Fault degradation prognostic, which estimates the time before a failure occurs and process breakdowns, has been recognized as a key component in maintenance strategies nowadays. Fault degradation processes are, in gen...Fault degradation prognostic, which estimates the time before a failure occurs and process breakdowns, has been recognized as a key component in maintenance strategies nowadays. Fault degradation processes are, in general,slowly varying and can be modeled by autoregressive models. However, industrial processes always show typical nonstationary nature, which may bring two challenges: how to capture fault degradation information and how to model nonstationary processes. To address the critical issues, a novel fault degradation modeling and online fault prognostic strategy is developed in this paper. First, a fault degradation-oriented slow feature analysis(FDSFA) algorithm is proposed to extract fault degradation directions along which candidate fault degradation features are extracted. The trend ability assessment is then applied to select major fault degradation features. Second, a key fault degradation factor(KFDF) is calculated to characterize the fault degradation tendency by combining major fault degradation features and their stability weighting factors. After that, a time-varying regression model with temporal smoothness regularization is established considering nonstationary characteristics. On the basis of updating strategy, an online fault prognostic model is further developed by analyzing and modeling the prediction errors. The performance of the proposed method is illustrated with a real industrial process.展开更多
To learn from evolutionary experimental data points effectively,an evolutionary Gaussian mixture model based on constraint consistency(EGMM)is proposed and the corresponding method of parameter optimization is present...To learn from evolutionary experimental data points effectively,an evolutionary Gaussian mixture model based on constraint consistency(EGMM)is proposed and the corresponding method of parameter optimization is presented.Here,the Gaussian mixture model(GMM)is adopted to describe the data points,and the differences between the posterior probabilities of pairwise points under the current parameters are introduced to measure the temporal smoothness.Then,parameter optimization of EGMM can be realized by evolutionary clustering.Compared with most of the existing data analysis methods by evolutionary clustering,both the whole features and individual differences of data points are considered in the clustering framework of EGMM.It decreases the algorithm sensitivity to noises and increases the robustness of evaluated parameters.Experimental result shows that the clustering sequence really reflects the shift of data distribution,and the proposed algorithm can provide better clustering quality and temporal smoothness.展开更多
This paper addresses the challenge of scarcity and discontinuity in spatio-temporal observation data within oceanographic research.The primary goal is to develop a robust model capable of generating missing wave heigh...This paper addresses the challenge of scarcity and discontinuity in spatio-temporal observation data within oceanographic research.The primary goal is to develop a robust model capable of generating missing wave height data and enhancing the understanding of ocean wave behavior.We propose an end-to-end spatio-temporal sequence generation network based on convolutional U-Net,utilizing reanalyzed atmospheric datasets from the European Centre for Medium-Range Weather Forecasts(ECMWF).The model incorporates input features such as wind wave height,wave direction,mean wave period,and wind speed to generate significant wind wave heights in the South China Sea at specified times.Data smoothing was applied at intervals of 12,24,and 36 h under both moderate and extreme wave conditions,resulting in wave height data at 6-hour intervals.The findings demonstrate that the model effectively captures areas with high wave heights,outperforming traditional interpolation-based methods,especially as input data time intervals increase and wave conditions intensify.This approach offers a novel solution for generating missing spatio-temporal data,providing valuable insights into ocean wave behavior and supporting applications in ocean engineering.展开更多
Vision-based road detection is an important research topic in different areas of computer vision such as the autonomous navigation of mobile robots.In outdoor unstructured environments such as villages and deserts,the...Vision-based road detection is an important research topic in different areas of computer vision such as the autonomous navigation of mobile robots.In outdoor unstructured environments such as villages and deserts,the roads are usually not well-paved and have variant colors or texture distributions.Traditional region- or edge-based approaches,however,are effective only in specific environments,and most of them have weak adaptability to varying road types and appearances.In this paper we describe a novel top-down based hybrid algorithm which properly combines both region and edge cues from the images.The main difference between our proposed algorithm and previous ones is that,before road detection,an off-line scene classifier is efficiently learned by both low- and high-level image cues to predict the unstructured road model.This scene classification can be considered a decision process which guides the selection of the optimal solution from region- or edge-based approaches to detect the road.Moreover,a temporal smoothing mechanism is incorporated,which further makes both model prediction and region classification more stable.Experimental results demonstrate that compared with traditional region- and edge-based algorithms,our algorithm is more robust in detecting the road areas with diverse road types and varying appearances in unstructured conditions.展开更多
基金Project(U1709211) supported by NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization,ChinaProject(ICT2021A15) supported by the State Key Laboratory of Industrial Control Technology,Zhejiang University,ChinaProject(TPL2019C03) supported by Open Fund of Science and Technology on Thermal Energy and Power Laboratory,China。
文摘Fault degradation prognostic, which estimates the time before a failure occurs and process breakdowns, has been recognized as a key component in maintenance strategies nowadays. Fault degradation processes are, in general,slowly varying and can be modeled by autoregressive models. However, industrial processes always show typical nonstationary nature, which may bring two challenges: how to capture fault degradation information and how to model nonstationary processes. To address the critical issues, a novel fault degradation modeling and online fault prognostic strategy is developed in this paper. First, a fault degradation-oriented slow feature analysis(FDSFA) algorithm is proposed to extract fault degradation directions along which candidate fault degradation features are extracted. The trend ability assessment is then applied to select major fault degradation features. Second, a key fault degradation factor(KFDF) is calculated to characterize the fault degradation tendency by combining major fault degradation features and their stability weighting factors. After that, a time-varying regression model with temporal smoothness regularization is established considering nonstationary characteristics. On the basis of updating strategy, an online fault prognostic model is further developed by analyzing and modeling the prediction errors. The performance of the proposed method is illustrated with a real industrial process.
