Theoretical and experimental investigation of the effects of an irregular-pitch cutter on vibration in face-milling

Hui Li,Haiqi Zheng,Liwei Tang 대한기계학회 2010 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.24 No.11

In the present work, cutting-force models of single-tooth and multi-teeth face-milling cutters were set up. Based on a spectrum analysis of cutting force, the vibration mechanism of a face-milling cutter of irregular pitch was investigated theoretically. The single-objective function and constraint conditions were derived. A general-purpose irregular-pitch face-milling cutter subsequently was designed and tested, and its performance was compared with that of a regular-pitch cutter. The experimental results showed that the irregular-pitch face-milling cutter not only reduces vibration and noise but also enhances surface-finish quality.

Wear Detection in Gear System Using Hilbert-Huang Transform

Li, Hui,Zhang, Yuping,Zheng, Haiqi The Korean Society of Mechanical Engineers 2006 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.20 No.11

Fourier methods are not generally an appropriate approach in the investigation of faults signals with transient components. This work presents the application of a new signal processing technique, the Hilbert-Huang transform and its marginal spectrum, in analysis of vibration signals and faults diagnosis of gear. The Empirical mode decomposition (EMD), Hilbert-Huang transform (HHT) and marginal spectrum are introduced. Firstly, the vibration signals are separated into several intrinsic mode functions (IMFs) using EMD. Then the marginal spectrum of each IMF can be obtained. According to the marginal spectrum, the wear fault of the gear can be detected and faults patterns can be identified. The results show that the proposed method may provide not only an increase in the spectral resolution but also reliability for the faults diagnosis of the gear.

Wear Detection in Gear System Using Hilbert-Huang Transform

Hui Li,Yuping Zhang,Haiqi Zheng 대한기계학회 2006 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.20 No.11

Fourier methods are not generally an appropriate approach in the investigation of faults signals with transient components. This work presents the application of a new signal processing technique, the Hilbert-Huang transform and its marginal spectrum, in analysis of vibration signals and faults diagnosis of gear. The Empirical mode decomposition (EMD), Hilbert-Huang transform (HHT) and marginal spectrum are introduced. Firstly, the vibration signals are separated into several intrinsic mode functions (IMFs) using EMD. Then the marginal spectrum of each IMF can be obtained. According to the marginal spectrum, the wear fault of the gear can be detected and faults patterns can be identified. The results show that the proposed method may provide not only an increase in the spectral resolution but also reliability for the faults diagnosis of the gear.

Dynamics modeling and simulation of a new nine-bar press with hybrid-driven mechanism

Hui Li,Yuping Zhang,Haiqi Zheng 대한기계학회 2008 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.22 No.12

A novel hybrid-driven mechanical press for precision drawing is presented. This new press is composed of a ninebar linkage which has two degrees of freedom determined by inputs from a dc constant speed motor and a dc servomotor. Therefore, the generalized coordinates are the angular displacement of two cranks. The kinetic energy, potential energy and generalized torques are analyzed. According to the equivalent circuit of the dc motor and the brushless servomotor, their dynamical model and position negative feedback model are developed separately. Then, a dynamical model for the hybrid-driven press is developed by using Lagrange’s formulation. The dynamical equation is then transformed into a system of first order equations. Six first order differential equations are obtained in the state variables. In the end, the fourth fourth order Runge-Kutta method, an explicit method, is chosen as the integration technique of computer simulation. Two motors’ current, two cranks’ position and two cranks’ angular velocity are treated as unknowns and the time response of the hybrid-driven press is obtained by integrating the system of first order equations through time.

