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Fault diagnosis of HVAC system with imbalanced data using multi-scale convolution composite neural network

Building Simulation 2024年第3期17卷 371-386页

作者： Rouhui Wu Yizhu Ren Mengying Tan Lei Nie School of Mechanical Engineering Hubei University of TechnologyWuhan430068China Industrial Research Institute of Xiangyang Hubei University of Technology Xiangyang441100China

Accurate fault diagnosis of heating,ventilation,and air conditioning(HVAC)systems is of significant importance for maintaining normal operation,reducing energy consumption,and minimizing maintenance costs.However,in practical applications,it is challenging to obtain sufficient fault data for HVAC systems,leading to imbalanced data,where the number of fault samples is much smaller than that of normal samples.Moreover,most existing HVAC system fault diagnosis methods heavily rely on balanced training sets to achieve high fault diagnosis accuracy.Therefore,to address this issue,a composite neural network fault diagnosis model is proposed,which combines SMOTETomek,multi-scale one-dimensional convolutional neural networks(M1DCNN),and support vector machine(SVM).This method first utilizes SMOTETomek to augment the minority class samples in the imbalanced dataset,achieving a balanced number of faulty and normal data.Then,it employs the M1DCNN model to extract feature information from the augmented dataset.Finally,it replaces the original Softmax classifier with an SVM classifier for classification,thus enhancing the fault diagnosis accuracy.Using the SMOTETomek-M1DCNN-SVM method,we conducted fault diagnosis validation on both the ASHRAE RP-1043 dataset and experimental dataset with an imbalance ratio of 1:10.The results demonstrate the superiority of this approach,providing a novel and promising solution for intelligent building management,with accuracy and F1 scores of 98.45%and 100%for the RP-1043 dataset and experimental dataset,respectively.

关键词： fault diagnosis chiller imbalanced data SMOTETomek multi-scale neural networks

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multi-scale three-dimensional variational data assimilation for high-resolution aerosol observations: Methodology and application

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Science China Earth Sciences 2022年第10期65卷 1961-1971页

作者： Zengliang ZANG Yanfei LIANG Wei YOU Yi LI Xiaobin PAN Zhijin LI College of Meteorology and Oceanography National University of Defense TechnologyChangsha 410073China No.32145 Unit of PLA Xinxiang 453000China Department of Atmospheric and Oceanic Sciences Fudan UniversityShanghai 200433China Joint Institute for Regional Earth System Science and Engineering University of California Los AngelesCalifornia 91109USA

With an increasing number of air quality monitoring stations installed around the Chinese mainland,high-resolution aerosol observations become available,allowing improvements in air pollution monitoring and aerosol forecasting.However,the multi scales(especially small-scale)information included in high-resolution aerosol observations could not be effectively utilized by the traditional three-dimensional variational method(3DVAR).This study attempted to extend the traditional 3DVAR to a multi-scale 3DVAR with two iteration steps,two-scale-3DVAR(TS-3DVAR),to improve the effectiveness of assimilating high-resolution observations.In TS-3DVAR,the large-scale and small-scale components of observation information were decomposed from the original high-resolution observations using a Gaussian smoothing method and then assimilated using the corresponding large-scale or small-scale background error covariances which were derived from the partitioned background error samples.The data assimilation(DA)analysis field generated by TS-3DVAR is more accurate than 3DVAR in reproducing the field’s multi-scale characteristics,which could thus be used as the initial chemical field of the air quality model to improve aerosol forecasting.Particulate matter with an aerodynamic diameter of less than 2.5μm(PM_(2.5))and 10.0μm(PM_(10)) from the surface air quality monitoring stations from November 01 to November 30,2018 at 00:00 were assimilated daily to verify the effects of TS-3DVAR and 3DVAR on the aerosol analysis and forecast accuracy.The results showed that TS-3DVAR better constrained both large-scale and small-scale,especially the spatial wavelengths in a range of 54-216 km and those above 351 km.The average power spectra of the TS-3DVAR assimilation increment in the two wavelength ranges were 71.70%and 35.33%higher than those of 3DVAR.As a result,the TS-3DVAR was more effective than 3DVAR in improving the accuracy of the initial chemical field,and thereby the forecasting capability for PM_(2.5).In the initial chemical field,the 30-

