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Mechanical Fault Diagnosis Based on Band-phase-randomized Surrogate Data and Multifractal

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Chinese Journal of Mechanical Engineering 2011年第5期24卷 885-890页

作者： zhang shuqing ZHAO Yuchun zhang Liguo JIN Mei School of Electrical Engineering Yanshan University Qinhuangdao 066004 China

The vibration signals of machinery with various faults often show clear nonlinear ***,fractal dimension analysis as the common useful method for nonlinear signal analysis,is a kind of single fractal form,which only reflects the overall irregularity of signals,but cannot describe its local scaling *** comprehensive revealing of internal properties,a combinatorial method based on band-phase-randomized（BPR） surrogate data and multifractal is *** surrogate data method is effective to eliminate nonlinearity in specified frequency band for a fault signal,which can be utilized to detect nonlinear degree in whole fault signal by nonlinear titration method,and the overall nonlinear distribution of fault signal is displayed in nonlinear characteristic curve that can be used to analyze the fault signal *** multifractal theory as a quantitative analysis method is used to describe geometrical characteristics and local scaling properties,and asymmetry coefficient of multifractal spectrum and multifractal entropy for fault signals are extracted as new criterions to diagnose machinery *** typical faults include rotor misalignment,transversal crack,and static-dynamic rubbing fault are analyzed,and the results indicate that those faults can be distinguished by the proposed method effectively,which provides a qualitative and quantitative analysis way in the field of machinery fault diagnosis.

关键词： fault diagnosis band-phase-randomized surrogate data nonlinear titration multifractal

Multiple Time-scale Characteristics of Runoff Variations in Middle Reaches of Huolin River and Their Effects

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Chinese Geographical Science 2007年第2期17卷 143-150页

作者： LU Xiaoning DENG Wei zhang shuqing Northeast Institute of Geography and Agroecology Chinese Academy of Sciences Changchun 130012 China Institute of Mountain Hazards and Environment Chinese Academy of Sciences Chengdu 610041 China Graduate University of Chinese Academy of Sciences Beijing 100049 China Chengdu University of Information Technology Chengdu 620225 China

The Huolin River is one of the most important water sources for Xianghai wetland, Horqin wetland, and Chaganhu wetland in the western Songnen Plain of Northeast China. The annual runoff series of 46 years at Baiyunhushuo Hydrologic Station, which is located in the middle reaches of the Huolin River, were analyzed by using wavelet analysis. Main objective was to discuss the periodic characteristics of the runoff, and examine the temporal patterns of the Huolin River recharging to the floodplain wetlands in the lower reaches of the river, and the corresponding effects of recharging variation on the environmental evolution of the wetlands. The results show that the annual runoff varied mainly at three time scales. The intensities of periodical signals at different time scales were strongly characterized by local distribution in its time frequency domain. The interdecadal variation at a scale of more than 30yr played a leading role in the temporal pattern ofrnnoffvariation, and at this scale, the runoffat Baiyunhushuo Hydrologic Station varied in turn of flood, draught and flood. Accordingly, the landscape of the floodplain wetlands presented periodic features, especially prominent before the 1990s. Compared with intense human activities, the runoff periodic pattern at middle （10-20yr） and small （1-10yr） scales, which has relatively low energy, exerted unobvious effects on the environmental evolution of the floodplain wetlands, especially after the 1990s.

关键词： floodplain wetland runoff multiple time-scale wavelet analysis Huolin River

A New Method of Wind Turbine Bearing Fault Diagnosis Based on Multi-Masking Empirical Mode Decomposition and Fuzzy C-Means Clustering

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Chinese Journal of Mechanical Engineering 2019年第3期32卷 156-167页

作者： Yongtao Hu shuqing zhang Anqi Jiang Liguo zhang Wanlu Jiang Junfeng Li Institute of Electric Engineering Yanshan UniversityQinhuangdao 066004China School of Electrical Engineering and Automation Henan Institute of TechnologyXinxiang 453003China

