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As the degradation of bearing yield to an enormous adverse impact on machinery and the damage that comes within could jeopardize human precious life. Hence, the bearing fault diagnosis is indisputably indispensable. This paper is predominantly focused on the utilization of Convolutional Neural Network (CNN) in bearing fault diagnosis of the rolling bearing. By deployment of CNN, an accurate diagnosis can be achieved without the necessity of pre-training the data. The function of CNN in diagnosing the bearing and architecture development of CNN are discussed. Lastly, to establish new and significant contribution in this area, new challenges are pinpointed.

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Modified wake oscillator model for vortex-induced motion prediction of low aspect ratio structures Floating structures, such as Spar platform, are likely to experience significant motions while operation due to Vortex-induced Motion (VIM). The main features of VIM include aspect ratio and mass ratio of the structure. This paper focuses on further development of a previously published semi-empirical method called wake oscillator model (WOM) for VIM, which aims to derive a mathematical correlation in modelling all features of the phenomenon. Analytical solution is proposed to coupled WOM and structure oscillator model based on the Van der Pol equation by integrating mass ratio, m* = 4.36, and various low aspect ratios between 0.3 and 2.0. Additionally, a new approach was initiated to embed several empirical parameters linked to the aspect ratio in the existing WOM. It was found the aspect ratio parameter in the modified WOM significantly affected the performance of the model for estimation of VIM on floating structures.