关键词： multi-scale 3DVAR Data assimilation Aerosol forecast WRF-Chem

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A Soft Sensor with Light and Efficient multi-scale Feature Method for multiple Sampling Rates in Industrial Processing

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Machine Intelligence Research 2024年第2期21卷 400-410页

作者： Dezheng Wang Yinglong Wang Fan Yang Liyang Xu Yinong Zhang Yiran Chen Ning Liao School of Automation Southeast UniversityNanjing 210096China Software and Artificial Intelligence College Chongqing Institute of EngineeringChongqing 400056China Beijing National Research Center for Information Science and Technology(BNRist) Department of AutomationTsinghua UniversityBeijing 100084China Liangjiang International College Chongqing University of TechnologyChongqing 401135China Smart City College Beijing Union UniversityBeijing 100101China

In industrial process control systems,there is overwhelming evidence corroborating the notion that economic or technical limitations result in some key variables that are very difficult to measure online.The data-driven soft sensor is an effective solution because it provides a reliable and stable online estimation of such variables.This paper employs a deep neural network with multiscale feature extraction layers to build soft sensors,which are applied to the benchmarked Tennessee-Eastman process(TEP)and a real wind farm case.The comparison of modelling results demonstrates that the multiscale feature extraction layers have the following advantages over other methods.First,the multiscale feature extraction layers significantly reduce the number of parameters compared to the other deep neural networks.Second,the multiscale feature extraction layers can powerfully extract dataset characteristics.Finally,the multiscale feature extraction layers with fully considered historical measurements can contain richer useful information and improved representation compared to traditional data-driven models.

关键词： multi-scale feature extractor deep neural network(DNN) multirate sampled industrial processes prediction

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Efficient multi-scale community search method based on spectral graph wavelet

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Frontiers of Computer Science 2023年第5期17卷 89-101页

作者： Cairui YAN Huifang MA Qingqing LI Fanyi YANG Zhixin LI College of Computer Science and Engineering Northwest Normal UniversityLanzhou 730070China Guangxi Key Lab of multi-source Information Mining and Security Guangxi Normal UniversityGuilin 541004China

Community search is an important problem in network analysis,which has attracted much attention in recent years.As a query-oriented variant of community detection problem,community search starts with some given nodes,pays more attention to local network structures,and gets personalized resultant communities quickly.The existing community search method typically returns a single target community containing query nodes by default.This is a strict requirement and does not allow much flexibility.In many realworld applications,however,query nodes are expected to be located in multiple communities with different semantics.To address this limitation of existing methods,an efficient spectral-based multi-scale Community Search method(MSCS)is proposed,which can simultaneously identify the multi-scale target local communities to which query node belong.In MSCS,each node is equipped with a graph Fourier multiplier operator.The access of the graph Fourier multiplier operator helps nodes to obtain feature representations at various community scales.In addition,an efficient algorithm is proposed for avoiding the large number of matrix operations due to spectral methods.Comprehensive experimental evaluations on a variety of real-world datasets demonstrate the effectiveness and efficiency of the proposed method.

关键词： community search multi-scale spectral wavelets

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细胞单层的静态与动态力学研究: 一个多尺度结构模型

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Acta Mechanica Sinica 2022年第5期38卷 166-173,I0004页

作者：王欢杭久涛畅茁徐光魁 Laboratory for multiscale Mechanics and Medical Science Department of Engineering MechanicsState Key Laboratory for Strength and Vibration of Mechanical StructuresSchool of Aerospace EngineeringXi’an Jiaotong UniversityXi’an 710049China

上皮单层在伤口愈合和胚胎发育等各种生理活动中起着十分重要的作用.随着技术的快速发展,上皮单层的力学行为得到了广泛的关注与研究.在阶跃载荷作用下,上皮单层的蠕变响应表现出对时间的幂律依赖,且其幂律指数大于单细胞的幂律指数.而... 详细信息

上皮单层在伤口愈合和胚胎发育等各种生理活动中起着十分重要的作用.随着技术的快速发展,上皮单层的力学行为得到了广泛的关注与研究.在阶跃载荷作用下,上皮单层的蠕变响应表现出对时间的幂律依赖,且其幂律指数大于单细胞的幂律指数.而在静态载荷下,上皮单层的弹性模量比单细胞高出近两个数量级.到目前为止,仍然缺乏一个能够同时描述上皮单层动态和静态响应的力学模型.本文基于细胞的结构特征,建立了一个多尺度结构的细胞单层模型.所建模型能够自然地描述细胞单层的动态和静态力学行为.此外,本文还探讨了细胞骨架和细胞膜模量分别对单细胞和细胞单层的幂律流变学响应以及应力-应变关系的影响.这一工作为后续研究更复杂的上皮单层的力学行为奠定了基础.

