1、汇报安排

题目：参加ISFA2016国际会议总结报告会

时间：2016年8月14日14:30-16:30

地点：曲江校区西五楼A228会议室

报告人：博1213班-胡兵兵、博1402班-丁宝庆，博1501班-杨博渊，博1501班-马猛

2、参加国际会议信息

会议名称：International Symposium on Flexible Automation ISFA2016

会议时间：2016.8.1—2016.8.3

会议地点：Cleveland, Ohio, USA

会议简介：The ISFA conference was initiated in 1986 under co-sponsorship of the American Society of Mechanical Engineers (ASME) and the Institute of Systems, Control and Information Engineers (ISCIE) in Japan. Since then, the Symposium has been held every two years, and the title "Flexible Automation" was selected as a general term to describe automation technologies that are essential to meeting the increasing requirements of modern manufacturing and other related fields, such as dynamical systems, robotics, logistics, biomedical systems, and health care systems. While many of these requirements such as flexibility, artificial intelligence, mechatronics, 3D design and modeling, lead time reduction, and lean manufacturing, were identified over more than three decades ago, they still pose challenges and continue to motivate research in the field. In addition, newly emerged ideas and technologies, such as unmanned vehicle control, Internet of Things (IoT), cloud computing and manufacturing, additive manufacturing, image processing and pattern recognition, cyber-physical systems, security, and environmental sustainability present both new challenges and opportunities that broaden the scope of research and impact the continued advancement of flexible automation.

会议在ASME和ISCIE的资助下于1986开始举办，每两年举办一次，具有较高的国际声誉。2016年的主题是“灵活化制造：智能与连接”，旨在激励在节能、智能自动化和云计算方面的研究。

会议交流工作：

Presentation: Bearing Signature Extraction Using an Improved Adaptive Multiscale Stochastic Resonance Method. 汇报人-胡兵兵；

Presentation: SVD-Based Dictionary Learning for Bearing Fault Diagnosis. 汇报人-丁宝庆

Presentation: Sparse Time-Frequency Representation for Incipient Fault Diagnosis of Wind Turbine Drive Train. 汇报人-杨博渊

Presentation: Bearing Degradation Assessment Based on Weibull Distribution and Deep Belief Network. 汇报人-马猛

3、参会论文信息：

Titile:Bearing Signature Extraction Using an Improved Adaptive Multiscale Stochastic Resonance Method.

Author: 胡兵兵，李兵，丁锋

Abstract: To improve the accuracy of bearing fault diagnosis, it is very important to extract weak features effectively from a noise signal. Mulitscale noise tuning stochastic resonance (MSTSR) has proved to be an extremely effective way for weak signal detection. However, the original MSTSR method, which is based on signal-to-noise ratio (SNR) index, requires some prior knowledge to select two key parameters (the cut-off wavelet decomposition level and the tuning parameter). To solve this problem, we present a modified kurtosis index for the MSTSR method in fault diagnosis of rolling element bearings. The proposed index can not only combine the advantages of kurtosis index and zero-crossing ratio but also enhance or weaken the impact degree of the zero-crossing ratio by correlation coefficient. Meanwhile, the adaptive approach gets rid of the requirement of any prior knowledge. The simulation and experimental results verify the proposed scheme is suitable for bearing fault diagnosis.

Title: SVD-Based Dictionary Learning for Bearing Fault Diagnosis

Author:丁宝庆，陈雪峰*，张钰，严如强

Abstract:Bearing fault diagnosis is of great importance to maintain the high reliability and long-term safe operation of rotating machinery. The key factor of the processing result is the proper selection of basis, which is also called dictionary in sparse representation. In this paper, a data-driven method for designing dictionaries called singular value decomposition (SVD)-based dictionary learning method is proposed. Combining the K-SVD scheme and the idea of SVD based on hankel-matrix, the proposed method can extract the inherent components of signals, thus realizing the goal of training dictionary. The proposed method is applied to simulated signal and practical application in fault diagnosis of bearings. The processing result demonstrates that the proposed method outperforms the K-SVD method in learning dictionaries from vibration signal of rotating machine.

Title:Sparse Time-Frequency Representation for Incipient Fault Diagnosis of Wind Turbine Drive Train

Author:杨博渊，刘若楠，陈雪峰

Abstract:Due to the dramatic growth of total installation and individual capacity make the failures of wind turbines costly or even unacceptable. Therefore, wind turbine fault diagnosis, which is considered as a useful tool to ensuring the safety of wind turbines and reducing costly system maintenances, is attracting increasing attention. In this paper, a novel fault diagnosis for wind turbine gearbox based on basis pursuit denoising (BPDN) and the union of redundant dictionary is proposed. The union of redundant dictionary is constructed based on the underlying prior information of vibrations signal with multicomponent coupling effect. Within the frame work of BPDN, sparse coefficient and corresponding time-frequency atoms can be obtained. By time-frequency representation of the reconstructed signal, fault information can be displayed. Finally, an engineering application of a wind turbine gearbox is used to verify the effectiveness of the proposed method.

Title:Bearing Degradation Assessment Based on Weibull Distribution and Deep Belief Network.

Author:马猛，陈雪峰，王诗彬，刘妍萌，李巍华

Abstract:The most important task of Prognostic and Health Management (PHM) in industrial company is to prevent catastrophic failures and reduce maintenance costs, where degradation assessment plays a significant role in schedule required preventive actions. In this paper, to assess the bearing’s performance degradation, a new method which is based on the Weibull distribution and deep belief network is proposed. A health state and five degradation states are determined according to the fitted vibration feature by Weibull distribution, which is used to avoid areas of fluctuation of the statistical parameter. Deep belief network has the ability to model nonlinear time series, so it is used to classify the different states of the bearing. Finally, the experimental cases show that the proposed method has a good performance in degradation assessment.

欢迎各位感兴趣的同学届时前来交流。