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A TimeimageNet Sequence Learning for Remaining Useful Life Estimation of Turbofan Engine in Aircraft Systems

Structural durability & Health Monitoring 2021年第4期15卷 317-334页

作者： S.Kalyani K.Venkata Rao A.Mary Sowjanya department of Computer Science&Systems Engineering Andhra University College of Engineering(A)VisakhapatnamIndia

Internet of Things systems generate a large amount of sensor data that needs to be analyzed for extracting useful insights on the health status of the machine under *** data of all possible states of a system are used for building machine learning *** models are further used to predict the possible downtime for proactive action on the system *** engine data from run to failure is used in the current *** run to failure data includes states like new installation,stable operation,first reported issue,erroneous operation,and final *** the present work,the non-linear multivariate sensor data is used to understand the health status and anomalous *** methodology is based on different sampling sizes to obtain optimum results with great *** time series of each sensor is converted to a 2d image with a specific time *** images would represent the health of a system in higher-dimensional *** created images were fed to Convolutional Neural Network,which includes both time variation and space variation of each sensed *** these created images,a model for estimating the remaining life of the aircraft is ***,the proposed net is also used for predicting the number of engines that would fail in the given time *** current methodology is useful in avoiding the health index generation for predicting the remaining useful life of the industrial *** accuracy in the classification of components is achieved using the Timeimagenet-based approach.

关键词： Multivariate sensor data TimeimageNet Remaining life estimation machine learning 2d image Convolutional Neural Network

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Human Behavior Recognition Method Based on double-Branch deep Convolution Neural Network

Human Behavior Recognition Method Based on Double-Branch Dee...

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第30届中国控制与决策会议

作者： Zhou Zhigang duan Guangxue Lei Huan Zhou Guangbing Wang Nan Yang Wenjie School of Automation Guangdong University of Technology Guangdong Institute of Intelligent Manufacturing School of information Science and Engineering Shenyang University of Technology

Aiming at the poor robustness and the low accuracy of 2 d image recognition, this paper presents a method of human behaviors recognition based on double-branch deep convolution neural network. Firstly, the features of the input image are extracted, and the feature maps are input into the double-branch deep convolution neural network to obtain the joints information of human body and the joints connection information of human body respectively. And we used the Hopcroft-Karp algorithm to optimally match skeletal joints to obtain the human skeletal sequence diagram. The human behaviors are identified by the multi-classification support vector machines. Finally, through the training and testing of standing, walking, running, waving, bending, squatting and sitting on seven kinds of general human behaviors, the experimental results show that the proposed approach has good accuracy and robustness.

关键词： 2d image deep convolution neural network behavior recognition human skeleton multi-classification support vector machines

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