Based on Multi-Masking Empirical Mode Decomposition (MMEMD) and fuzzy c-means (FCM) clustering, a new method of wind turbine bearing fault diagnosis FCM-MMEMD is proposed, which can determine the fault accurately and timely. First, FCM clustering is employed to classify the data into different clusters, which helps to estimate whether there is a fault and how many fault types there are. If fault signals exist, the fault vibration signals are then demodulated and decomposed into different frequency bands by MMEMD in order to be analyzed further. In order to overcome the mode mixing defect of empirical mode decomposition (EMD), a novel method called MMEMD is proposed. It is an improvement to masking empirical mode decomposition (MEMD). By adding multi-masking signals to the signals to be decomposed in different levels, it can restrain low-frequency components from mixing in highfrequency components effectively in the sifting process and then suppress the mode mixing. It has the advantages of easy implementation and strong ability of suppressing modal mixing. The fault type is determined by Hilbert envelope finally. The results of simulation signal decomposition showed the high performance of MMEMD. Experiments of bearing fault diagnosis in wind turbine bearing fault diagnosis proved the validity and high accuracy of the new method.

关键词： Wind turbine bearing faults diagnosis Multi-masking empirical mode decomposition (MMEMD) Fuzzy c-mean (FCM) clustering

Temporal sequence Object-based CNN(TS-OCNN) for crop classification from fine resolution remote sensing image time-series

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The Crop Journal 2022年第5期10卷 1507-1516页

作者： Huapeng Li Yajun Tian Ce zhang shuqing zhang Peter MAtkinson Northeast Institute of Geography and Agroecology Chinese Academy of SciencesChangchun 130012JilinChina Lancaster Environment Centre Lancaster UniversityLancaster LA14YQUK Faculty of Science and Technology Lancaster UniversityLancaster LA14YRUK

Accurate crop distribution mapping is required for crop yield prediction and field management. Due to rapid progress in remote sensing technology, fine spatial resolution(FSR) remotely sensed imagery now offers great opportunities for mapping crop types in great detail. However, within-class variance can hamper attempts to discriminate crop classes at fine resolutions. Multi-temporal FSR remotely sensed imagery provides a means of increasing crop classification from FSR imagery, although current methods do not exploit the available information fully. In this research, a novel Temporal Sequence Object-based Convolutional Neural Network(TS-OCNN) was proposed to classify agricultural crop type from FSR image time-series. An object-based CNN(OCNN) model was adopted in the TS-OCNN to classify images at the object level(i.e., segmented objects or crop parcels), thus, maintaining the precise boundary information of crop parcels. The combination of image time-series was first utilized as the input to the OCNN model to produce an ‘original’ or baseline classification. Then the single-date images were fed automatically into the deep learning model scene-by-scene in order of image acquisition date to increase successively the crop classification accuracy. By doing so, the joint information in the FSR multi-temporal observations and the unique individual information from the single-date images were exploited comprehensively for crop classification. The effectiveness of the proposed approach was investigated using multitemporal SAR and optical imagery, respectively, over two heterogeneous agricultural areas. The experimental results demonstrated that the newly proposed TS-OCNN approach consistently increased crop classification accuracy, and achieved the greatest accuracies(82.68% and 87.40%) in comparison with state-of-the-art benchmark methods, including the object-based CNN(OCNN)(81.63% and85.88%), object-based image analysis(OBIA)(78.21% and 84.83%), and standard pixel-wise

关键词： Convolutional neural network Multi-temporal imagery Object-based image analysis(OBIA) Crop classification Fine spatial resolution imagery

Land Cover Classification with Multi-source Data Using Evidential Reasoning Approach

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Chinese Geographical Science 2011年第3期21卷 312-321页

作者： LI Huapeng zhang shuqing SUN Yan GAO Jing Northeast Institute of Geography and Agroecology Chinese Academy of Sciences Changchun 130012 China Graduate University of Chinese Academy of Sciences Beijing 100049 China School of Computer and Information Science University of South Australia Adelaide 5095 Australia

