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A New Particle Swarm Optimization Algorithm for Outlier Detection: Industrial Data Clustering inWire Arc Additive Manufacturing
Department of Computer Science, Brunel University London, Middlesex, UB8 3PH Uxbridge (GBR).
Department of Computer Science, Brunel University London, Middlesex, UB8 3PH Uxbridge (GBR).ORCID iD: 0000-0002-9576-7401
Department of Computer Science, Brunel University London, Middlesex, UB8 3PH Uxbridge (GBR).ORCID iD: 0000-0002-8169-3261
Department of Computer Science, Brunel University London, Middlesex, UB8 3PH Uxbridge (GBR).ORCID iD: 0000-0003-1954-1547
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2023 (English)In: IEEE Transactions on Automation Science and Engineering, ISSN 1545-5955, E-ISSN 1558-3783, p. 1-14Article in journal (Refereed) Epub ahead of print
Abstract [en]

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 out performs the standard FCM algorithm in detecting outliers of real-world industrial data.

Note to Practitioners

Electric arc (which is governed by the current and arc voltage) plays a significant role in monitoring the operating status of the wire arc additive manufacturing (WAAM) process. The nominal periodic current and voltage may occasionally change abruptly due to anomalies (such asarc instability, unstable metal transfer, geometrical deviations, and surface contaminations), which would affect the quality of the fabricated component. This paper focuses on detecting possible anomalies by analyzing the current and voltage during the WAAM process. A novel clustering-based outlier detection method is proposed for anomaly detection where abnormal and normal instances are categorized into two separate clusters. A new particle swarm optimization algorithm is put forward to optimize the initial cluster centroid so as to improve the detection accuracy. The proposed outlier detection method is applied to real-world data collected from a WAAM pilot line for detecting abnormal instances. Experimental results demonstrate the effectiveness of the proposed outlier detection method. The proposed outlier detection method can be applied to other industrial applications including electrical engineering, mechanical engineering and medical engineering. In the future, we aim to develop an online outlier detection system based on the proposed method for real-time for anomaly detection and defect prediction.

Place, publisher, year, edition, pages
2023. p. 1-14
Keywords [en]
Industrial data analysis, outlier detection, fuzzy C-means, particle swarm optimization, wire arc additive manufacturing
National Category
Manufacturing, Surface and Joining Technology
Research subject
Production Technology
Identifiers
URN: urn:nbn:se:hv:diva-19942DOI: 10.1109/TASE.2022.3230080ISI: 000910587300001Scopus ID: 2-s2.0-85147228838OAI: oai:DiVA.org:hv-19942DiVA, id: diva2:1837899
Available from: 2024-02-15 Created: 2024-02-15 Last updated: 2024-04-12

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Sikström, Fredrik

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Wang, ZidongLiu, WeiboLauria, StanislaoZeng, NianyinSikström, Fredrik
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