Automatic Fault Diagnosis of Rotating Machinery (Published)
A key challenge to successful implementation of Time-Frequency (TF) analysis for machine diagnosis is the development of an accurate and consistent method to interpret the images so that they truly reflect machine condition. In this paper, the time-frequency domain is used to study signals from industrial bearings. Examination results are presented as TF images. A Fuzzy logic approach is developed to classify Fourier Descriptors obtained from the time–frequency images so that the fault can be automatically identified. The analysis and results of experimental data indicate that bearing faults can be correctly classified using the developed method.
Alternative Outliers Detection Procedures In Linear Regression Analysis : A Comparative Study (Published)
A Common problem in linear regression analysis is outliers, which produces undesirable effects on the least squares estimates. Many widely used regression diagnostics procedures have been introduced to detect these outliers. However, such diagnostics, which are based on the least squares estimates, are not efficient and cannot detect correctly swamping and masking effects. In this paper, we attempt to investigate the robustness of some well known diagnostics tools, namely, Cook’s distance, the Welsch-Kuh distance and the Hadi measure. The robust version of these diagnostics based on the Huber-M estimation have been proposed to identify the outliers. A simulation study is performed to compare the performance of the classical diagnostics with the proposed versions. The findings of this study indicate that, the proposed alternative versions seem to be reasonable well and should be considered as worthy robust alternative to the least squares method