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Fault diagnosis of rocket engine ground testing bed with self-organizing maps(SOMs)

Journal of Harbin Institute of technology(New Series) 2009年第2期16卷 204-208页

作者：朱宁冯志刚王祁 Dept.of Automatic Measurement and Control Harbin Institute of Technology School of Automation Shenyang Institute of Aeronautical Engineering

To solve the fault diagnosis problem of liquid propellant rocket engine ground testing bed,a fault diagnosis approach based on self-organizing map(SOM)is *** SOM projects the multidimensional ground testing bed data into a two-dimensional *** of the SOM is used to cluster the ground testing bed *** out map of the SOM is divided to several *** region is represented for one fault *** fault mode of testing data is determined according to the region of their labels belonged *** method is evaluated using the testing data of a liquid-propellant rocket engine ground testing bed with sixteen fault *** results show that it is a reliable and effective method for fault diagnosis with good visualization property.

关键词： fault diagnosis self-organizing map （SOM） U-matrix visualization

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Mask R-CNN based deep learning analysis on in-situ measured crystal images with automatic dataset labelling 41

Mask R-CNN based deep learning analysis on in-situ measured ...

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第41届中国控制会议

作者： Chen Jiang Tao Liu Timing Yang Zhenyu Jiang Honglin Liu School of Control Science and Engineering Dalian University of Technology School of Information and Communication Engineering Dalian University of Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

For real-time measurement of crystal size distribution(CSD) by in-situ captured crystal images,a deep-learning based image analysis method is proposed to improve measurement accuracy and efficiency,based on the well recognized maskregional convolutional neural network(Mask R-CNN).An automatic dataset labelling algorithm is established to facilitate preparing the training dataset that is required to include a large number of crystal image samples for effective deep ***,an image thresholding segmentation algorithm is introduced to extract the region of interest(ROI) in each crystal image sample for training the Mask R-CNN,such that improved segmentation accuracy and efficiency could be obtained for online image analysis to measure CSD during a crystallization *** results on measuring the crystallization process of β form L-glutamic acid(β-LGA) are shown to verify the effectiveness and advantage of the proposed method.

关键词： crystal image analysis in-situ measurement Mask R-CNN image labelling crystal size distribution L-glutamic acid crystallization process

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Automatic three-dimensional reconstruction of transparent objects with multiple optimization strategies under limited constraints

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Image and Vision Computing 2025年 160卷

作者： Xiaopeng Sha Xiaopeng Si Yujie Zhu Shuyu Wang Yuliang Zhao School of Control Engineering Northeastern University at Qinhuangdao Qinhuangdao 066004 China Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology Qinhuangdao 066004 China

Reconstructing transparent objects with limited constraints has long been considered a highly challenging problem. Due to the complex interaction between transparent objects and light, which involves intricate refraction and reflection relationships, traditional three-dimensional (3D) reconstruction methods are less than effective for transparent objects. To address this issue, this study proposes a 3D reconstruction method specifically designed for transparent objects. Incorporating multiple optimization strategies, the method works under limited constraints to achieve the automatic reconstruction of transparent objects with only a few transparent object images in any known environment, without the need for specific data collection devices or environments. The proposed method makes use of automatic image segmentation and modifies the network interface and structure of the PointNeXt algorithm to introduce the TransNeXt network, which enhances normal features, optimizes weight attenuation, and employs a preheating cosine annealing learning rate. We use several steps to reconstruct the complete 3D shape of transparent objects. First, we initialize the transparent shape with a visual hull reconstructed with the contours obtained by the TOM-Net. Then, we construct the normal reconstruction network to estimate the normal values. Finally, we reconstruct the complete 3D shape using the TransNeXt network. Multiple experiments show that the TransNeXt network exhibits superior reconstruction performance to other networks and can effectively perform the automatic reconstruction of transparent objects even under limited constraints.

