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Current research in broken rotor bar (BRB) fault detection in induction motors is primarily focused on a high-frequency resolution analysis of the stator current. Compared with a discrete Fourier transformation, the parametric spectrum estimation technique has a higher frequency accuracy and resolution. However, the existing detection methods based on parametric spectrum estimation cannot realize online detection, owing to the large computational cost. To improve the efficiency of BRB fault detection, a new detection method based on the min-norm algorithm and least square estimation is proposed in this paper. First, the stator current is filtered using a band-pass filter and divided into short overlapped data windows. The min-norm algorithm is then applied to determine the frequencies of the fundamental and fault characteristic components with each overlapped data window. Next, based on the frequency values obtained, a model of the fault current signal is constructed. Subsequently, a linear least squares problem solved through singular value decomposition is designed to estimate the amplitudes and phases of the related components. Finally, the proposed method is applied to a simulated current and an actual motor, the results of which indicate that, not only parametric spectrum estimation technique.

Compared with single rotor small axial flow fans, dual-rotor small axial flow fans is better regarding the static characteristics. But the aerodynamic noise of dual-rotor small axial flow fans is worse than that of single rotor small axial flow fans. In order to improve aerodynamic noise of dual-rotor small axial flow fans, the pre-stage blades with different perforation numbers are designed in this research. The RANS equations and the standard k-ɛ turbulence model as well as the FW-H noise model are used to simulate the flow field within the fan. Then, the aerodynamic performance of the fans with different perforation number is compared and analyzed. The results show that: (1) Compared to the prototype fan, the noise of fans with perforation blades is reduced. Additionally, the noise of the fans decreases with the increase of the number of perforations. (2) The vorticity value in the trailing edge of the pre-stage blades of perforated fans is reduced. It is found that the vorticity value in the trailing edge of the pre-stage blades decreases with the increase of the number of perforations. (3) Compared to the prototype fan, the total pressure rising and efficiency of the fans with perforation blades drop slightly.

The unique Coanda surface has a great influence on the performance of bladeless fan. However, there is few studies to explain the relationship between the performance and Coanda surface curvature at present. In order to gain a qualitative understanding of effect of the curvature on the performance of bladeless fan, numerical studies are performed in this paper. Firstly, three-dimensional numerical simulation is done by Fluent software. For the purpose to obtain detailed information of the flow field around the Coanda surface, two-dimensional numerical simulation is also conducted. Five types of Coanda surfaces with different curvature are designed, and the flow behaviour and the performance of them are analyzed and compared with those of the prototype. The analysis indicates that the curvature of Coanda surface is strongly related to blowing performance, It is found that there is an optimal curvature of Coanda surfaces among the studied models. Simulation result shows that there is a special low pressure region. With increasing curvature in Y direction, several low pressure regions gradually enlarged, then begin to merge slowly, and finally form a large area of low pressure. From the analyses of streamlines and velocity angle, it is found that the magnitude of the curvature affects the flow direction and reasonable curvature can induce fluid flow close to the wall. Thus, it leads to that the curvature of the streamlines is consistent with that of Coanda surface. Meanwhile, it also causes the fluid movement towards the most suitable direction. This study will provide useful information to performance improvements of bladeless fans.

To effectively extract the interturn short circuit fault features of induction motor from stator current signal, a novel feature extraction method based on the bare-bones particle swarm optimization (BBPSO) algorithm and wavelet packet was proposed. First, according to the maximum inner product between the current signal and the cosine basis functions, this method could precisely estimate the waveform parameters of the fundamental component using the powerful global search capability of the BBPSO, which can eliminate the fundamental component and not affect other harmonic components. Then, the harmonic components of residual current signal were decomposed to a series of frequency bands by wavelet packet to extract the interturn circuit fault features of the induction motor. Finally, the results of simulation and laboratory tests demonstrated the effectiveness of the proposed method.