Accurate Detection of Avian Respiratory Viruses by Use of Multiplex PCR-Based Luminex Suspension Microarray Assay.

A novel oligonucleotide suspension microarray (Luminex microsphere system) was developed for the rapid detection of avian respiratory viruses of major clinical importance. This test was optimized and validated with 70 clinical samples. The developed tool was accurate for high-throughput detection and differentiation of the most important avian respiratory viruses: avian influenza virus (AIV), Newcastle disease virus (NDV), infection bronchitis virus (IBV), and infectious laryngotracheitis virus (ILTV) in single- and mixed-virus infections. A multiplex reverse transcriptase PCR (RT-PCR), followed by a monoplex or a multiplex Luminex assays, were realized using a Luminex 200 analyzer instrument. The sensitivity, specificity, and reproducibility of the multiplex DNA suspension microarray system were evaluated. The results showed no significant differences in the median fluorescence intensity (MFI) value in monoplex and multiplex Luminex assays. The sensitivity and specificity proved to be completely concordant with monoplex real-time RT-PCR. We demonstrated that the multiplex DNA suspension microarray system is an accurate, high-throughput, and relatively simple method for the rapid detection of the main respiratory viruses of poultry.

The use of SESK as a trend parameter for localized bearing fault diagnosis in induction machines.

A critical work of bearing fault diagnosis is locating the optimum frequency band that contains faulty bearing signal, which is usually buried in the noise background. Now, envelope analysis is commonly used to obtain the bearing defect harmonics from the envelope signal spectrum analysis and has shown fine results in identifying incipient failures occurring in the different parts of a bearing. However, the main step in implementing envelope analysis is to determine a frequency band that contains faulty bearing signal component with the highest signal noise level. Conventionally, the choice of the band is made by manual spectrum comparison via identifying the resonance frequency where the largest change occurred. In this paper, we present a squared envelope based spectral kurtosis method to determine optimum envelope analysis parameters including the filtering band and center frequency through a short time Fourier transform. We have verified the potential of the spectral kurtosis diagnostic strategy in performance improvements for single-defect diagnosis using real laboratory-collected vibration data sets.

Application of higher order spectral features and support vector machines for bearing faults classification.

Condition monitoring and fault diagnosis of rolling element bearings timely and accurately are very important to ensure the reliability of rotating machinery. This paper presents a novel pattern classification approach for bearings diagnostics, which combines the higher order spectra analysis features and support vector machine classifier. The use of non-linear features motivated by the higher order spectra has been reported to be a promising approach to analyze the non-linear and non-Gaussian characteristics of the mechanical vibration signals. The vibration bi-spectrum (third order spectrum) patterns are extracted as the feature vectors presenting different bearing faults. The extracted bi-spectrum features are subjected to principal component analysis for dimensionality reduction. These principal components were fed to support vector machine to distinguish four kinds of bearing faults covering different levels of severity for each fault type, which were measured in the experimental test bench running under different working conditions. In order to find the optimal parameters for the multi-class support vector machine model, a grid-search method in combination with 10-fold cross-validation has been used. Based on the correct classification of bearing patterns in the test set, in each fold the performance measures are computed. The average of these performance measures is computed to report the overall performance of the support vector machine classifier. In addition, in fault detection problems, the performance of a detection algorithm usually depends on the trade-off between robustness and sensitivity. The sensitivity and robustness of the proposed method are explored by running a series of experiments. A receiver operating characteristic (ROC) curve made the results more convincing. The results indicated that the proposed method can reliably identify different fault patterns of rolling element bearings based on vibration signals.