Contribution of random sampling in the context of rotating machinery diagnostic

Abstract : Nowadays, machine monitoring and supervision became one of the most important domains of research. Many axes of exploration are involved in this domain: signal processing, machine learning and several others. Besides, industrial systems can now be remotely monitored because of the internet availability. In fact, as many other systems, machines can now be connected to any network by a specified address due to the Internet of Things (IOT) concept. However, this combination is challenging in data acquisition and storage. In 2004, the compressive sensing was introduced to provide data with low rate in order to save energy consumption within wireless sensor networks. This aspect can also be achieved using random sampling (RS). This approach is found to be advantageous in acquiring data randomly with low frequency (much lower than Nyquist rate) while guaranteeing an aliasing-free spectrum. However, this method of sampling is still not available by hardware means in markets. Thus, a comprehensive review on its concept, its impact on sampled signal and its implementation in hardware is conducted. In this thesis, a study of RS and its different modes is presented with their conditions and limitations in time domain. A detailed examination of the RS’s spectral analysis is then explained. From there, the RS features are concluded. Also, recommendations regarding the choice of the adequate mode with the convenient parameters are proposed. In addition, some spectral analysis techniques are proposed for RS signals in order to provide an enhanced spectral representation. In order to validate the properties of such sampling, simulations and practical studies are shown. The research is then concluded with an application on vibration signals acquired from bearing and gear. The obtained results are satisfying, which proves that RS is quite promising and can be taken as a solution for reducing sampling frequencies and decreasing the amount of stored data. As a conclusion, the RS is an advantageous sampling process due to its anti-aliasing property. Further studies can be done in the scope of reducing its added noise that was proven to be cyclostationary of order 1 or 2 according to the chosen parameters
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Mayssaa Hajar. Contribution of random sampling in the context of rotating machinery diagnostic. Signal and Image Processing. Université de Lyon; Université libanaise, 2018. English. ⟨NNT : 2018LYSES001⟩. ⟨tel-02137521⟩

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