Fault diagnosis is essentially a kind of pattern recognition. The measured signal samples usually distribute on nonlinear low-dimensional manifolds embedded in the high-dimensional signal space, so how to implement feature extraction, dimensionality reduction and improve recognition performance is a crucial task. In this paper a novel machinery fault diagnosis approach based on a statistical locally linear embedding (S-LLE) algorithm which is an extension of LLE by exploiting the fault class label information is proposed. The fault diagnosis approach first extracts the intrinsic manifold features from the high-dimensional feature vectors which are obtained from vibration signals that feature extraction by time-domain, frequency-domain and empirical mode decomposition (EMD), and then translates the complex mode space into a salient low-dimensional feature space by the manifold learning algorithm S-LLE, which outperforms other feature reduction methods such as PCA, LDA and LLE. Finally in the feature reduction space pattern classification and fault diagnosis by classifier are carried out easily and rapidly. Rolling bearing fault signals are used to validate the proposed fault diagnosis approach. The results indicate that the proposed approach obviously improves the classification performance of fault pattern recognition and outperforms the other traditional approaches.

Making good use of vertical axis wind turbines (VAWTs) is an attractive and potential way to deal with the energy and environmental issues due to the unique superiorities of it. Computational Fluid Dynamics (CFD) technology is a useful tool for the design process of VAWTs. Various turbulence models have been developed and available for turbulent flow simulations. Currently, there have been few researchers studying on meshing strategies and turbulence model selections of VAWT simulations. In this paper, 2D unsteady models under 4 meshing strategies and 6 turbulence models were established and simulated to investigate the effect of the above two aspects on numerical simulations of VAWTs. The numerical results were compared with the experimental data of Oler et al. (“Dynamic stall regulation of the Darrieus Turbine,” SAND Report No. 83–7029, Sandia National Laboratories, Albuquerque, 1983, pp. 67–96) and the analytical solution of Deglaire et al. (Eur. J. Mech., B: Fluids 28(4), 506–520 (2009)). The results reveal that a mesh of 213 656 grids is sufficient to meet the requirements of grid independence with the help of boundary layer and size function techniques. Besides, the realizable k-ε model enables the closest CFD simulation of the experimental data and shows better prediction performance than the analytical model of Deglaire et al. and other turbulence models.

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On the circular blade path of a Darrieus-type vertical axis wind turbine (VAWT), there are two azimuth positions where torques are small or even negative. These two azimuths have the lowest aerodynamic performance and greatly weaken the overall performance of the VAWT. An approach is proposed to rebuild the flow field of the turbine blades by applying two additional airflows at the two azimuths to enhance the local performance. The aim is to improve the overall power coefficient of the VAWT. A Φ-type VAWT with a diameter of 2 m and a height of 2 m is studied to evaluate the effectiveness of the proposed approach. The effect of additional airflow on the aerodynamic performance of the blade and the turbine is researched using the double multiple streamtube model. The simulation results suggest that the additional airflows are effective in promoting the performance of the rotor blades and the overall rotor; the increase in maximum power coefficient achieved was 6% at an optimum tip speed ratio. Some rules of the effect on the performances of the rotor are obtained in this paper.