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IEEE International Conference on Prognostics and Health Management (ICPHM)

作者： Shi, Xiaoyu Cheng, Yuhua Zhang, Bo Zhang, Haonan Elect Sci & Technol China Sch Automat Engn Chengdu 611731 Peoples R China

ISBN: (纸本)9781728162867

Bearings are necessary rotating machinery and plays an important role in the modern industrial systems for its safety and reliability. Timely fault diagnosis of bearings can reduce the probability of failure, thereby reducing economic losses and casualties. Many signal processing methods for fault feature extraction and fault recognition have been applied by scholars and engineers. Although numerous current methods identify and diagnose bearing faults correctly, they rely on a lot of existing information and experts experience, so it is not possible to establish a one-to-one correspondence between the original signal and the failure mode. Furthermore, the structure and parameters of the artificial intelligent neural network need to be optimized through experts and current knowledge. alexnet neural network improves the learning ability and provides inspiration and direction for the above problem. The ensemble empirical mode decomposition (EEMD) solve the problem of mode mixing. The wavelet transform could impose the time and frequency features. Combing the prior of EEMD with continue wavelet transform, an adaptive fault feature model has been constructed that can directly provide the information to corresponding with the fault classified neural network. In this approach, fault signals are enhanced by extracting envelope decomposition and frequency signals. Numerous bearing data which containing different fault signals are used to verify the effectiveness and accuracy of the proposed method. The diagnosis results show that the novel alexnet neural network classifies bearings fault with high accuracy and robustness under complexity environment.

关键词： Fault diagnosis Rotating machinery Feature model alexnet neural network

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Weighted Recursive Graph Color Coding for Enhanced Load Identification

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第5期15卷 1118-1124页

作者： Zhang, Li Ai, Hengtao Liu, Yuhang Li, Shiqing Zhang, Tao Henan Polytech Univ Sch Elect Engn & Automat Jiaozuo Peoples R China Henan Key Lab Intelligent Detect & Control Coal M Jiaozuo Peoples R China Henan Int Joint Lab Direct Drive & Control Intell Jiaozuo Peoples R China

In the pursuit of high-precision load identification, traditional methodologies grapple with significant drawbacks, including low recognition rates, intricate signature construction, and narrow applicability. This study introduces a novel approach employing weighted recursive graph (WRG) color coding to surmount these challenges. Power consumption data, procured from advanced load monitoring devices, undergo extraction of single-cycle currents, which are then subjected to dimensional reduction via Piece-wise Aggregate Approximation (PAA). In a transformative step, these currents are encoded into load signatures through the recursive graph time series methodology, culminating in the generation of WRG images. An alexnet neural network model is engaged to distil and assimilate the distinctive features of the WRG images. The simulation results indicate that the identification rate can exceed 97%. Additionally, an experimental platform was set up to verify the method proposed in this paper, and the results show that the actual identification rate can reach over 96%. Both the simulation results and experiments fully demonstrate that the proposed identification method has a high accuracy. This method not only sets a new standard in non-intrusive load identification but also enhances the generalization of load signature applicability across diverse scenarios.

关键词： Non-Intrusive Load Monitoring (NILM) Weighted Recurrence Graph (WRG) color coding alexnet neural network load signature

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An Improved Bearing Fault Diagnosis Scheme Based on Hierarchical Fuzzy Entropy and alexnet network

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IEEE ACCESS 2021年 9卷 61710-61720页

作者： Shi, Xiaoyu Qiu, Gen Yin, Chun Huang, Xuegang Chen, Kai Cheng, Yuhua Zhong, Shouming Univ Elect Sci & Technol China Sch Automat Engn Chengdu 611731 Peoples R China China Aerodynam Res & Dev Ctr Hyperveloc Aerodynam Inst Mianyang 621000 Sichuan Peoples R China Univ Elect Sci & Technol China Sch Math Sci Chengdu 611731 Peoples R China

Bearings are important part of aerospace equipments. Therefore, bearings fault diagnosis and fault detection is necessary for the safe operation. Generally, the bearing are core component and runs in a complex and heavy background noise environment. Thence, the bearing signals will be overwhelmed by noise. At present, the diagnosis methods of bearings are based on the experience of experts which costs a lot of labor and time-consuming. Consequently, to address these inadequacies, a novel method based on variational mode decomposition (VMD) algorithms and alexnet neural network has been presented. Firstly, it decomposes the nonstationary bearing signals into intrinsic mode functions adaptively. However, background noise will seriously affect the number of signal decompositions and reduce accuracy even over-decomposition. Thence, this paper proposed the hierarchical fuzzy entropy method to adaptively extract weak fault characteristics of bearings. Then alexnet neural network has been utilized to learn the relationship of fault features and bearings health conditions. The architecture of alexnet diagnosis model is convenient and efficient. Finally, two tentative dataset are adopted to verify the effectiveness and feasibility of the method proposed in this article. The results show that the presented method has a higher fault diagnosis accuracy rate than traditional convolutional neural networks.

关键词： Entropy Fault diagnosis Feature extraction neural networks Frequency estimation Data mining Time-domain analysis Hierarchical fuzzy entropy alexnet neural network feature model

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Rolling bearing fault classification method based on data simulation collaborative drive and transfer learning 3

Rolling bearing fault classification method based on data si...

