Adaptive Channel Weighted CNN With Multisensor Fusion for Condition Monitoring of Helicopter Transmission System

Li, Tianfu; Zhao, Zhibin; Sun, Chuang; Yan, Ruqiang; Chen, Xuefeng

doi:10.1109/jsen.2020.2980596

Cited by 50 publications

(9 citation statements)

References 34 publications

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“…Multiple sensors can provide more recognition ability. More complex activities can be identified by processing and analyzing the data collected by various sensors [27]. With the progress of science and technology and the increase in demand, it is urgent to identify complex activities through the data collected by a variety of sensors.…”

Section: Multisensor Fusion Technologymentioning

confidence: 99%

Construction of Leisure Physical Education Teaching Model Based on Multisensor Fusion

Zhang

2022

Mobile Information Systems

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Physical education is an important part of course teaching. Doing well in the teaching of this course can not only improve students’ quality, but also promote students’ healthy and all-round development. With the rapid development of China’s economy, leisure sports, as an important way of leisure life, are increasingly favored by people. However, the related leisure sports service talents and industrial management talents are in short supply, which has restricted the development of the leisure sports industry in breadth and depth. The demand in the leisure sports market for advanced applied talents has put forward the call of the times for the direction of running sports colleges and universities, which is also the significance of the study. To sum up, this paper builds a leisure physical education teaching model based on multisensor fusion. Firstly, it summarizes leisure sports and multisensor fusion technology and then introduces how to add multisensor technology to physical education teaching. Finally, some experiments are carried out using the data set published by the University of California. In the model training, the super parameters of the convolutional neural network with two to eight layers are tested. The experimental results verify the effectiveness of the model; it achieves an accuracy of 86% when the number of convolutional layers is 6. The connotation of sports leisure and entertainment and the content of talent training mode is clarified, which lays a theoretical foundation for the following research. Among them, the construction of a curriculum system is the core part of talent training measures, and it is also an important part of the construction of leisure sports specialty.

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Section: Multisensor Fusion Technologymentioning

confidence: 99%

Construction of Leisure Physical Education Teaching Model Based on Multisensor Fusion

Zhang

2022

Mobile Information Systems

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“…Convolutional Neural Network has proven very powerful in many recognition and classification tasks [26][27][28]. It has also shown the power to address sequential data in task of natural language processing [29][30][31].…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Multi-Scale Convolutional Gated Recurrent Unit Networks for Tool Wear Prediction in Smart Manufacturing

Miao

Zhao

et al. 2021

Chin. J. Mech. Eng.

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As an integrated application of modern information technologies and artificial intelligence, Prognostic and Health Management (PHM) is important for machine health monitoring. Prediction of tool wear is one of the symbolic applications of PHM technology in modern manufacturing systems and industry. In this paper, a multi-scale Convolutional Gated Recurrent Unit network (MCGRU) is proposed to address raw sensory data for tool wear prediction. At the bottom of MCGRU, six parallel and independent branches with different kernel sizes are designed to form a multi-scale convolutional neural network, which augments the adaptability to features of different time scales. These features of different scales extracted from raw data are then fed into a Deep Gated Recurrent Unit network to capture long-term dependencies and learn significant representations. At the top of the MCGRU, a fully connected layer and a regression layer are built for cutting tool wear prediction. Two case studies are performed to verify the capability and effectiveness of the proposed MCGRU network and results show that MCGRU outperforms several state-of-the-art baseline models.

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“…On the other hand, there is redundant information between multisensor signals, and the accuracy and effect of equipment fault diagnosis can be improved through effective information fusion. In this regard, Xie et al [25] used principal component analysis (PCA) to fuse and convert multisensor signal features into RGB images, and then, the image samples are input into a convolutional neural network (CNN) with residuals for further extraction of deep features; Li et al [26] proposed an adaptive channel weighted neural network to study the importance of different sensor signals in the feature fusion method while maximizing the mining of the deep fault feature information of each sensor and finally realized the condition monitoring of the gearbox transmission system and the helicopter transmission system; Cao and Yunusa-Kaltungo [27] proposed a gearbox fault classification framework for the automatic fusion of multisensor data, generating features through coherent composite spectros-copy (CCS) and using PCA for data dimensionality reduction. The final diagnosis results were obtained from artificial neural network training feature samples.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis Algorithm of Gearbox Based on NAPSO-VMD Self-Adaptive Noise Reduction and Dual-Sensor Feature Fusion

Liu

Kang

Guo

et al. 2022

Wireless Communications and Mobile Computing

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Condition monitoring plays a very important role in equipment fault diagnosis technology. However, existing monitoring methods often collect equipment fault signals from a single dimension, resulting in a major lack of fault information. To improve the problem, we built a gearbox preset fault test bench and constructed a dual-sensor acquisition system to realize the multiple dimensions of vibration signal acquisition in the horizontal and vertical directions of the gearbox. At the same time, given the poor adaptability of most current signal preprocessing methods, the improved nonlinear adaptive inertial weight particle swarm optimization algorithm (NAPSO) and variational modal decomposition (VMD) are combined to optimize the key parameters in VMD with the maximum correlation kurtosis convolution (MCKD) as the fitness function. Further, after extracting fault features from the intrinsic mode functions (IMFS) decomposed by VMD, the single-layer sparse autoencoder network (SAE) and the double-layer stacked sparse autoencoder network (SSAE) with different structures are used to realize an effective fusion of multidimensional information and deep feature extraction. Finally, the hybrid fault diagnosis of gearboxes is realized by using the random forest algorithm (RF) as the classifier. The experimental results show that the accuracy of the method proposed in this paper can reach 96.0%, and the accuracy can be improved by 3.0% and 4.0%, respectively, when compared with a single horizontal or vertical sensor signal input.

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Adaptive Channel Weighted CNN With Multisensor Fusion for Condition Monitoring of Helicopter Transmission System

Cited by 50 publications

References 34 publications

Construction of Leisure Physical Education Teaching Model Based on Multisensor Fusion

Construction of Leisure Physical Education Teaching Model Based on Multisensor Fusion

Multi-Scale Convolutional Gated Recurrent Unit Networks for Tool Wear Prediction in Smart Manufacturing

Fault Diagnosis Algorithm of Gearbox Based on NAPSO-VMD Self-Adaptive Noise Reduction and Dual-Sensor Feature Fusion

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