The Experts below are selected from a list of 93210 Experts worldwide ranked by ideXlab platform
Gang Xiao - One of the best experts on this subject based on the ideXlab platform.
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study on an improved hausdorff distance for multi Sensor image matching
2012Co-Authors: Jianming Wu, Zhongliang Jing, Zheng Wu, Yan Feng, Gang XiaoAbstract:Abstract A new modifying Hausdorff distance image matching algorithm was proposed in this paper. After the corners of two images was extracted using Harris corner detector, a kind of Hausdorff distance integrating points set coincidence numbers was presented to aim at the traditional Hausdorff distance. The accuracy of matching was improved by this modifying. Hausdorff distance coefficient matrix is calculating by corners neighborhood’s related matching. The initial matching point-pairs are obtained by the rule that the small coefficient is good matching. Finally the wrong matching point-pairs are deleted by the distance-ration invariant, the right matching point-pairs are acquired. Experimental results show that the proposed method can be easily and quickly to process the Multiple Sensor images.
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study on an improved hausdorff distance for multi Sensor image matching
2012Co-Authors: Jianming Wu, Zhongliang Jing, Zheng Wu, Yan Feng, Gang XiaoAbstract:Abstract A new modifying Hausdorff distance image matching algorithm was proposed in this paper. After the corners of two images was extracted using Harris corner detector, a kind of Hausdorff distance integrating points set coincidence numbers was presented to aim at the traditional Hausdorff distance. The accuracy of matching was improved by this modifying. Hausdorff distance coefficient matrix is calculating by corners neighborhood’s related matching. The initial matching point-pairs are obtained by the rule that the small coefficient is good matching. Finally the wrong matching point-pairs are deleted by the distance-ration invariant, the right matching point-pairs are acquired. Experimental results show that the proposed method can be easily and quickly to process the Multiple Sensor images.
Kaibo Liu - One of the best experts on this subject based on the ideXlab platform.
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integration of data level fusion model and kernel methods for degradation modeling and prognostic analysis
2018Co-Authors: Changyue Song, Kaibo Liu, Xi ZhangAbstract:To prevent unexpected failures of complex engineering systems, Multiple Sensors have been widely used to simultaneously monitor the degradation process and make inference about the remaining useful life in real time. As each of the Sensor signals often contains partial and dependent information, data-level fusion techniques have been developed that aim to construct a health index via the combination of Multiple Sensor signals. While the existing data-level fusion approaches have shown a promise for degradation modeling and prognostics, they are limited by only considering a linear fusion function. Such a linear assumption is usually insufficient to accurately characterize the complicated relations between Multiple Sensor signals and the underlying degradation process in practice, especially for complex engineering systems considered in this study. To address this issue, this study fills the literature gap by integrating kernel methods into the data-level fusion approaches to construct a health index for better characterizing the degradation process of the system. Through selecting a proper kernel function, the nonlinear relation between Multiple Sensor signals and the underlying degradation process can be captured. As a result, the constructed health index is expected to perform better in prognosis than existing data-level fusion methods that are based on the linear assumption. In fact, the existing data-level fusion models turn out to be only a special case of the proposed method. A case study based on the degradation signals of aircraft gas turbine engines is conducted and finally shows the developed health index by using the proposed method is insensitive for missing data and leads to an improved prognostic performance.
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statistical degradation modeling and prognostics of Multiple Sensor signals via data fusion a composite health index approach
2018Co-Authors: Changyue Song, Kaibo LiuAbstract:Nowadays Multiple Sensors are widely used to simultaneously monitor the degradation status of a unit. Because those Sensor signals are often correlated and measure different characteristics of the same unit, effective fusion of such a diverse “gene pool” is an important step to better understanding the degradation process and producing a more accurate prediction of the remaining useful life. To address this issue, this article proposes a novel data fusion method that constructs a composite Health Index (HI) via the combination of Multiple Sensor signals for better characterizing the degradation process. In particular, we formulate the problem as indirect supervised learning and leverage the quantile regression to derive the optimal fusion coefficient. In this way, the prognostic performance of the proposed method is guaranteed. To the best of our knowledge, this is the first article that provides the theoretical analysis of the data fusion method for degradation modeling and prognostics. Simulation studies are conducted to evaluate the proposed method in different scenarios. A case study on the degradation of aircraft engines is also performed, which shows the superior performance of our method over existing HI-based methods.
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Multiple Sensor data fusion for degradation modeling and prognostics under Multiple operational conditions
2016Co-Authors: Hao Yan, Kaibo Liu, Xi Zhang, Jianjun ShiAbstract:Due to the rapid advances in sensing and computing technology, Multiple Sensors have been widely used to simultaneously monitor the health status of an operation unit. This creates a data-rich environment, enabling an unprecedented opportunity to make better understanding and inference about the current and future behavior of the unit in real time. Depending on specific task requirements, a unit is often required to run under Multiple operational conditions, each of which may affect the degradation path of the unit differently. Thus, two fundamental challenges remain to be solved for effective degradation modeling and prognostic analysis: 1) how to leverage the dependent information among Multiple Sensor signals to better understand the health condition of the unit; and 2) how to model the effects of Multiple conditions on the degradation characteristics of the unit. To address these two issues, this paper develops a data fusion methodology that integrates the information from Multiple Sensors to construct a health index when the monitored unit runs under Multiple operational conditions. Our goal is that the developed health index provides a much better characterization of the health condition of the degraded unit, and, thus, leads to a better prediction of the remaining lifetime. Unlike other existing approaches, the developed data fusion model combines the fusion procedure and the degradation modeling under different operational conditions in a unified manner. The effectiveness of the proposed method is demonstrated in a case study, which involves a degradation dataset of aircraft gas turbine engines collected from 21 Sensors under six different operational conditions.