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2023 IEEE 3rd International Conference on Data Science and Computer Application, ICDSCA 2023

作者： Xiaoyu, Tang Zihan, Wan Yiyang, Shen Nanjing Tech University Nanjing China

ISBN: (纸本)9798350341546

The simulation data is complete, but the measured data is unbalanced or missing, which makes it difficult to apply the data-driven model. In addition, there are many adjustable hyperparameters and artificially adjusted random factors in the data-driven model, which leads to the final result of the model. Insufficient precision. Therefore, this paper proposes a rolling bearing fault image recognition method based on hyperparameter optimization and transfer ***, build a simulation model to generate some samples, and then screen the network through noise resistance, select a network with strong robustness, and then input the network with strong noise resistance with simulation data. In this paper, alexnet depth volume is selected. A neural network is integrated and the model is tuned through hyperparameter optimization techniques. Finally, this paper constructs a model by migrating the evaluation model from the simulation domain to the measured data domain through feature projection to make up for the scarcity of samples. It is verified by experiments that the actual measurement data of rolling bearing faults from Case Western Reserve University is used, and the overall accuracy of the model is improved by means of the hyperparameter optimization method. In addition, this paper also uses transfer learning and feature projection methods to solve the problem of scarcity of measured fault samples, and has a good transfer effect and high accuracy after transfer. The above results have verified the improvement effect and good noise immunity of the above method. © 2023 IEEE.

关键词： alexnet neural network hyperparameter optimization rolling bearing transfer learning algorithm

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Lung CT image aided detection COVID-19 based on alexnet network 5

Lung CT image aided detection COVID-19 based on Alexnet netw...

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5th International Conference on Communication, Image and Signal Processing (CCISP)

作者： Wang, Tao Zhao, Yongguo Zhu, Lin Liu, Guangliang Ma, Zhengguang Zheng, Jianghua Qilu Univ Technol Shandong Acad Sci Inst Automat Shandong Prov Key Lab Robot & Mfg Automat Technol Jinan Shandong Peoples R China

ISBN: (纸本)9781728185897

In this article, we use the alexnet network in deep learning to determine whether the lung CT images are infected with covid-19. First of all, in the data preprocessing stage, the original CT image data is scaled and normalized to reduce noise interference. The batch operation of training set and test set can increase the training speed;Secondly, build an eight-layer alexnet network model, set reasonable hyperparameters for each layer of the network, define the loss function and optimizer, and use the processed data to train the weight parameters of each layer in the network model. Finally, three indicators of accuracy, accuracy and recall are used to quantify the effect of model classification, and the impact of different training times on these three indicators is compared to select the best classification model. At the same time, use pyqt5 to write the corresponding auxiliary detection interface to facilitate the display of test results and the selection of classification models. The construction of the network model, the definition of the loss function and the definition of the optimizer are all based on the Pytorch deep learning framework.

关键词： Deep learning alexnet neural network Medical image

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Lung CT image aided detection COVID-19 based on alexnet network

Lung CT image aided detection COVID-19 based on Alexnet netw...

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作者： Tao Wang Yongguo Zhao Lin Zhu Guangliang Liu Zhengguang Ma Jianghua Zheng Qilu University of Technology (Shandong Academy of Sciences) Institute of automation Shandong Academy of Sciences Shandong Provincial Key Laboratory of Robot and Manufacturing Automation Technology

In this article, we use the alexnet network in deep learning to determine whether the lung CT images are infected with *** of all, in the data preprocessing stage, the original CT image data is scaled and normalized to reduce noise interference. The batch operation of training set and test set can increase the training speed;Secondly, build an eight-layer alexnet network model, set reasonable hyperparameters for each layer of the network, define the loss function and optimizer, and use the processed data to train the weight parameters of each layer in the network ***, three indicators of accuracy, accuracy and recall are used to quantify the effect of model classification, and the impact of different training times on these three indicators is compared to select the best classification *** the same time, use pyqt5 to write the corresponding auxiliary detection interface to facilitate the display of test results and the selection of classification *** construction of the network model, the definition of the loss function and the definition of the optimizer are all based on the Pytorch deep learning framework.

关键词： Deep learning alexnet neural network Medical image

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In situ investigation and detection of opto-mechanical properties of polymeric fibres from their digital distorted microinterferograms using machine learning algorithms

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OPTICS AND LASER TECHNOLOGY 2020年 129卷 106295-106295页

作者： Omar, E. Z. Sokkar, T. Z. N. Hamza, A. A. Mansoura Univ Fac Sci Phys Dept Mansoura Egypt

During the dynamic investigation of opto-mechanical properties of the fibres, hundreds of microinterferograms that carry the fibres information may be produced. These microinterferograms may be classified into four main classes due to focus plane of CCD camera and the inclination of the fibre relative to the fringes. Each class of them has a specific analysis method according to its class. The manual classification for these microinterferograms is a complex process and needs a professionally expert in the field. So, the automatic classification and investigation for these classes are main goal of the present study. Hence, an experimental dataset, including 400 microinterferograms for the four classes is established. To perform this task, Mach-Zehnder interferometer with a stretched device is used. To automate the classification for the classes in our dataset, we finetuned a pre-trained alexnet neural network. After training our dataset using this network, we obtained an 89.46% validation accuracy (VA). To increase the accuracy for the constructed network, two methods are proposed. The first method contains a combination between the alexnet and the support vector machine and this network achieved an 94.11% VA. The second method includes a combination between the alexnet and the K-Nearest Neighbor and an VA 96.64% is yielded. After the classification process, each microinterferogram is analyzed according to its class and the 2D refractive index of their fibres are investigated. Dynamic cold drawing and creep experiments are carried out for iPP fibres by considering alexnet and the K-Nearest Neighbor network to give the relation between its refractive indices and strains.

关键词： Holographic interferometer Refractive index Fourier optics Machine learning techniques Polymeric fibres alexnet neural network Cold drawing Creep

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