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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Comparative study of signal multi-scrambling

Comparative study of signal multi-scrambling

Benmahdi, Dailia

Paperback

Physics

ISBN10: 6208837596
ISBN13: 9786208837594
Publisher: Our Knowledge Publishing
Published: Feb 9 2026
Pages: 72
Weight: 0.24
Height: 0.17 Width: 6.00 Depth: 9.00
Language: English
To detect a bearing fault, it is useful to use temporal and spectral analysis. These enable fault detection, but do not determine the nature and severity of the fault. This requires a priori knowledge of the fault characteristics and kinematic parameters of the machine. To overcome this problem, an unsupervised OPTICS classification method has been proposed for diagnosing real and simulated faults. However, dynamic classification methods and even signal processing are influenced by several parameters, such as pre-processing methods to reduce or cancel noise. For this reason, our work is based on a comparative study of two denoising methods: wavelet filtering and Fourier transform filtering (FFT).

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