Jianming Wu - One of the best experts on this subject based on the ideXlab platform.
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study on an improved hausdorff distance for multi Sensor image matching
2012Co-Authors: Jianming Wu, Zhongliang Jing, Zheng Wu, Yan Feng, Gang XiaoAbstract:Abstract A new modifying Hausdorff distance image matching algorithm was proposed in this paper. After the corners of two images was extracted using Harris corner detector, a kind of Hausdorff distance integrating points set coincidence numbers was presented to aim at the traditional Hausdorff distance. The accuracy of matching was improved by this modifying. Hausdorff distance coefficient matrix is calculating by corners neighborhood’s related matching. The initial matching point-pairs are obtained by the rule that the small coefficient is good matching. Finally the wrong matching point-pairs are deleted by the distance-ration invariant, the right matching point-pairs are acquired. Experimental results show that the proposed method can be easily and quickly to process the Multiple Sensor images.
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study on an improved hausdorff distance for multi Sensor image matching
2012Co-Authors: Jianming Wu, Zhongliang Jing, Zheng Wu, Yan Feng, Gang XiaoAbstract:Abstract A new modifying Hausdorff distance image matching algorithm was proposed in this paper. After the corners of two images was extracted using Harris corner detector, a kind of Hausdorff distance integrating points set coincidence numbers was presented to aim at the traditional Hausdorff distance. The accuracy of matching was improved by this modifying. Hausdorff distance coefficient matrix is calculating by corners neighborhood’s related matching. The initial matching point-pairs are obtained by the rule that the small coefficient is good matching. Finally the wrong matching point-pairs are deleted by the distance-ration invariant, the right matching point-pairs are acquired. Experimental results show that the proposed method can be easily and quickly to process the Multiple Sensor images.
Ja Van Impe - One of the best experts on this subject based on the ideXlab platform.
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analysis of smearing out in contribution plot based fault isolation for statistical process control
2013Co-Authors: Pieter Van Den Kerkhof, Geert Gins, Jef Vanlae, Ja Van ImpeAbstract:Abstract This paper studies the smearing effect encountered in contribution plot based fault isolation, i.e., the influence of faulty variables on the contributions of non-faulty variables. Since the generation of contribution plots requires no a priori information about the detected disturbance (e.g., historical faulty data), it is a popular fault isolation technique in Statistical Process Control (SPC). However, Westerhuis et al. (2000) demonstrated that contributions suffer from fault smearing. As a consequence, variables unaffected by the fault may be highlighted and faulty variables obscured during the contribution analysis. This paper presents a thorough analysis of the smearing effect for three general contribution computation methods: complete decomposition, partial decomposition and reconstruction-based contributions. The analysis shows that (i) smearing is present in all three methods, (ii) smearing depends on the chosen number of principal components of the underlying PCA or PLS model and (iii) the extent of smearing increases for variables correlated in the training data for a well-chosen model order. The effect of smearing on the isolation performance of single and Multiple Sensor faults of various magnitudes is studied and illustrated using a simulation case study. The results indicate that correct isolation with contribution plots is not guaranteed for Multiple Sensor faults. Furthermore, contribution plots only outperform univariate fault isolation for single Sensor faults with small magnitudes. For Multiple Sensor faults, univariate fault isolation exhibits a significantly larger correct fault isolation rate. Based on the smearing analysis and the specific results for Sensor faults, the authors advise to use contributions only if a sound physical interpretation of the principal components is available. Otherwise multivariate detection followed by univariate fault isolation is recommended.
Radislav Smid - One of the best experts on this subject based on the ideXlab platform.
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quality based Multiple Sensor fusion in an industrial wireless Sensor network for mcm
2014Co-Authors: Ondrej Kreibich, Jan Neuzil, Radislav SmidAbstract:The early alert monitoring system for an effective scheduled maintenance strategy based on a wireless technology requires reliable transfer of the diagnostic information between the Sensor and the gateway. This paper presents an industrial wireless Sensor network (IWSN)-based machine condition monitoring (MCM) system capable of overcoming a false indication caused by temporary loss of data, signal interference, or invalid data. We use multiSensor fusion driven by a quality parameter, which is produced by each Sensor node according to the data history outliers and the actual state of the node. The fusion node provides a quality evaluation on its output as well. This novel approach enables the propagation of information about the uncertainty of a measured value from the source node to the sink node. Thus, potential degradation of acquired or transferred diagnostic information is minimized. Instead of raw data, the signal features are transferred, so that bandwidth savings are rapidly improved. The proposed concept was experimentally verified on real wireless Sensor network (WSN) hardware. The performance evaluated using the signal-to-noise ratio and false-alarm rate detection demonstrates the effectiveness of the proposed approach.
