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发布时间:2020-06-30 点击数:

汇 报 人:杨文展、罗欣
会议名称:IEEE International Instrumentation and Measurement Technology Conference 2020
会议时间:May 25 – June 25, 2020
会议地点:Valamar Lacroma, Dubrovnik, Croatia [Online]
会议简介:The IEEE I2MTC – International Instrumentation and Measurement Technology Conference – is the flagship conference of the IEEE Instrumentation and Measurement Society, and is dedicated to advances in measurement methodologies, measurement systems, instrumentation, and sensors in all areas of science and technology. These features make I2MTC a unique event and one of the most important conferences in the field of instrumentation and measurement. IEEE I2MTC is proposed as a catalyst to promote interactions between industry and academia. A wide spectrum of research results will be presented, with potential practical applications in current industrial technology, as well as industry and application driven developments.
Title:State recognition of bolted structures based on quasi-analytic wavelet packet transform and generalized Gegenbauer support vector machine
Author:Wenzhan Yang, Zhousuo Zhang, Yujie Hong
Abstract:Monitoring the looseness of bolted structures is important to ensure the reliability and integrity of engineering structures. In the past decades, various methods have been developed to characterize looseness states of bolted structures. However, in the long-term storage, transportation, and usage process of bolted structures, especially under random excitation, the vibration-based method for monitoring is the most applicative technology with merits such as low cost and comprehensive operability. To fulfill this task accurately and automatically, a novel looseness state recognition approach of bolted structures based on multi-domain sensitive features derived from quasi-analytic wavelet packet transform (QAWPT) and generalized Gegenbauer support vector machine (GGSVM) is proposed in this paper. To extract effective looseness feature information, the measured non-stationary and nonlinear vibration response signals are processed by QAWPT, and then multi-domain sensitive features are extracted from obtained frequency band signals. For accurate and automatic state recognition, generalized Gegenbauer kernel is introduced, and then multi-class GGSVM is developed to recognize looseness states. In order to validate the effectiveness of the proposed method, a typical bolted beam structure is designed and fabricated, and various looseness states are implemented. The testing results show that the proposed method is effective for looseness state recognition of bolted structures. In addition, QAWPT owns superiority in processing vibration response signals compared with classical WPT, and GGSVM has higher recognition accuracy and better generalization ability than that of kernel SVM with radial basis function.
Title:A Kernel-Based Nonlinear Blind Source Separation Algorithm with Reference and Its Application in Satellite Micro-vibration System
Author:Xin Luo, Zhousuo Zhang, Teng Gong, Yuheng Yang, Yongjie Li
Abstract:In this paper, a kernel-based nonlinear blind source separation algorithm with reference information is proposed to identify the harmonic source signals in satellite micro-vibration system. The kernel feature space with reduced dimension constructed by the proposed algorithm can transform the nonlinear blind source separation in the input space into linear blind source separation. In the linear blind source separation phase, aiming at the weak non-Gaussian characteristic of micro-vibration harmonic sources, a new linear blind source separation objective function with reference information is proposed to ensure the accuracy of source identification. The effective estimated signals of the sources are selected from the linear separated signals according to the spectrum correlation coefficient index. The effectiveness of the proposed algorithm is verified by the satellite cabin structure experiment.

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