关键词： 3D reconstruction

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Learning disturbance-aware correlation filter with adaptive Kaiser window for visual object tracking

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Image and Vision Computing 2025年 160卷

作者： Jianming Zhang Jiangxin Dai Wentao Chen Ke Nai Key Laboratory of Safety Control of Bridge Engineering Ministry of Education (Changsha University of Science and Technology) Changsha 410076 China School of Computer and Communication Engineering Changsha University of Science and Technology Changsha 410076 China

Discriminative Correlation Filters (DCF) have been recognized as a classic and effective method in the field of object tracking. In order to mitigate boundary effects, prior DCF-based tracking methods have commonly employed a fixed Hanning window, limiting the adaptability to fluctuations of the response map. Therefore, we propose a disturbance-aware correlation filter with adaptive Kaiser window (DCFAK) for visual object tracking. The adaptive Kaiser window dynamically adjusts its values according to the kurtosis of the response map, effectively suppressing boundary effects. Additionally, to further improve robustness, our DCFAK introduces a disturbance peaks suppression method, which can better distinguish the target object from the objects with similar appearance in the background by attenuating the sub-peaks within the response map. We comprehensively evaluate the performance of our DCFAK on seven datasets, including OTB-2013, OTB, 2015, TC-128, DroneTB, 70, UAV123, UAVDT, and LaSOT. The results demonstrate the superior performance of our method across these datasets.

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Characteristics and underlying physics of ionic wind in dc corona discharge under different polarities

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Chinese Physics B 2019年第7期28卷 324-332页

作者： Tongkai Zhang Yu Zhang Qizheng Ji Ben Li Jiting Ouyang National Key Laboratory of Mechatronic Engineering and Control School of Mechatronical Engineering Beijing Institute of Technology Beijing 100081 China School of Physics Beijing Institute of Technology Beijing 100081 China Beijing Orient Institute of Measurement and Test Beijing 100094 China

During a dc corona discharge, the ions’ momentum will be transferred to the surrounding neutral molecules, inducing an ionic *** characteristics of corona discharge and the induced ionic wind are investigated experimentally and numerically under different polarities using a needle-to-ring electrode *** morphology and mechanism of corona discharge, as well as the characteristics and mechanism of the ionic wind, are different when the needle serves as cathode or *** the different polarities of the applied voltage, the ionic wind velocity has a linear relation with the *** ionic wind is stronger but has a smaller active region for positive corona compared to that for negative corona under a similar *** involved physics are analyzed by theoretical deduction as well as simulation using a fluid *** ionic wind of negative corona is mainly affected by negative *** discharge channel has a dispersed feature due to the dispersed field, and therefore the ionic wind has a larger active *** ionic wind of positive corona is mainly affected by positive *** discharge develops in streamer mode, leading to a stronger ionic wind but a lower active area.

关键词： corona discharge ionic wind electrode polarity

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A novel image reconstruction algorithm based oncompressed sensing for electrical capacitance 5

A novel image reconstruction algorithm based oncompressed se...

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5th International Symposium on Computational Intelligence and Industrial Applications, ISCIIA 2012

作者： Deyun, Chen Zhiqiang, Li Zhen, Zhang Liyuan, Xu Wenxin, Yu Xiaoyang, Yu School of Computer Science and Technology Harbin University of Science and Technology Harbin China School of Measurement-Control and Communication Engineering Harbin University of Science and Technology Harbin China

According to the image reconstruction accuracy influenced by the "soft field" nature and the limited projection data in electrical capacitance tomography, based on the working principle of the electrical capacitance tomography system, a Novel image reconstruction algorithm based on compressed sensing is proposed in the paper. The method based on ART (algebra reconstruction technique) organically combines the gradient sparse of image and ART, and reduces the norm of image gradient with full-variational method, and improves the accuracy and speed of image reconstruction. Experimental results and simulation data indicate that the imaging accuracy is markedly improved, and the image is closed to the *** new algorithm presents a feasible and effective way to research on image reconstruction algorithm for Electrical Capacitance Tomography System.

关键词： Image reconstruction

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Fault Diagnosis Method of Rolling Bearings under Different Working Conditions Based on Federated Feature Transfer Learning 3

Fault Diagnosis Method of Rolling Bearings under Different W...

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3rd International Conference on Sensing, measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2022

作者： Kang, Shouqiang Yang, Jiawei Sun, Yulin Wang, Yujing Wang, Qingyan Mikulovich, V.I. School of Measurement-Control and Communication Engineering Harbin University of Science and Technology Harbin China Belarusian State University Minsk Belarus

ISBN: (纸本)9781665492812

A rolling bearing fault diagnosis method based on the federated feature transfer learning is proposed for the low accuracy of the diagnosis model in the presence of large differences in data distribution under different working conditions, difficulty in obtaining labeled data and non-sharing of data among different users. This method performs wavelet transformation on the time domain vibration data of rolling bearings to obtain a time-frequency diagram. The priori labeled public data and the multi-user island private data are regarded as the source domain and the target domain. The multi-representation feature extraction structure is introduced to improve the original residual network. Based on an improved residual network and multi-representation features in the source domain and the target domain, every local model and a federated global model are constructed. Through verification of bearing data, the proposed method can establish an effective fault diagnosis model with high fault diagnosis accuracy. It can integrate the knowledge of isolated island data without sharing data among multiple users. © 2022 IEEE.