Land cover classification is the core of converting satellite imagery to available geographic ***,spectral signatures do not always provide enough information in classification ***,the application of multi-source data becomes *** paper presents an evidential reasoning (ER) approach to incorporate Landsat TM imagery,altitude and slope *** show that multi-source data contribute to the classification accuracy achieved by the ER method,whereas play a negative role to that derived by maximum likelihood classifier (MLC).In comparison to the results derived based on TM imagery alone,the overall accuracy rate of the ER method increases by 7.66% and that of the MLC method decreases by 8.35% when all data sources (TM plus altitude and slope) are *** ER method is regarded as a better approach for multi-source image *** addition,the method produces not only an accurate classification result,but also the uncertainty which presents the inherent difficulty in classification *** uncertainty associated to the ER classification image is evaluated and proved to be useful for improved classification accuracy.

关键词： evidential reasoning Dempster-Shafer theory of evidence multi-source data geographic ancillary data land cover classification classification uncertainty

Evolving Neural Network Using Variable String Genetic Algorithm for Color Infrared Aerial Image Classification

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Chinese Geographical Science 2008年第2期18卷 162-170页

作者： FU Xiaoyang P E R Dale zhang shuqing Institute of Computer and Science Technology Jilin University Zhuhai 519041 China Griffith Sehool of Environment Griffith University Queensland 4111 Australia Northeast Institute of Geography and Agroeeology Chinese Aeademy of Seienees Changehun 130012 China

Coastal wetlands are characterized by complex patterns both in their geomorphlc and ecological teatures. Besides field observations, it is necessary to analyze the land cover of wetlands through the color infrared （CIR） aerial photography or remote sensing image. In this paper, we designed an evolving neural network classifier using variable string genetic algorithm （VGA） for the land cover classification of CIR aerial image. With the VGA, the classifier that we designed is able to evolve automatically the appropriate number of hidden nodes for modeling the neural network topology optimally and to find a near-optimal set of connection weights globally. Then, with backpropagation algorithm （BP）, it can find the best connection weights. The VGA-BP classifier, which is derived from hybrid algorithms mentioned above, is demonstrated on CIR images classification effectively. Compared with standard classifiers, such as Bayes maximum-likelihood classifier, VGA classifier and BP-MLP （multi-layer perception） classifier, it has shown that the VGA-BP classifier can have better performance on highly resolution land cover classification.

关键词： variable string genetic algorithm neural network pattern classification CIR image

A Scale Sequence Object-based Convolutional Neural Network(SS-OCNN)for crop classification from fine spatial resolution remotely sensed imagery

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International Journal of Digital Earth 2021年第11期14卷 1528-1546页

作者： Huapeng Li Ce zhang Yong zhang shuqing zhang Xiaohui Ding Peter M.Atkinson Chinese Academy of Sciences Northeast Institute of Geography and AgroecologyChangchunPeople’s Republic of China School of Electrical and Information Engineering Changchun Guanghua UniversityChangchunPeople’s Republic of China Lancaster Environment Centre Lancaster UniversityLancasterUK Guangzhou Institute of Geography GuangzhouPeople’s Republic of China

The highly dynamic nature of agro-ecosystems in space and time usually leads to high intra-class variance and low inter-class separability in the fine spatial resolution(FSR)remotely sensed *** makes traditional classifiers essentially relying on spectral information for crop mapping from FSR imagery an extremely challenging *** mine effectively the rich spectral and spatial information in FSR imagery,this paper proposed a Scale Sequence Object-based Convolutional Neural Network(SS-OCNN)that classifies images at the object level by taking segmented objects(crop parcels)as basic units of analysis,thus,ensuring that the boundaries between crop parcels are delineated *** segmented objects were subsequently classified using a CNN model integrated with an automatically generated scale sequence of input patch *** scale sequence can fuse effectively the features learned at different scales by transforming progressively the information extracted at small scales to larger *** effectiveness of the SS-OCNN was investigated using two heterogeneous agricultural areas with FSR SAR and optical imagery,*** results revealed that the SS-OCNN consistently achieved the most accurate classification *** SS-OCNN,thus,provides a new paradigm for crop classification over heterogeneous areas using FSR imagery,and has a wide application prospect.

