A Novel Decentralized Weighted ReliefF-PCA Method for Fault Detection

Yang, Ying; Chen, Xiangming; Zhang, Yue; Liu, Xiaozhi

doi:10.1109/access.2019.2943024

Cited by 5 publications

(4 citation statements)

References 30 publications

(29 reference statements)

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“…The total distance is simply the sum of distances over all features (Manhattan distance). [27] The specific steps of ReliefF algorithm are shown in Table 1 and described as the pseudo code.…”

Section: B the Relieff Algorithmmentioning

confidence: 99%

“…In the establishment of the fault monitoring model based on the DRRSFA method, the dynamic process order determined by the Akaike Information Standard (AIC) is 6, so the time delay q = 6 is selected of the heat treatment furnace process. The Hankel matrix can be constructed by Equation (27) and (28), where the number of columns N is 989 and the number of rows mq is 216. Then through the projection transformation, we obtain 216 slow features, which are put into ReliefF algorithm to get PCs.…”

Section: B Case Study Of the Actual Production Process Datamentioning

confidence: 99%

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A Novel Process Monitoring Method Based on Dynamic Related ReliefF-SFA Method

Xiaozhi

Zhang

et al. 2020

IEEE Access

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This paper proposes a condition monitoring method based on Dynamic Related ReliefF-SFA (DRRSFA), which solves the problem of huge variable dimensions and strong autocorrelation in process monitoring. First, the samples are mapped into a new space, and the slow projection components (SPCs) of the samples are calculated. The SPCs not only considers the autocorrelation between the variables, but also characterizes the inherent properties of the whole system. Second, the feature selection algorithm ReliefF is used to select the more weighted features, which is called the principal components(PCs) to achieve dimensionality reduction. Then the corresponding statistics and control limits are calculated based on the obtained PCs. Finally, the process monitoring using the proposed algorithm is performed by testing a numerical example and the actual production process data, and the results show the effectiveness of the proposed method. INDEX TERMS Process monitoring, slow feature analysis, slow projection components, ReliefF algorithm, projection components.

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Section: B the Relieff Algorithmmentioning

confidence: 99%

Section: B Case Study Of the Actual Production Process Datamentioning

confidence: 99%

A Novel Process Monitoring Method Based on Dynamic Related ReliefF-SFA Method

Xiaozhi

Zhang

et al. 2020

IEEE Access

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“…Remarks: The algorithms of Kmeans, AC and NMI can be found on the homepage of 4 Deng Cai. The algorithm of MAP can be found on the 5 github.…”

Section: ) Clustering and Retrieval Evaluation Indicesmentioning

confidence: 99%

“…Principal Components Analysis [1] (PCA), Linear Discriminant Analysis [2] and Locality Preserving Projections [3]) were proposed to learn an effective subspace from the high-dimensional data. They were improved and applied to various areas including fault detection [4], image denoising [5], classification [6] and clustering [7]. Especially, Saeed et al [8] proposed to utilize the clustering methods to analyze the performance of the Complaint Audit Module.…”

Section: Introductionmentioning

confidence: 99%

Binary Codes Based on Non-Negative Matrix Factorization for Clustering and Retrieval

et al. 2020

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Traditional non-negative matrix factorization methods cannot learn the subspace from the high-dimensional data space composed of binary codes. One hopes to discover a compact parts-based representation composed of binary codes, which can uncover the intrinsic information and simultaneously respect the geometric structure of the original data. For this purpose, we introduce discrete hashing methods and propose a novel non-negative matrix factorization to generate binary codes from the original data. In this paper, we construct an affinity graph to encode the geometrical structure of the original data, and the learned binary code subspace achieved by matrix factorization respects the structure. The proposed problem can be formulated as a mixed integer optimization problem. Therefore, we transform it into several sub-problems including an integer optimization problem, two convex problems with the non-negative constraint and a quadratic programming problem. Optimizing each sub-problem alternately until we achieve a local optimal solution. Image clustering and retrieval on image datasets show the excellent performance of our method in comparison to other dimensional reduction methods.

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