Papers by Author: Wen Tao Sui

Abstract: The contour precision has been an important CNC machine accuracy index along with the increase of high precision complex parts NC machining. The error of perpendicularity and the mismatch error of position loop gains among linked axes are regarded as the prime reasons to bring about CNC machine contour error. After analyzing the influence of the error of orthogonal axes perpendicularity to CNC machine contour precision, a compensation approach is set up that the following error is corrected in each sampling period, by introducing a perpendicularity deflection parameter. After analyzing the influence of the mismatch error of position loop gains to contour precision, a cross-coupled control approach based on interpolation dots is developed to enhance the matching degree among all of the linked axes. Finally, the developed compensation approaches are testified on a CNC experiment table. The experimentation results reveal that the developed compensation approaches are effective to enhance contour precision.

Abstract: In order to eliminate the noise in ECG signal and increase the diagnosis efficiency, a method based on morphological filtering and wavelet algorithm is proposed. The morphological filters is used to filter out the baseline interference signal, and the wavelet transform is applied to remove high frequency interference. The experiment proves that the algorithm is effective.

Abstract: A method for canceling noise in mechanical signals was presented, which was based on adaptive filtering and discrete wavelet transform Through multi-scale decomposition of wavelet transform, the isolated noise components was as the input signals of the adaptive filter. Through the simulated signal, it shows that the method can achieve noise reduction of non-stationary signals. The proposed approach for noise reduction has been successfully applied to fault diagnosis of bearing signals.

Abstract: A method of vibration analysis for mechanical fault detection based on adaptive noise canceling(ANC) and envelope analysis was presented. The adaptive filtering was used for noise canceling and feature extraction from vibration signal measured for the detection. The envelope analysis based on analytic wavelet was also used for the fault detection. Experiment shows that the method proposed in this paper is very effective for reducing noise, and for vibration analysis to discriminating the fault types with a high accuracy.

Abstract: This paper presents a fault diagnosis method on roller bearings based on adaptive neuro-fuzzy inference system (ANFIS) in combination with feature selection. The class separability index was used as a feature selection criterion to select pertinent features from data set. An adaptive neural-fuzzy inference system was trained and used as a diagnostic classifier. For comparison purposes, the back propagation neural networks (BPN) method was also investigated. The results indicate that the ANFIS model has potential for fault diagnosis of roller bearings.