectral norms along with real order derivatives. The solution was tested for bearing and misalignment faults induced in two test grids. The potential challenges of selecting parameters and interpreting results are discussed. Results show that when used with a properly selected set of indices, the presented method can be an effective tool for condition monitoring. Moreover, this technique provides an opportunity for automated analytics also for frequency domain analyses." @default.
- W4384788551 created "2023-07-20" @default.
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- W4384788551 date "2023-01-01" @default.
- W4384788551 modified "2023-09-26" @default.
- W4384788551 title "Real Order Derivatives and Spectral Norms in Fault Detection" @default.
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- W4384788551 doi "https://doi.org/10.1007/978-981-99-1988-8_9" @default.
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