1.国际会议信息

·会议名称：IEEE I²MTC 2015(2015 IEEE International Instrumentation and Measurement Technology Conference)

·会议时间：11 – 14 MAY 2015

·会议地点：Pisa, Italy

·会议简介：国际仪表与测量技术会议（International Instrumentation and Measurement Technology Conference，I²MTC）是IEEE仪表与测量学会（IEEE Instrumentation and Measurement Society）主办的仪器仪表领域顶级国际学术会议，会议主题包括：仪表与测量基础、传感器与换能器、物理量测量、测量系统、测量应用、信号与图像处理、监测与故障诊断和工业应用等。其分会SPECIAL SESSION: Advanced Measurement and Data Processing for Engineering System Health Monitoring专场讨论铁路系统的健康监测与故障诊断，涉及复合材料结构健康监测、图像处理技术、电机故障诊断分类等。

·会议交流工作：

Presentation --Compressed Sensing Based Impulsive Feature Identification for Machine Fault Diagnosis.(杜朝辉)

Presentation --Time-frequency Distribution Decomposition with Applications to Detection of Rotor Rub-impact Fault (王岩)

Poster --State recognition of viscoelastic sandwich structures based on permutation entropy and generalized Chebyshev support vector machine (瞿金秀)

2.回校汇报申请信息

·申请汇报时间：2015年5月26日下午3点

·申请汇报地点：曲江科技园机械制造系统工程国家重点实验室A228室

·汇报申请人：杜朝辉、王岩、瞿金秀

3.参会论文信息

·论文标题：Feature Identification with Compressive Measurements for machine fault diagnosis

·作者：杜朝辉，陈雪峰，张晗

·摘要：Machine fault diagnosis collects massive amounts of vibration data about complex mechanical systems. Analyses of the information contained in these data sets have already led to a major challenge. Compressed sensing (CS) theory is a new sampling framework that provides an alternative to the well-known Shannon sampling theory. This theory enables the recovery of sparse or compressible signals from a small set of nonadaptive, linear measurements. However, it is suboptimal to recover full signal from the compressive measurements and then solve feature identification problems through traditional DSP techniques. Thus, a novel mechanical feature identification method is proposed in this paper. Its main advantage is that fault features are extracted directly in the compressive measurement domain without sacrificing accuracy. Meanwhile, a significant reduction in the dimensionality of the measurement data is achieved and the computational efficiency is improved dramatically. Numerical simulations and experiment are performed to prove the reliability and effectiveness of the proposed method.

·论文标题：Time-frequency Distribution Decomposition with Applications to Detection of Rotor Rub-impact Fault

·作者：王岩，张周锁，王诗彬

·摘要：In this paper, we introduce a new data analysis method, called time-frequency distribution decomposition (TFDD), to extract the components of vibration signal one by one for rotor rub-impact fault. When the early rub-impact fault occurs in the rotor system, the vibration signal will present frequency modulation feature because of the periodic rub-impact between the stator and the rotor. Through approximating phase and amplitude functions of each component of vibration signal, the feature of the original signal can be characterized by these functions. Moreover, each component can be analyzed individually. The validity of the method is demonstrated on a real rotor system of a gas turbine with rub-impact fault. The analysis of the application shows that the TFDD is powerful in analysis of nonlinear and nonstationary signals and is an effective tool for the detection of rub-impact faults.

·论文标题：State recognition of viscoelastic sandwich structures based on permutation entropy and generalized Chebyshev support vector machine

·作者：瞿金秀，张周锁，罗雪，李兵，温金鹏

·摘要：Viscoelastic sandwich structures are widely used in mechanical equipment, yet viscoelastic materials always suffer from aging which changes the dynamic characteristics of the structure and affects the whole performance of the equipment. Therefore, state recognition of viscoelastic sandwich structures is very necessary for monitoring structural health states and keeping the equipment running reliably. Considering the high nonlinearity on the dynamic characteristics and the strong non-stationarity of vibration response signals, a novel method based on permutation entropy (PE) and generalized Chebyshev support vector machine (GCSVM) is proposed in this paper. For obtaining more effective state information, redundant second generation wavelet packet transform is firstly used to process the non-stationary vibration response signals, and then PE features are extracted from the resultant wavelet packet coefficients to reveal the changes of the nonlinear dynamic characteristics. Aiming at improving the generalization ability of SVM, based on generalized Chebyshev kernel, the GCSVM is introduced to classify the various structural states. In order to demonstrate the effectiveness of the proposed method, different structural states are created by the accelerated aging experiment of viscoelastic material. The testing results show that the proposed method is effective for state recognition of viscoelastic sandwich structures, which has more strong generalization ability and can achieve higher recognition accuracy than that of the wavelet SVM.

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