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

共 50 条

[21] MapReduce Intrusion Detection System based on a Particle Swarm Optimization Clustering Algorithm
Aljarah, Ibrahim
Ludwig, Simone A.
2013 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 2013, : 955 - 962
[22] A novel modified particle swarm optimization algorithm with mutation for data clustering problem
Ratanavilisagul, Chiabwoot
2020 5TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND APPLICATIONS (ICCIA 2020), 2020, : 55 - 59
[23] Study of Different Approach to Clustering Data by Using the Particle Swarm Optimization Algorithm
Esmin, A. A. A.
Pereira, D. L.
de Araujo, F. P. A.
2008 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-8, 2008, : 1817 - 1822
[24] Automatic Data Clustering Using Hybrid Firefly Particle Swarm Optimization Algorithm
Agbaje, Moyinoluwa B.
Ezugwu, Absalom E.
Els, Rosanne
IEEE ACCESS, 2019, 7 : 184963 - 184984
[25] Industrial defect detection and location based on greedy particle swarm optimization algorithm
Tang, Yaorui
Yang, Bo
Peng, Hong
Luo, Xiaohui
INTERNATIONAL JOURNAL OF PARALLEL EMERGENT AND DISTRIBUTED SYSTEMS, 2024,
[26] An application of particle swarm optimization algorithm to clustering analysis
R. J. Kuo
M. J. Wang
T. W. Huang
Soft Computing, 2011, 15 : 533 - 542
[27] A Distributed Particle Swarm Optimization Algorithm for Distributed Clustering
Li, Zi-Xing
Guo, Xiao-Qi
Chen, Wei-Neng
Hu, Xiao-Min
PROCEEDINGS OF THE 2022 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE COMPANION, GECCO 2022, 2022, : 260 - 263
[28] Gaussian Kernel Particle Swarm Optimization Clustering Algorithm
Pei, Shengyu
Tong, Lang
2016 12TH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION, FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY (ICNC-FSKD), 2016, : 198 - 204
[29] An application of particle swarm optimization algorithm to clustering analysis
Kuo, R. J.
Wang, M. J.
Huang, T. W.
SOFT COMPUTING, 2011, 15 (03) : 533 - 542
[30] A hybrid particle swarm optimization algorithm for clustering analysis
Marinakis, Yannis
Marinaki, Magdalene
Matsatsinis, Nikolaos
DATA WAREHOUSING AND KNOWLEDGE DISCOVERY, PROCEEDINGS, 2007, 4654 : 241 - +

← 1 2 3 4 5 →