A New Particle Swarm Optimization Algorithm for Outlier Detection: Industrial Data Clustering in Wire Arc Additive Manufacturing

被引：31

作者：

Fang, Jingzhong ^{[1
]}

Wang, Zidong ^{[1
]}

Liu, Weibo ^{[1
]}

Lauria, Stanislao ^{[1
]}

Zeng, Nianyin ^{[2
]}

Prieto, Camilo ^{[3
]}

Sikstrom, Fredrik ^{[4
]}

Liu, Xiaohui ^{[1
]}

机构：

[1] Brunel Univ London, Dept Comp Sci, Uxbridge UB8 3PH, Middx, England

[2] Xiamen Univ, Dept Instrumental & Elect Engn, Xiamen 361005, Fujian, Peoples R China

[3] AIMEN Technol Ctr, E-36418 Pontevedra, Spain

[4] Univ West, Dept Engn Sci, S-46132 Trollhattan, Sweden

来源：

IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING | 2024年 / 21卷 / 02期

基金：

欧盟地平线“2020”; 中国国家自然科学基金;

关键词：

Clustering algorithms; Anomaly detection; Switches; Metals; Convergence; Wires; Particle swarm optimization; Industrial data analysis; outlier detection; fuzzy C-means; particle swarm optimization; wire arc additive manufacturing; RELIABILITY; PARAMETERS;

D O I：

10.1109/TASE.2022.3230080

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, a novel outlier detection method is proposed for industrial data analysis based on the fuzzy C-means (FCM) algorithm. An adaptive switching randomly perturbed particle swarm optimization algorithm (ASRPPSO) is put forward to optimize the initial cluster centroids of the FCM algorithm. The superiority of the proposed ASRPPSO is demonstrated over five existing PSO algorithms on a series of benchmark functions. To illustrate its application potential, the proposed ASRPPSO-based FCM algorithm is exploited in the outlier detection problem for analyzing the real-world industrial data collected from a wire arc additive manufacturing pilot line in Sweden. Experimental results demonstrate that the proposed ASRPPSO-based FCM algorithm outperforms the standard FCM algorithm in detecting outliers of real-world industrial data.

引用

页码：1244 / 1257

页数：14

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