Phase I non-linear profiles monitoring using a modified Hausdorff distance algorithm and clustering analysis

Nie, Bin; Liu, Diqing; Liu, Xiaohui; Ye, Wenjing

doi:10.1108/ijqrm-01-2020-0001

Cited by 3 publications

(1 citation statement)

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“…(domain distance). Inspired by the literature (Vavpetic and Zagar, 2021; Ryu and Kamata, 2021; Nie et al , 2021), the authors adopt the Hausdorff distance to calculate the domain distance between point-domains. Assume that a point-domain D 1 = { d 1 1 , d 1 2 , d 1 3 , …, d 1 i } and the other point-domain D 2 = { d 2 1 , d 2 2 , d 2 3 , …, d 2 j }, where d 1 i and d 2 j denote the two different points and i and j denote the serial number of data points in D 1 and D 2, respectively.…”

Section: The Proposed Clustering Methodsmentioning

confidence: 99%

TMsDP: two-stage density peak clustering based on multi-strategy optimization

Hao

2022

DTA

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PurposeThe density peak clustering algorithm (DP) is proposed to identify cluster centers by two parameters, i.e. ρ value (local density) and δ value (the distance between a point and another point with a higher ρ value). According to the center-identifying principle of the DP, the potential cluster centers should have a higher ρ value and a higher δ value than other points. However, this principle may limit the DP from identifying some categories with multi-centers or the centers in lower-density regions. In addition, the improper assignment strategy of the DP could cause a wrong assignment result for the non-center points. This paper aims to address the aforementioned issues and improve the clustering performance of the DP.Design/methodology/approachFirst, to identify as many potential cluster centers as possible, the authors construct a point-domain by introducing the pinhole imaging strategy to extend the searching range of the potential cluster centers. Second, they design different novel calculation methods for calculating the domain distance, point-domain density and domain similarity. Third, they adopt domain similarity to achieve the domain merging process and optimize the final clustering results.FindingsThe experimental results on analyzing 12 synthetic data sets and 12 real-world data sets show that two-stage density peak clustering based on multi-strategy optimization (TMsDP) outperforms the DP and other state-of-the-art algorithms.Originality/valueThe authors propose a novel DP-based clustering method, i.e. TMsDP, and transform the relationship between points into that between domains to ultimately further optimize the clustering performance of the DP.

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Section: The Proposed Clustering Methodsmentioning

confidence: 99%

TMsDP: two-stage density peak clustering based on multi-strategy optimization

Hao

2022

DTA

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Online monitoring of profiles via RVC model with an application to industrial busbar

Zhang,

He,

et al. 2024

Quality Technology & Quantitative Management

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Phase I Analysis of Nonlinear Profiles Using Anomaly Detection Techniques

Cheng

Chen

2023

Applied Sciences

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In various industries, the process or product quality is evaluated by a functional relationship between a dependent variable y and one or a few input variables x, expressed as y=fx. This relationship is called a profile in the literature. Recently, profile monitoring has received a lot of research attention. In this study, we formulated profile monitoring as an anomaly-detection problem and proposed an outlier-detection procedure for phase I nonlinear profile analysis. The developed procedure consists of three key processes. First, we obtained smoothed nonlinear profiles using the spline smoothing method. Second, we proposed a method for estimating the proportion of outliers in the dataset. A distance-based decision function was developed to identify potential outliers and provide a rough estimate of the contamination rate. Finally, PCA was used as a dimensionality reduction method. An outlier-detection algorithm was then employed to identify outlying profiles based on the estimated contamination rate. The algorithms considered in this study included Local Outlier Factor (LOF), Elliptic Envelope (EE), and Isolation Forest (IF). The proposed procedure was evaluated using a nonlinear profile that has been studied by various researchers. We compared various competing methods based on commonly used metrics such as type I error, type II error, and F2 score. Based on the evaluation metrics, our experimental results indicate that the performance of the proposed method is better than other existing methods. When considering the smallest and hardest-to-detect variation, the LOF algorithm, with the contamination rate determined by the method proposed in this study, achieved type I errors, type II errors, and F2 scores of 0.049, 0.001, and 0.951, respectively, while the performance metrics of the current best method were 0.081, 0.015, and 0.899, respectively.

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Phase I non-linear profiles monitoring using a modified Hausdorff distance algorithm and clustering analysis

Cited by 3 publications

References 34 publications

TMsDP: two-stage density peak clustering based on multi-strategy optimization

TMsDP: two-stage density peak clustering based on multi-strategy optimization

Online monitoring of profiles via RVC model with an application to industrial busbar

Phase I Analysis of Nonlinear Profiles Using Anomaly Detection Techniques

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