Xiufeng Wang

A wavelet bicoherence-based quadratic nonlinearity feature for translational axis condition monitoring.

The translational axis is one of the most important subsystems in modern machine tools, as its degradation may result in the loss of the product qualification and lower the control precision. Condition-based maintenance (CBM) has been considered as one of the advanced maintenance schemes to achieve effective, reliable and cost-effective operation of machine systems, however, current vibration-based maintenance schemes cannot be employed directly in the translational axis system, due to its complex structure and the inefficiency of commonly used condition monitoring features. In this paper, a wavelet bicoherence-based quadratic nonlinearity feature is proposed for translational axis condition monitoring by using the torque signature of the drive servomotor. Firstly, the quadratic nonlinearity of the servomotor torque signature is discussed, and then, a biphase randomization wavelet bicoherence is introduced for its quadratic nonlinear detection. On this basis, a quadratic nonlinearity feature is proposed for condition monitoring of the translational axis. The properties of the proposed quadratic nonlinearity feature are investigated by simulations. Subsequently, this feature is applied to the real-world servomotor torque data collected from the X-axis on a high precision vertical machining centre. All the results show that the performance of the proposed feature is much better than that of original condition monitoring features.

Bearing Fault Detection Based on Empirical Wavelet Transform and Correlated Kurtosis by Acoustic Emission

Rolling bearings are widely used in rotating equipment. Detection of bearing faults is of great importance to guarantee safe operation of mechanical systems. Acoustic emission (AE), as one of the bearing monitoring technologies, is sensitive to weak signals and performs well in detecting incipient faults. Therefore, AE is widely used in monitoring the operating status of rolling bearing. This paper utilizes Empirical Wavelet Transform (EWT) to decompose AE signals into mono-components adaptively followed by calculation of the correlated kurtosis (CK) at certain time intervals of these components. By comparing these CK values, the resonant frequency of the rolling bearing can be determined. Then the fault characteristic frequencies are found by spectrum envelope. Both simulation signal and rolling bearing AE signals are used to verify the effectiveness of the proposed method. The results show that the new method performs well in identifying bearing fault frequency under strong background noise.

Dynamic transmission error analysis for a CNC machine tool based on built-in encoders

The dynamic transmission error of a CNC machine tool affects the manufacturing precision significantly. It may be caused by many factors, such as the geometric error of the transmission parts, the defects on the tooth flank as well as the torsional vibration of running shafts etc. It is very important to locate where the major source is so as to provide a guidance to reduce the transmission error. In this article, the dynamic behavior of the transmission is obtained by using some built-in encoders of a CNC machine tool. Signals obtained from those encoders are the rotating angles of the shafts varying with time. The transmission error can be estimated by using those signals. Some signal processing approaches are established to analyze those signals to locate the error sources. Vibration is one of the most troubling problems encountered in CNC machine tools, this paper also provides an approach for vibration source identification based on the information obtained from the built-in encoders.

Fault diagnosis of rolling element bearing using nonlinear wavelet bicoherence features

Unexpected bearing failures may cause unscheduled downtime and economic losses. It is, therefore, very important to find the faults symptoms of the rolling element bearing components. Vibration signal of fault bearing is nonlinear and non-stationary in nature, which makes the stationary assumed methods not appropriate. In this paper, a biphase randomization wavelet bicoherence method is introduced, which combines benefits of the wavelet transform and the bicoherence analysis. By simultaneously using the amplitude of the continuous wavelet transform and biphase information, this method can eliminate the spurious bicoherence coming from long coherence time waves and non phase coupling waves efficiently. Based on this method, two quadratic nonlinearity features are proposed for fault diagnosis of rolling element bearing. At the same time, the proposed features are applied to the real-world vibration data collected from locomotive roller bearings with faults on inner race, outer race and rollers, respectively. Experiment results demonstrate that the performance of the proposed features is much better than that of some original features.

Flexible time domain averaging technique

Time domain averaging(TDA) is essentially a comb filter, it cannot extract the specified harmonics which may be caused by some faults, such as gear eccentric. Meanwhile, TDA always suffers from period cutting error(PCE) to different extent. Several improved TDA methods have been proposed, however they cannot completely eliminate the waveform reconstruction error caused by PCE. In order to overcome the shortcomings of conventional methods, a flexible time domain averaging(FTDA) technique is established, which adapts to the analyzed signal through adjusting each harmonic of the comb filter. In this technique, the explicit form of FTDA is first constructed by frequency domain sampling. Subsequently, chirp Z-transform(CZT) is employed in the algorithm of FTDA, which can improve the calculating efficiency significantly. Since the signal is reconstructed in the continuous time domain, there is no PCE in the FTDA. To validate the effectiveness of FTDA in the signal de-noising, interpolation and harmonic reconstruction, a simulated multi-components periodic signal that corrupted by noise is processed by FTDA. The simulation results show that the FTDA is capable of recovering the periodic components from the background noise effectively. Moreover, it can improve the signal-to-noise ratio by 7.9 dB compared with conventional ones. Experiments are also carried out on gearbox test rigs with chipped tooth and eccentricity gear, respectively. It is shown that the FTDA can identify the direction and severity of the eccentricity gear, and further enhances the amplitudes of impulses by 35%. The proposed technique not only solves the problem of PCE, but also provides a useful tool for the fault symptom extraction of rotating machinery.

Multiple-axis synchronization evaluation for CNC machine tool based on sensorless measurement

Synchronization among the running shafts in a CNC machine tool is always suffered by the speed variation of the drive motor and the vibration of mechanical transmission components. Synchronization error may decrease the manufacturing accuracy. A novel method is proposed to evaluate the synchronization for the shafts by using the built-in sensors in this article, which can be considered as a sensorless evaluation method. The advantage of this method is that no extra sensors are required. At the same time, some advanced signal processing techniques are applied to analyze those signals to discover more information about the synchronization. Experiments are taken on two CNC grinding machines to study on the synchronization among the running shafts. Some spectrum analysis approaches are employed to evaluate the synchronization among the running shafts. The stability of the running process can be clearly described by using those methods. The synchronization error fluctuation is also obtained at the same time, which is of great help to improve the performance of the machine tool.