Global–Local Structure Analysis Model and Its Application for Fault Detection and Identification

Zhang, Muguang; Ge, Zhiqiang; Fu, Ruowei

doi:10.1021/ie102564d

Cited by 151 publications

(145 citation statements)

References 33 publications

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“…Thus, the parameter η 1 should be selected to balance the scale issue. Inspired by, 46) the scale of J x and J y can be defined as: (15) In summary, the steps to select the relevant samples for construction of a JLSSVR model can be described as follows:…”

Section: )mentioning

confidence: 99%

Adaptive Weighting Just-in-Time-Learning Quality Prediction Model for an Industrial Blast Furnace

Chen

Liu

2017

ISIJ Int.

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Development of accurate soft sensors for online quality prediction (e.g., silicon content) in an industrial blast furnace is a difficult task. A novel just-in-time-learning (JITL) prediction approach using adaptive feature-weighting for similar samples is developed. First, a dual-objective joint-optimization framework is proposed to introduce both input and output information into the model. Then, a suitable similarity criterion with feature weighting strategy is formulated, which is not considered in conventional JITL methods. Moreover, the trade-off parameter in the joint-optimization problem can be chosen automatically, without the time-consuming cross-validation procedure. The proposed method is applied to online predict the silicon content in an industrial blast furnace in China. Compared with other JITL-based soft sensors, better prediction performance has been obtained.

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Section: )mentioning

confidence: 99%

Adaptive Weighting Just-in-Time-Learning Quality Prediction Model for an Industrial Blast Furnace

Chen

Liu

2017

ISIJ Int.

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“…In order to effectively solve a problem, we may have to further ask whether an approximate solution is enough, whether can make use of a randomized, and whether to allow false positives and false negative. Computational thinking is through reduction, embedded, transformation and simulation methods, such as a question seems to be difficult to interpret as we know how to solve the problem [4][5]. In general, the characteristics of the computational thinking is shown in figure 1.…”

Section: The Characteristics Of Computational Thinkingmentioning

confidence: 99%

The Study on Features of Computational Thinking and Its Common Operation Mode Methods

Zhong¹,

Liao²

2015

Advances in Intelligent Systems Research

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Abstract. The computational thinking is the current international computer widely attention an important concept, is also the current computer education need to key research important topic. Computer industry at home and abroad in the field of philosophy and social science and the general scholars on this subject has carried on the extensive research and discussion. In this paper, the formation and development of computational thinking process has carried on the comprehensive analysis, to study the forming characteristics of computational thinking, focus on key content, summarizes the common patterns of computational thinking in combination with the existing research results and the research direction and its computational thinking is prospected in the face of challenges.

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“…However, the features extracted from the local structure of the data can also represent the different aspects of the data. The loss of the important information may have impact on dimension reduction and monitoring result [6].…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Model for Condition Monitoring and Fault Detection Based on Nonlocal Kernel Orthogonal Preserving Embedding

She

Tian

Liang

et al. 2018

Shock and Vibration

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The dimension reduction methods have been proved powerful and practical to extract latent features in the signal for process monitoring. A linear dimension reduction method called nonlocal orthogonal preserving embedding (NLOPE) and its nonlinear form named nonlocal kernel orthogonal preserving embedding (NLKOPE) are proposed and applied for condition monitoring and fault detection. Different from kernel orthogonal neighborhood preserving embedding (KONPE) and kernel principal component analysis (KPCA), the NLOPE and NLKOPE models aim at preserving global and local data structures simultaneously by constructing a dual-objective optimization function. In order to adjust the trade-off between global and local data structures, a weighted parameter is introduced to balance the objective function. Compared with KONPE and KPCA, NLKOPE combines both the advantages of KONPE and KPCA, and NLKOPE is also more powerful in extracting potential useful features in nonlinear data set than NLOPE. For the purpose of condition monitoring and fault detection, monitoring statistics are constructed in feature space. Finally, three case studies on the gearbox and bearing test rig are carried out to demonstrate the effectiveness of the proposed nonlinear fault detection method.

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Global–Local Structure Analysis Model and Its Application for Fault Detection and Identification

Cited by 151 publications

References 33 publications

Adaptive Weighting Just-in-Time-Learning Quality Prediction Model for an Industrial Blast Furnace

Adaptive Weighting Just-in-Time-Learning Quality Prediction Model for an Industrial Blast Furnace

The Study on Features of Computational Thinking and Its Common Operation Mode Methods

Nonlinear Model for Condition Monitoring and Fault Detection Based on Nonlocal Kernel Orthogonal Preserving Embedding

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