基金Supported by the National Natural Science Foundation of China(61202137)the Open Project Foundation of Information Technology Research Base of Civil Aviation Administration of China(CAAC-ITRB-201302)+1 种基金the University Natural Science Basic Research Project of Jiangsu Province(13KJB520004)the Fundamental Research Funds for the Central Universities(NS2012134)
文摘To learn from evolutionary experimental data points effectively,an evolutionary Gaussian mixture model based on constraint consistency(EGMM)is proposed and the corresponding method of parameter optimization is presented.Here,the Gaussian mixture model(GMM)is adopted to describe the data points,and the differences between the posterior probabilities of pairwise points under the current parameters are introduced to measure the temporal smoothness.Then,parameter optimization of EGMM can be realized by evolutionary clustering.Compared with most of the existing data analysis methods by evolutionary clustering,both the whole features and individual differences of data points are considered in the clustering framework of EGMM.It decreases the algorithm sensitivity to noises and increases the robustness of evaluated parameters.Experimental result shows that the clustering sequence really reflects the shift of data distribution,and the proposed algorithm can provide better clustering quality and temporal smoothness.
基金supported by the Fundamental Research Funds for the Central Universities.
文摘This paper addresses the challenge of scarcity and discontinuity in spatio-temporal observation data within oceanographic research.The primary goal is to develop a robust model capable of generating missing wave height data and enhancing the understanding of ocean wave behavior.We propose an end-to-end spatio-temporal sequence generation network based on convolutional U-Net,utilizing reanalyzed atmospheric datasets from the European Centre for Medium-Range Weather Forecasts(ECMWF).The model incorporates input features such as wind wave height,wave direction,mean wave period,and wind speed to generate significant wind wave heights in the South China Sea at specified times.Data smoothing was applied at intervals of 12,24,and 36 h under both moderate and extreme wave conditions,resulting in wave height data at 6-hour intervals.The findings demonstrate that the model effectively captures areas with high wave heights,outperforming traditional interpolation-based methods,especially as input data time intervals increase and wave conditions intensify.This approach offers a novel solution for generating missing spatio-temporal data,providing valuable insights into ocean wave behavior and supporting applications in ocean engineering.
文摘Vision-based road detection is an important research topic in different areas of computer vision such as the autonomous navigation of mobile robots.In outdoor unstructured environments such as villages and deserts,the roads are usually not well-paved and have variant colors or texture distributions.Traditional region- or edge-based approaches,however,are effective only in specific environments,and most of them have weak adaptability to varying road types and appearances.In this paper we describe a novel top-down based hybrid algorithm which properly combines both region and edge cues from the images.The main difference between our proposed algorithm and previous ones is that,before road detection,an off-line scene classifier is efficiently learned by both low- and high-level image cues to predict the unstructured road model.This scene classification can be considered a decision process which guides the selection of the optimal solution from region- or edge-based approaches to detect the road.Moreover,a temporal smoothing mechanism is incorporated,which further makes both model prediction and region classification more stable.Experimental results demonstrate that compared with traditional region- and edge-based algorithms,our algorithm is more robust in detecting the road areas with diverse road types and varying appearances in unstructured conditions.
