Zhaoli Yan

Defect Detection for Wheel-Bearings with Time-Spectral Kurtosis and Entropy

Wheel-bearings easily acquire defects due to their high-speed operating conditions and constant metal-metal contact, so defect detection is of great importance for railroad safety. The conventional spectral kurtosis (SK) technique provides an optimal bandwidth for envelope demodulation. However, this technique may cause false detections when processing real vibration signals for wheel-bearings, because of sparse interference impulses. In this paper, a novel defect detection method with entropy, time-spectral kurtosis (TSK) and support vector machine (SVM) is proposed. In this method, the possible outliers in the short time Fourier transform (STFT) amplitude series are first estimated and preprocessed with information entropy. Then the method extends the SK technique to the time-domain, and extracts defective frequencies from reconstructed vibration signals by TSK filtering. Finally, the multi-class SVM was applied to classify bearing defects. The effectiveness of the proposed method is illustrated using real wheel-bearing vibration signals. Experimental results show that the proposed method provides a better performance in defect frequency detection and classification than the conventional SK-based envelope demodulation.

A laser-Compton scattering prototype experiment at 100 MeV linac of Shanghai Institute of Applied Physics

As a prototype of the Shanghai Laser Electron Gamma Source in the Shanghai Synchrotron Radiation Facility, an x-ray source based on laser-Compton scattering (LCS) has been installed at the terminal of the 100 MeV linac of the Shanghai Institute of Applied Physics. LCS x-rays are generated by interactions between Q-switched Nd:yttrium aluminum garnet laser pulses [with wavelength of 1064 nm and pulse width of 21 ns (full width at half maximum)] and electron bunches [with energy of 108 MeV and pulse width of 0.95 ns (rms)] at an angle of 42 degrees between laser and electron beam. In order to measure the energy spectrum of LCS x-rays, a Si(Li) detector along the electron beam line axis is positioned at 9.8 m away from a LCS chamber. After background subtraction, the LCS x-ray spectrum with the peak energy of 29.1+/-4.4|(stat)+/-2.1|(syst) keV and the peak width (rms) of 7.8+/-2.8|(stat)+/-0.4|(syst) keV is observed. Normally the 100 MeV linac operates with the electron macropulse charge of 1.0 nC/pulse, and the electron and laser collision repetition rate of 20 Hz. Therefore, the total LCS x-ray flux of (5.2+/-2.0) x 10(2) Hz can be achieved.

A Future Laser Compton Scattering (LCS) γ-Ray Source: SLEGS at SSRF

The Shanghai Synchrotron Radiation Facility (SSRF) is a third-generation synchrotron radiation light source and will come into commission in April 2009. The project Shanghai Laser Electron Gamma Source (SLEGS), which is a high intensity γ-ray beamline based on Laser Compton Scattering (LCS) between relativistic electron bunches and a laser, has been proposed at the SSRF. According to our simulations, the SLEGS is expected to generate a polarized γ-ray beam of up to 22 MeV and 109–10 photons/s if using 3.5 GeV, 200–300 mA relativistic electrons and a 500 W CO2 polarized laser. Here we describe the status and the application prospects of SLEGS and its developed prototype.

Use of Acoustic Emission and Pattern Recognition for Crack Detection of a Large Carbide Anvil

Large-volume cubic high-pressure apparatus is commonly used to produce synthetic diamond. Due to the high pressure, high temperature and alternative stresses in practical production, cracks often occur in the carbide anvil, thereby resulting in significant economic losses or even casualties. Conventional methods are unsuitable for crack detection of the carbide anvil. This paper is concerned with acoustic emission-based crack detection of carbide anvils, regarded as a pattern recognition problem; this is achieved using a microphone, with methods including sound pulse detection, feature extraction, feature optimization and classifier design. Through analyzing the characteristics of background noise, the cracked sound pulses are separated accurately from the originally continuous signal. Subsequently, three different kinds of features including a zero-crossing rate, sound pressure levels, and linear prediction cepstrum coefficients are presented for characterizing the cracked sound pulses. The original high-dimensional features are adaptively optimized using principal component analysis. A hybrid framework of a support vector machine with k nearest neighbors is designed to recognize the cracked sound pulses. Finally, experiments are conducted in a practical diamond workshop to validate the feasibility and efficiency of the proposed method.

Acoustic detection of cracks in the anvil of a large-volume cubic high-pressure apparatus.

