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- W4378977427 abstract "The most critical issue of envelope demodulation analysis for rotating machinery fault diagnosis is to identify the fault-induced informative frequency bands. However, the majority of currently used blind or targeted demodulation methods either only focus on selecting a single frequency band as the best (which may result in unexpected missed diagnosis in the case of multiple faults) or require prior fault knowledge (this is unfeasible for most blind cases). To address this issue, this study proposes a coarse-to-fine demodulation frequency band selection strategy for multi-fault detection of rotating machines. A reweighted Variational Mode Decomposition algorithm, aided by a new evaluation indicator coined fault information correlation index (CFII), is first proposed to coarsely select the useful frequency bands for signal initial denoising and reconstruction. Then, Cyclic Spectral Coherence (CSCoh), a powerful cyclic spectral analysis tool, is implemented to further process the reconstructed signal for fine demodulation analysis. To solve the problem of selecting the spectral frequency band of CSCoh, a practical guideline is established together with the cyclic-frequency-domain fault information index (αFII) for the automatic determination of informative frequency bands to be integrated. Benefiting from the above two stages and developed indicators, the proposed method can effectively detect features of incipient and multiple faults under complex interferences without requiring any prior candidate fault period. Its effectiveness and advancements over several commonly used demodulation techniques are validated using simulation and experimental scenarios involving multi-fault gears and bearings." @default.
- W4378977427 created "2023-06-02" @default.
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- W4378977427 date "2023-08-01" @default.
- W4378977427 modified "2023-09-27" @default.
- W4378977427 title "A coarse-to-fine demodulation frequency band selection strategy for multi-fault detection of rotating machinery" @default.
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- W4378977427 doi "https://doi.org/10.1016/j.measurement.2023.113108" @default.
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