A modified cross-correlation algorithm for PIV image processing of particle-fluid two-phase flow

PIV (Particle Image Velocimetry) technique for flow field measurement has achieved popular self-identify through over ten years development, and its application range is becoming wider and wider. PIV post-processing techniques have a great influence on the success of particle-fluid two-phase flow field measurement and thus become a hot and difficult topic. In the present study, a Phase Respective Identification Algorithm (PRIA) is introduced to separate low-density solid particles or bubbles and high-density tracer particles from the PIV image of particle-fluid two-phase flow. PTV (Particle Tracking Velocimetry) technique is employed to calculate the velocity fields of low-density solid particles or bubbles. For the velocity fields of high-density solid particles or bubble phase and continuous phase traced by high-density smaller particles, based on the thought of wavelet transform and multi-resolution analysis and the theory of cross-correlation of image, a delaminated processing algorithm (MCCWM) is presented to conquer the limitation of conventional Fourier transform. The algorithm is firstly testified on synthetic two-phase flows, such as uniform steady flow, shearing flow and rotating flow, and the computational results from the simulated particle images are in reasonable agreement with the given simulated data. The algorithm is then applied to images of actual bubble-liquid two-phase flow and jet flow, and the results also confirmed that the algorithm proposed in the present study has good performance and reliability for post-processing PIV images of particle-fluid two-phase flow.     

A modified optical flow algorithm based on bilateral-filter and multi-resolution analysis for PIV image processing

Particle Image Velocimetry (PIV) technology is an efficient and powerful testing method to investigate the characteristics of flow field. The topic of PIV post-processing techniques has roused researchers׳ wide concern for its great influence on the success of flow field measurement. The traditional correlation algorithms have their innate defects. In the present study, a modified optical flow algorithm is proposed to overcome these deficiencies based on bilateral-filter and multi-resolution analysis of PIV image processing. The algorithm is designed based on the principle of multilayer segments, in which the isotropic diffusion method is employed to calculate the low-resolution layer of the image and the nonlinear filtering method is used to process the high-resolution layer. This new algorithm can reduce image noise effectively and maintain the details of the image boundary. In addition, the design of nonlinear filter makes the optical flow equation simpler, and the optimal velocity mapping factor method needs less iteration and reduces the computational load. The algorithm is first tested on synthetic time-resolved channel flow images, and the computational results from the simulated particle images are found to be in reasonable agreement with the given simulated data. The algorithm is then applied to images of actual up-channel flow, and the results also confirmed that the algorithm proposed in the present study has good performance and reliability for post-processing PIV images.     

相序检测的极速算法

醉提出了一种相序检测的新算法，该算法运算极快，使相序检测的速度提高了1000至10000倍，可广泛运用于数字化检测之中。     

An automatic method for identifying appropriate gradient magnitude for 3D boundary detection of confocal image stacks

Gradients play an important role in 2D image processing. Many edge detection algorithms are gradient‐based. We are interested in 3D boundary detection which can be considered as an extension of 2D edge detection in 3D space. In this paper, an algorithm to automatically and quantitatively measure the suitability of gradient magnitudes in detection of 3D boundary points of confocal image stacks is presented. A Measurement Function is defined to evaluate the suitability of each gradient magnitude chosen to be the threshold for 3D boundary detection. The application of Gauss's Divergence Theorem provides a solution to calculate the Measurement Function numerically. The gradient magnitude at which the maximum of the Measurement Function is achieved can be utilized as the most appropriate threshold for gradient‐based boundary detection and other operations like volume visualization.     

An approach for automated fault diagnosis based on a fuzzy decision tree and boundary analysis of a reconstructed phase space

Although reconstructed phase space is one of the most powerful methods for analyzing a time series, it can fail in fault diagnosis of an induction motor when the appropriate pre-processing is not performed. Therefore, boundary analysis based a new feature extraction method in phase space is proposed for diagnosis of induction motor faults. The proposed approach requires the measurement of one phase current signal to construct the phase space representation. Each phase space is converted into an image, and the boundary of each image is extracted by a boundary detection algorithm. A fuzzy decision tree has been designed to detect broken rotor bars and broken connector faults. The results indicate that the proposed approach has a higher recognition rate than other methods on the same dataset.     

A family of non-conforming crack front elements of quadrilateral and triangular types for 3D crack problems using the boundary element method

This study focuses on establishing non- conforming crack front elements of quadrilateral and triangular types for 3D crack problems when the dual boundary element method is applied. The asymptotic behavior of the physical variables in the area near the crack front is fully considered in the construction of the shape function. In the developed quadrilateral and triangular crack front elements, the asymptotic term, which captures the asymptotic behavior of the physical variable, is multiplied directly by the conventional Lagrange shape function to form a new crack front shape function. Several benchmark numerical examples that consider penny-shaped cracks and straight-edge crack problems are presented to illustrate the validity and efficiency of the developed crack front elements.     

高压三相感应电机缺相检测的一种新技术

Open phase in three phase induction motors is a common fault that can occur as a result of a fuse blowing or a pro- tective device failing on one phase of the motor. This paper introduces a new method,which is based on the transient mea- surement and can distinguish the fault of one phase connecting ground. The method has been proved to be in correspondence with the simulation results by Matlab and LabVIEW in practice, The method has merits of simplicity, accuracy and ease of USe.     

A CO2 tracer-gas method for local air leakage detection and characterization

A method for local measurement of air leakage rate is presented that can be used to accurately and quickly assess leakage rates across a surface, such as around a valve or hatch in a pressurized gas tank or a window in a building. The method uses a small local enclosure with constant volume placed about a region on the structure under investigation (e.g., a window), which is depressurized and injected with a small concentration of carbon dioxide as a tracer gas. The time variation of the pressure and carbon dioxide concentration inside the enclosure are monitored and used to quantify the leakage flow rate as a function of pressure difference. This method uses a small enclosure with internal mixing so that a quasi-steady-state condition is quickly achieved. Because of the small size of the enclosure, advanced data processing techniques are necessary to reduce uncertainty in determination of the rate of change of the carbon dioxide concentration that arises from sensor variability. Results of a laboratory demonstration of the proposed leakage detection and characterization device are reported for the problem of leakage through a circular hole in a plate with prescribed pressure differences. Experimental results from the laboratory tests are found to be in excellent agreement with results of a numerical simulation of leakage flow through a hole, as well as predictions from a number of empirical equations for this problem found in the literature.     

A hybrid intelligent multi-fault detection method for rotating machinery based on RSGWPT,KPCA and Twin SVM

This paper proposes a hybrid intelligent method for multi-fault detection of rotating machinery, in which three methods, i.e. including the redundant second generation wavelet package transform (RSGWPT), the kernel principal component analysis (KPCA) and the twin support vector machine (TWSVM), are combined. Firstly, RSGWPT is used to extract feature vectors from representative statistical characteristics in the decomposition frequency band, and then the KPCA in the feature space is performed to reduce the dimension of features and to extract the dominant features for the following classification. Finally, a novel support vector machine, called twin support vector machine is used to construct a multi-class classifier. Inputting superior features to this classifier, the condition of the monitored machine component can be determined. Experimental results demonstrate that the proposed hybrid method is effective for multi-fault detection of rotating machinery. The TWSVM is also indicated that has better classification performance and faster convergence speed than the normal SVM.