Bearing fault diagnosis based on amplitude and phase map of Hermitian wavelet transform

Hui Li,Lihui Fu,Haiqi Zheng 대한기계학회 2011 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.25 No.11

The rolling element bearing characteristic frequencies contain very little energy and are usually overwhelmed by noise and higher level of structural vibrations. The continuous wavelet transform enables one to look at the evolution in the time scale joint representation plane. This makes it very suitable for the detection of singularity generated by localized defects in a mechanical system. However, most applications of the continuous wavelet transform have widely focused on the use of the Morlet wavelet transform. The complex Hermitian wavelet is constructed based on the first and the second derivatives of the Gaussian function to detect signal singularities. The Fourier spectrum of Hermitian wavelet is real, which the Fourier spectrum has no complex phase and the Hermitian wavelet does not affect the phase of a signal in complex domain. This gives the desirable ability to detect the singularity characteristic of a signal precisely. In this study, the Hermitian wavelet amplitude and phase map are used in conjunction to detect and diagnose the bearing fault. The Hermitian wavelet amplitude and phase map are found to show distinctive signatures in the presence of bearing inner race or outer race damage. The simulative and experimental results show that the Hermitian wavelet amplitude and phase map can extract the transients from strong noise signals and can effectively diagnose bearing faults.

Hilbert-Huang transform and marginal spectrum for detection and diagnosis of localized defects in roller bearings

Hui Li,Yuping Zhang,Haiqi Zheng 대한기계학회 2009 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.23 No.2

This work presents the application of a new signal processing technique, the Hilbert-Huang transform and its marginal spectrum, in analysis of vibration signals and fault diagnosis of roller bearings. The empirical mode decomposition (EMD), Hilbert-Huang transform (HHT) and marginal spectrum are introduced. First, the vibration signals are separated into several intrinsic mode functions (IMFs) by using EMD. Then the marginal spectrum of each IMF can be obtained. According to the marginal spectrum, the localized fault in a roller bearing can be detected and fault patterns can be identified. The experimental results show that the proposed method may provide not only an increase in the spectral resolution but also reliability for the fault detection and diagnosis of roller bearings.

Gear fault detection and diagnosis under speed-up condition based on order cepstrum and radial basis function neural network

Hui Li,Yuping Zhang,Haiqi Zheng 대한기계학회 2009 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.23 No.10

Varying speed machinery condition detection and fault diagnosis are more difficult due to non-stationary machine dynamics and vibration. Therefore, most conventional signal processing methods based on time invariant carried out in constant time interval are frequently unable to provide meaningful results. In this paper, a study is presented to apply order cepstrum and radial basis function (RBF) artificial neural network (ANN) for gear fault detection during speedup process. This method combines computed order tracking, cepstrum analysis with ANN. First, the vibration signal during speed-up process of the gearbox is sampled at constant time increments and then is re-sampled at constant angle increments. Second, the re-sampled signals are processed by cepstrum analysis. The order cepstrum with normal, wear and crack fault are processed for feature extracting. In the end, the extracted features are used as inputs to RBF for recognition. The RBF is trained with a subset of the experimental data for known machine conditions. The ANN is tested by using the remaining set of data. The procedure is illustrated with the experimental vibration data of a gearbox. The results show the effectiveness of order cepstrum and RBF in detection and diagnosis of the gear condition.

Bearing fault detection and diagnosis based on order tracking and Teager-Huang transform

Hui Li,Yuping Zhang,Haiqi Zheng 대한기계학회 2010 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.24 No.3

The vibration signal of the run-up or run-down process is more complex than that of the stationary process. A novel approach to fault diagnosis of roller bearing under run-up condition based on order tracking and Teager-Huang transform (THT) is presented. This method is based on order tracking, empirical mode decomposition (EMD) and Teager Kaiser energy operator (TKEO) technique. The nonstationary vibration signals are transformed from the time domain transient signal to angle domain stationary one using order tracking. EMD can adaptively decompose the vibration signal into a series of zero mean amplitude modulation-frequency modulation (AM-FM)intrinsic mode functions (IMFs). TKEO can track the instantaneous amplitude and instantaneous frequency of the AM-FM component at any instant. Experimental examples are conducted to evaluate the effectiveness of the proposed approach. The experimental results provide strong evidence that the performance of the Teager-Huang transform approach is better to that of the Hilbert-Huang transform approach for bearing fault detection and diagnosis. The Teager-Huang transform has better resolution than that of Hilbert-Huang transform. Teager-Huang transform can effectively diagnose the faults of the bearing, thus providing a viable processing tool for gearbox defect monitoring.