A large-volume cubic high-pressure apparatus with three pairs of tungsten carbide anvils is the most popular device for synthetic diamond production. Currently, the consumption of anvils is one of the important costs for the diamond production industry. If one of the anvils is fractured during the production process, the other five anvils in the apparatus may be endangered as a result of a sudden loss of pressure. It is of critical importance to detect and replace cracked anvils before they fracture for reduction of the cost of diamond production and safety. An acoustic detection method is studied in this paper. Two new features, nested power spectrum centroid and modified power spectrum variance, are proposed and combined with linear prediction coefficients to construct a feature vector. A support vector machine model is trained for classification. A sliding time window is proposed for decision-level information fusion. The experiments and analysis show that the recognition rate of anvil cracks is 95%, while the false-alarm rate is as low as 5.8 × 10(-4) during a time window; this false-alarm rate indicates that at most one false alarm occurs every 2 months at a confidence level of 90%. An instrument to monitor anvil cracking was designed based on a digital signal processor and has been running for more than eight months in a diamond production field. In this time, two anvil-crack incidents occurred and were detected by the instrument correctly. In addition, no false alarms occurred.

An Intelligent Monitoring Network for Detection of Cracks in Anvils of High-Press Apparatus

Due to the endurance of alternating high pressure and temperature, the carbide anvils of the high-press apparatus, which are widely used in the synthetic diamond industry, are prone to crack. In this paper, an acoustic method is used to monitor the crack events, and the intelligent monitoring network is proposed to classify the sound samples. The pulse sound signals produced by such cracking are first extracted based on a short-time energy threshold. Then, the signals are processed with the proposed intelligent monitoring network to identify the operation condition of the anvil of the high-pressure apparatus. The monitoring network is an improved convolutional neural network that solves the problems that may occur in practice. The length of pulse sound excited by the crack growth is variable, so a spatial pyramid pooling layer is adopted to solve the variable-length input problem. An adaptive weighted algorithm for loss function is proposed in this method to handle the class imbalance problem. The good performance regarding the accuracy and balance of the proposed intelligent monitoring network is validated through the experiments finally.

An on-line calibration hydrophone with electrostatic actuator

On-line sensitivity calibration of hydrophones is of considerable significance for their practical application. An on-line method using an electrostatic actuator is proposed in this paper for a radially polarized cylindrical piezoelectric hydrophone. The theory of this calibration method is analyzed. A calibration structure with an electrostatic actuator is designed, and is integrated with the hydrophone sensing element. The sensitivity and frequency characteristics of the electrostatic actuator are measured experimentally. The sensitivity of the hydrophone is calibrated and compared with its free-field sensitivity. Uncertainty analysis shows that the expanded uncertainty of the proposed calibration method is about 1.1 dB at a confidence probability of 95%, which meets the uncertainty requirements for hydrophone sensitivity calibration.创新点针对径向极化的圆柱形压电水听器,提出了一种基于静电激励机制的灵敏度在线自校准方法,解决了水听器使用中灵敏度变化,而又不便拆卸进行校准的难题。文中建立了静电激励自校准模型,设计了圆柱形压电陶瓷水听器及其静电激励器,并制作相应的部件集成了自校准水听器样品。通过静电激励器校准实验,得到了静电激励器的灵敏度频率响应特性,并利用水听器的自由场校准实验,验证了基于静电激励机制的在线自校准方法的可行性。实验不确定性分析表明,95%置信概率下,该校准方法的扩展不确定度为1.1dB,满足测量水听器校准的不确定度要求。

Fault diagnosis of railway roller bearing based on vibration analysis and information fusion

Roller bearing is an important mechanical element of railway vehicle. It usually has defects in outer race, inner race or balls due to continuous metal-metal contacts in high-speed operating conditions. For the reason of impurity in lubricant oil, measuring locations and background noise, fault features extracted from vibration signal directly in time-domain or frequency-domain are unstable or uncertain, which may seriously affect the diagnosis accuracy. This paper presents a diagnostic method based on vibration analysis and information fusion. In the method, the signal analysis methods such as wavelet packet are processed to depress background noise of collected vibration signals. Considering that vibration energy at characteristic rotational frequency may increase with defect on a particular bearing element, they are extracted as fault feature vectors, which are used to train support vector data description (SVDD) classifiers. To reduce recognition uncertainty of single fault classifier, each classifier...