关键词： Cell monolayer Power-law Elastic modulus multi-scale Biomechanical model

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multi-scale synergic optimization strategy for dielectric energy storage ceramics

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Journal of Advanced Ceramics 2023年第4期12卷 649-680页

作者： Xuhui Fan Jing Wang Hao Yua Zehan Zheng Ji Zhang Kongjun Zhu State Key Laboratory of Mechanics and Control of Mechanical Structures College of Aerospace EngineeringNanjing University of Aeronautics and AstronauticsNanjing 210016China School of Materials Science and Engineering Nanjing University of Aeronautics and AstronauticsNanjing 210016China Department of Materials Science and Engineering Southern University of Science and TechnologyShenzhen 518055China School of Materials Science and Engineering Nanjing University of Science and TechnologyNanjing 210094China

Dielectric capacitors,serving as the indispensable components in advanced high-power energy storage devices,have attracted ever-increasing attention with the rapid development of science and technology.Among various dielectric capacitors,ceramic capacitors with perovskite structures show unique advantages in actual application,e.g.,excellent adaptability in high-temperature environments.And the optimization of their energy storage performance has become a hot research topic recently.This review presents the basic principles of energy storage in dielectric ceramics and introduces multi-scale synergic optimization strategies according to the key factors for superior energy storage performance.By summarizing the common points in numerous works,several universal modification strategies are reviewed,and future research on fatigue fracture of ceramic capacitors under multi-field including but not limited to force,electric,and thermal coupling conditions is also anticipated.

关键词： dielectric capacitors high-power energy storage multi-scale

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multi-scale analysis on the pulverized coal flow behaviors under high,pressure dense-phase pneumatic conveying

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Particuology 2022年第1期20卷 107-114页

作者： Heming Gao Xiaojuan Wang Qi Chang School of Mechanical and Precision Instrument Engineering Xi'an University of TechnologyXi'an 710048China

To deeply knowledge of the flow behaviors of pulverized coal particles in dense gas-solid two-phase flow,a multi-scale analysis method based on electrostatic sensor array is applied for the multi-scale characterization of flow behaviors of dense gas-solid flow.The experimental results indicate that:for steady flow,with the increment of conveying pressure difference,the individual particles increase and the particle clusters decrease,the individual particle distribution is always inhomogeneous but the particle cluster distribution tends to be more homogeneous over the cross-section of pipe,while the average flow behavior of pulverized coal particles is always in the relatively static state.For unsteady flow,the average flow behavior of pulverized coal particles is dynamic,and the flow behaviors of the multi-scale flow structures over the cross-section of pipe are all significantly inhomogeneous.Moreover,the effect of particle size on flow behavior of pulverized coal is also investigated and validated.

关键词： Dense-phase pneumatic conveying Pulverized coal multi-scale Particle charging

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Identification of tomato leaf diseases using convolutional neural network with multi-scale and feature reuse

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International Journal of Agricultural and Biological Engineering 2023年第6期16卷 226-235页

作者： Peng Li Nan Zhong Wei Dong Meng Zhang Dantong Yang College of Engineering South China Agricultural UniversityGuangzhou 510642China Agricultural Economy and Information Research Institute Anhui Academy of Agricultural SciencesHefei 230031China

Various diseases seriously affect the quality and yield of tomatoes. Fast and accurate identification of disease types is of great significance for the development of smart agriculture. Many Convolution Neural Network (CNN) models have been applied to the identification of tomato leaf diseases and achieved good results. However, some of these are executed at the cost of large calculation time and huge storage space. This study proposed a lightweight CNN model named MFRCNN, which is established by the multi-scale and feature reuse structure rather than simply stacking convolution layer by layer. To examine the model performances, two types of tomato leaf disease datasets were collected. One is the laboratory-based dataset, including one healthy and nine diseases, and the other is the field-based dataset, including five kinds of diseases. Afterward, the proposed MFRCNN and some popular CNN models (AlexNet, SqueezeNet, VGG16, ResNet18, and GoogLeNet) were tested on the two datasets. The results showed that compared to traditional models, the MFRCNN achieved the optimal performance, with an accuracy of 99.01% and 98.75% in laboratory and field datasets, respectively. The MFRCNN not only had the highest accuracy but also had relatively less computing time and few training parameters. Especially in terms of storage space, the MFRCNN model only needs 2.7 MB of space. Therefore, this work provides a novel solution for plant disease diagnosis, which is of great importance for the development of plant disease diagnosis systems on low-performance terminals.

