汇报时间：2019年11月5日（星期二）20:00

汇报地点：创新港 巨构2 5F-036

汇报人：沈智宪、李天福、武靖耀

国际会议信息

会议名称：8th International Conference on Through-Life Engineering Service – TESConf 2019

会议时间：October 27 – 29, 2019

会议地点：the Tinkham Veale University Center at the CWRU campus in Cleveland, Ohio

会议简介：The International Conference on Through-life Engineering Services (TESConf) is in its eighth year and it has grown interest of both academia and industry. High-value products are technology intensive, expensive, and reliability-critical, requiring the services (e.g., maintenance, repair, and overhaul) throughout the life cycle. The conference will bring experts and researchers in this area together to exchange ideas in delivering solutions to provide world-class capability to enable industry to produce high-value products with outstanding availability, predictability, and reliability with the lowest life cycle cost.

参会论文信息

Titile：Dynamic modeling of planetary gear set with tooth surface wear

Author：Zhixian Shen, Baijie Qiao, Laihao Yang, Wei Luo, Ruqiang Yan, Xuefeng Chen*

Abstract：Wear commonly occurs in planetary gear transmission systems. Fundamentally, the tooth wear could cause tooth profile deviation, which would increase the vibration and noise of gearbox. In order to monitor and forecast the wear condition of gears via vibration-based methods, it is necessary to establish the dynamics model of a planetary gear set with tooth surface wear, which can provide a prior about the vibration characteristics of gear wear. In this study, a purely torsional dynamics model of a planetary gear set with tooth surface wear is proposed to analyze the fault mechanism of tooth surface wear. The tooth surface wear is incorporated into the dynamics model through unloaded static transmission error (USTE) and time-varying mesh stiffness (TVMS), which are evaluated by Archard’s wear equation. Subsequently, the vibration responses of the planetary gear set with tooth surface wear are analyzed. It is revealed that tooth wear would change the vibration responses in both time- and frequency-domain and the condition indicators present different trends.

参会论文信息

Titile：Multi-scale CNN for Multi-sensor Feature Fusion in Helical Gear Fault Detection

Author：Tianfu Li, Zhibin Zhao, Chuang Sun, Ruqiang Yan, Xuefeng Chen*

Abstract：Studies on fault detection and diagnosis of helical gears under high speed and heavy load conditions are quite limited comparing with spur gears under light load and low speed conditions. It is a fact that the working conditions of helical gears are very complicated, thus multiple sensors mounted on its different locations can provide complementary information on the fault detection and diagnosis. On this basis, a multi-scale multi-sensor feature fusion convolutional neural network (MSMFCNN) is derived, and it operates information fusion on both data level and feature level. MSMFCNN contains three parts, including a conventional one-dimensional CNN part, a multi-scale multi-sensor feature fusion part, and an output part. To better understand this network, the theoretical foundation of MSMFCNN is given. Moreover, in order to demonstrate the effectiveness of the proposed method, experiments are carried out on a parallel shaft gearbox test rig on which multiple acceleration sensors are mounted for data acquisition. The experimental results show that MSMFCNN can fully utilize the multi-sensor information and get a high accuracy on helical gear fault detection and can converge faster than the standard CNN.

参会论文信息

Titile：Ss-InfoGAN for Class-Imbalance Classification of Bearing Faults

Author：Jingyao Wu, Zhibin Zhao, Chuang Sun, Ruqiang Yan*, Xuefeng Chen

Abstract：As the core part of the Prognostic and Health Management (PHM) of major equipment such as high-speed trains and aero engines, bearing fault classification have been the research priorities in the field. Although convolutional neural network (CNN) has shown good results in this type of task, the real application with limited training data makes CNN have a big gap between the actual application and the expected effect. Therefore, bearing faults classification with class-imbalance is a very practical work. In this paper, semi-supervised information maximizing generative adversarial network (ss-InfoGAN), which uses adversarial structure to generate samples of the minority, is introduced to augment data to solve class imbalance problem. In addition, the latent codes, the inputs of generator, are decomposed into three parts with three additional networks, respectively, at the start of generator. Meanwhile, the 50% precision threshold is proposed during the training stage of discriminator to make a trade-off between computing resources and theoretical foundations and facilitate the network converge. Bearing fault experiments are conducted to investigate the effectiveness of the presented network. The result shows classification accuracy is improved by 40% by the ss-InfoGAN compared to the traditional CNN for the case of extremely class-imbalance condition.