Showing papers by "Yuan Kang published in 2012"

The screw-type capillary module with restriction length being adjustable

Abstract: The calibration screw can be used to adjust the effective length for restriction of the screw-type capillary, capillary screw and curved surface are manufactured on both ends of restriction stud, respectively. Another surface is manufactured on the end of calibration screw, both surfaces are matched for the contact transmission between calibration screw and restriction stud. Move calibration screw to change the effective length of capillary screw which determines the restriction of capillary. The motion can be transmitted from calibration screw to restriction stud due to the contact between both surfaces that make the independence between both screws such that self-alignment of restriction screw can be attained and accuracy of restriction hole that contain restriction stud cannot be influenced by the assembly clearance of calibration screw.

The Gearbox Fault Detection of Machine Center by Using Time Frequency Order Spectrum

Abstract: Previously, for the case of fixed or steady state rotation rate, spectrum analysis can be used to extract the frequency features as the basis for the gearbox fault detection of machine center. However, the gearbox of machine center for increasingly instant speed variations mostly generate non-stationary signals, and the signal features must be averaged with analysis time which makes it difficult to identify the causes of failures. This study proposes a time frequency order spectrum method combining the short-time Fourier transform (STFT) and speed frequency order method to capture the order features of non-stationary signals. Such signal features do not change with speed, and are thus effective in identifying faults in mechanical components under non-stationary conditions. In this study, back propagation neural networks (BPNN) and time frequency order spectrum methods were used to verify faults diagnosis and obtained superior diagnosis results in non-stationary signals of gear-rotor systems in machine center.

Machinery Fault Detection Using the Wavelet Order Spectrum

Abstract: The non-stationary signal features are averaged in correspondence with the length of analysis time, thus making it impossible to highlight the signal characteristics and caused the difficulties in identifying or diagnosing faults. In this paper, the wavelet order spectrum method using a combination of wavelet transform (WT) and speed frequency ordering. The feature order does not change with variations in speed, thus can effectively identify non-stationary faults in mechanical equipment. In addition, principal components analysis (PCA) is used to extract the main features of the wavelet order spectrum and reduce the volume of data. This is combined with self-organizing maps (SOM) to devise an artificial intelligence method for fault diagnosis in non-stationary states.