关键词： Different working conditions domain adaptation fault diagnosis feature transfer learning federated learning

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Combined Classification of Hyperspectral and LiDAR Data based on Dual-Channel Cross-Transformer 5

Combined Classification of Hyperspectral and LiDAR Data base...

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5th International Conference on Video, Signal and Image Processing, VSIP 2023

作者： Zhou, Binbin Wang, Qingyan Zhang, Junping Wang, Yujing School Of: Measurement-Control And Communication Engineering Harbin University Of Science And Technology Harbin150080 China School Of: Electronics And Information Engineering Harbin Institute Of Technology Harbin150001 China

ISBN: (纸本)9798400709272

In the face of complex scenes, single-modal dominant classification tasks encounter limitations in performance due to insufficient information. On the other hand, joint classification of multimodal remote sensing data faces challenges such as data sample differences and lack of correlation in physical features between modalities, which can impact classification performance. To fully integrate the heterogeneous information of multimodal data and improve classification performance, we propose a dual-channel cross-transformer feature fusion extraction network. The framework leverages self-attention mechanisms to aggregate features within each modality, and the feature fusion module based on cross-modal attention fully considers the complementary information between modalities. Classification tasks are performed using the fused spatial-spectral features obtained from the joint representation of modalities. Extensive experiments conducted on the Houston and MUUFL datasets demonstrate the effectiveness of the proposed model compared to existing methods. © 2023 ACM.

关键词： Classification (of information)

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Research and simulation test base on LFSR reseeding test compression technology

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4th International Symposium on Computational Intelligence and Industrial Applications, ISCIIA 2010

作者： Gu, Jing Yi, Lin Yu, Xiaoyang School of Computer Science and Technology Harbin University of Science and Technology Harbin China School of Measurement and Control Technology and Communication Engineering Harbin University of Science and Technology Harbin China

ISBN: (纸本)9787121113154

Considering from test time, test power and access, this article adopts new method that LFSR reseeding based on syncopation of some test patterns. In a test set, some patterns have a lot of confirmed bits and are hardly encoded by LFSR. They are syncopated and substituted by generating patterns. This method can reduce the number of seeds and improve the utilization of seed so that achieve the purpose of test data compression. Using in the benchmark circuits, experimental results show that it can better improve the compression rate and reduce the cost of test.

关键词： Timing circuits

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Remaining Life Prediction Method of Rolling Bearing Based on TD3 Optimization Model Updating Strategy 16

Remaining Life Prediction Method of Rolling Bearing Based on...

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16th IEEE International Conference on Electronic measurement and Instruments, ICEMI 2023

作者： Wang, Yujing Jiao, Guangpeng Kang, Shouqiang Xie, Jinbao Sun, Yulin School of Measurement-Control and Communication Engineering Harbin University of Science and Technology Harbin China School of Physics and Electronic Engineering Hainan Normal University Haikou China

ISBN: (纸本)9798350327144

To address the problems of model degradation and low prediction accuracy in the of update procedure the rolling bearing life prediction model, the updating strategy of twin delayed deep deterministic policy gradient(TD3) is suggested to optimize the prediction model, and it applied to rolling bearing remaining useful life prediction. Continuous wavelet transform is used to build a dataset by converting the rolling bearing vibration data into two-dimensional time-frequency spectrum diagram. Deep reinforcement learning is introduced to design the elements of state, action, reward value and loss function, which are employed to training of the prediction model, this model is composed convolutional neural network (CNN) and bidirectional long short-term memory. The delayed update and soft update techniques of the TD3 algorithm are used to reduce the update frequency of the CNN feature network. A new loss function is proposed to establish a new model updating strategy for solving the prediction model degradation problem. The experimental study using the PHM2012 dataset and the finding indicate that, in comparison to the compared methodologies, the suggested prediction method with model optimization strategy can reduce the root mean square error and mean square error by more than 23.8% and 20.8% respectively, reducing the prediction error by at least 18.6% and improve the scores by more than 0.213. © 2023 IEEE.

关键词： Mean square error

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