关键词： CNNs multi-scale deep learning object-based mapping crop classification image classification

Integrating TM and Ancillary Geographical Data with Classification Trees for Land Cover Classification of Marsh Area

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Chinese Geographical Science 2009年第2期19卷 177-185页

作者： NA Xiaodong zhang shuqing zhang Huaiqing LI Xiaofeng YU Huan LIU Chunyue Northeast Institute of Geograpky and Agroecology Chinese Academy of Sciences Ckangchun 130012 China Graduate University of the Chinese Academy of Sciences Beijing 100049 China Institute of Resources Information Chinese Academy of Forestry Beijing 100091 China

The main objective of this research is to determine the capacity of land cover classification combining spec- tral and textural features of Landsat TM imagery with ancillary geographical data in wetlands of the Sanjiang Plain, Heilongjiang Province, China. Semi-variograms and Z-test value were calculated to assess the separability of grey-level co-occurrence texture measures to maximize the difference between land cover types. The degree of spatial autocorrelation showed that window sizes of 3×3 pixels and 11×11 pixels were most appropriate for Landsat TM im- age texture calculations. The texture analysis showed that co-occurrence entropy, dissimilarity, and variance texture measures, derived from the Landsat TM spectrum bands and vegetation indices provided the most significant statistical differentiation between land cover types. Subsequently, a Classification and Regression Tree (CART) algorithm was applied to three different combinations of predictors: 1) TM imagery alone (TM-only); 2) TM imagery plus image texture (TM+TXT model); and 3) all predictors including TM imagery, image texture and additional ancillary GIS in- formation (TM+TXT+GIS model). Compared with traditional Maximum Likelihood Classification (MLC) supervised classification, three classification trees predictive models reduced the overall error rate significantly. Image texture measures and ancillary geographical variables depressed the speckle noise effectively and reduced classification error rate of marsh obviously. For classification trees model making use of all available predictors, omission error rate was 12.90% and commission error rate was 10.99% for marsh. The developed method is portable, relatively easy to im- plement and should be applicable in other settings and over larger extents.

关键词： land cover classification classification trees Landsat TM ancillary geographical data marsh

Cross-Project Software Defect Prediction Based on SMOTE and Deep Canonical Correlation Analysis

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Computers, Materials & Continua 2024年第2期78卷 1687-1711页

作者： Xin Fan shuqing zhang Kaisheng Wu Wei Zheng Yu Ge School of Software Nanchang Hangkong UniversityNanchang330063China Software Testing and Evaluation Center Nanchang Hangkong UniversityNanchang330063China

Cross-Project Defect Prediction(CPDP)is a method that utilizes historical data from other source projects to train predictive models for defect prediction in the target ***,existing CPDP methods only consider linear correlations between features(indicators)of the source and target *** models are not capable of evaluating non-linear correlations between features when they exist,for example,when there are differences in data distributions between the source and target *** a result,the performance of such CPDP models is *** this paper,this paper proposes a novel CPDP method based on Synthetic Minority Oversampling Technique(SMOTE)and Deep Canonical Correlation Analysis(DCCA),referred to as *** Correlation Analysis(CCA)is employed to address the issue of non-linear correlations between features of the source and target projects.S-DCCA extends CCA by incorporating the MlpNet model for feature extraction from the *** redundant features are then eliminated by maximizing the correlated feature subset using the CCA loss ***,cross-project defect prediction is achieved through the application of the SMOTE data sampling *** Under Curve(AUC)and F1 scores(F1)are used as evaluation *** paper conducted experiments on 27 projects from four public datasets to validate the proposed *** results demonstrate that,on average,our method outperforms all baseline approaches by at least 1.2%in AUC and 5.5%in F1 *** indicates that the proposed method exhibits favorable performance characteristics.

关键词： Cross-project defect prediction deep canonical correlation analysis feature similarity