关键词： tomato diseases convolutional neural network confusion matrix multi-scale feature reuse

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YOLO-MFD:Remote Sensing Image Object Detection with multi-scale Fusion Dynamic Head

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Computers, Materials & Continua 2024年第5期79卷 2547-2563页

作者： Zhongyuan Zhang Wenqiu Zhu School of Computer Science Hunan University of TechnologyZhuzhou412007China

Remote sensing imagery,due to its high altitude,presents inherent challenges characterized by multiple scales,limited target areas,and intricate backgrounds.These inherent traits often lead to increased miss and false detection rates when applying object recognition algorithms tailored for remote sensing imagery.Additionally,these complexities contribute to inaccuracies in target localization and hinder precise target categorization.This paper addresses these challenges by proposing a solution:The YOLO-MFD model(YOLO-MFD:Remote Sensing Image Object Detection withmulti-scale Fusion Dynamic Head).Before presenting our method,we delve into the prevalent issues faced in remote sensing imagery analysis.Specifically,we emphasize the struggles of existing object recognition algorithms in comprehensively capturing critical image features amidst varying scales and complex backgrounds.To resolve these issues,we introduce a novel approach.First,we propose the implementation of a lightweight multi-scale module called CEF.This module significantly improves the model’s ability to comprehensively capture important image features by merging multi-scale feature information.It effectively addresses the issues of missed detection and mistaken alarms that are common in remote sensing imagery.Second,an additional layer of small target detection heads is added,and a residual link is established with the higher-level feature extraction module in the backbone section.This allows the model to incorporate shallower information,significantly improving the accuracy of target localization in remotely sensed images.Finally,a dynamic head attentionmechanism is introduced.This allows themodel to exhibit greater flexibility and accuracy in recognizing shapes and targets of different sizes.Consequently,the precision of object detection is significantly improved.The trial results show that the YOLO-MFD model shows improvements of 6.3%,3.5%,and 2.5%over the original YOLOv8 model in Precision,map@0.5 and map@0.5:0.95,separately.These results illustr

关键词： Object detection YOLOv8 multi-scale attention mechanism dynamic detection head

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Dynamic evolution and trend prediction of multi-scale green innovation in China

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Geography and Sustainability 2023年第3期4卷 222-231页

作者： Xiaohua Xin Lachang Lyu Yanan Zhao College of Resource Environment and Tourism Capital Normal UniversityBeijing 100048China Beijing Urban Innovation and Development Research Center Beijing 100048China

Numerous studies deal with spatial analysis of green innovation(GI).However,researchers have paid limited attention to analyzing the multi-scale evolution patterns and predicting trends of GI in China.This paper seeks to address this research gap by examining the multi-scale distribution and evolutionary characteristics of GI activities based on the data from 337 cities in China during 2000-2019.We used scale variance and the two-stage nested Theil decomposition method to examine the spatial distribution and inequalities of GI in China at multiple scales,including regional,provincial,and prefectural.Additionally,we utilized the Markov chain and spatial Markov chain to explore the dynamic evolution of GI in China and predict its long-term development.The findings indicate that GI in China has a multi-scale effect and is highly sensitive to changes in spatial scale,with significant spatial differences of GI decreasing in each scale.Furthermore,the spatiotemporal evolution of GI is influenced by both geospatial patterns and spatial scales,exhibiting the“club convergence”effect and a tendency to transfer to higher levels of proximity.This effect is more pronounced on a larger scale,but it is increasingly challenging to transfer to higher levels.The study also indicates a steady and sustained growth of GI in China,which concentrates on higher levels over time.These results contribute to a more precise understanding of the scale at which GI develops and provide a scientific basis and policy suggestions for optimizing the spatial structure of GI and promoting its development in China.

关键词： Green innovation Spatial pattern Trend prediction multi-scale China

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