Industrial process monitoring using support vector data description: A systematic review and application for fault detection in multiphase flow system

IF 2.7 3区工程技术 Q2 ENGINEERING, MECHANICAL

Flow Measurement and Instrumentation Pub Date : 2025-08-20 DOI:10.1016/j.flowmeasinst.2025.103031

G. Bharathwaj, Amir Khurshid, S. Venkatavijayan, Ajaya Kumar Pani

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引用次数: 0

Abstract

In the era of Industry 4.0, machine learning based data-driven techniques are increasingly explored for industrial process monitoring. This article presents a review on application of support vector data description (SVDD) for industrial process monitoring followed by the design of SVDD model for fault detection in a multiphase flow system. In the review section, the basic technique, open design issues and a detailed survey on industrial applications are presented. In the application part, PRONTO benchmark multiphase flow dataset, is used to design SVDD model for detection of three faults: air leakage, air blockage and diverted flow. The Gaussian kernel parameter of the SVDD model is determined using particle swarm optimization (PSO) and the starting value for PSO, is obtained from literature provided analytical formula. Simulation of PSO-SVDD models shows promising results for fault detection in multiphase flow system.

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基于支持向量数据描述的工业过程监控：多相流系统故障检测的系统综述与应用

在工业4.0时代，基于机器学习的数据驱动技术被越来越多地用于工业过程监控。本文综述了支持向量数据描述（SVDD）在工业过程监测中的应用，并设计了用于多相流系统故障检测的支持向量数据描述模型。在回顾部分，介绍了基本技术，开放设计问题和工业应用的详细调查。在应用部分，利用PRONTO基准多相流数据集，设计SVDD模型检测漏气、堵塞和分流三种故障。采用粒子群优化（PSO）确定SVDD模型的高斯核参数，PSO的起始值由文献提供的解析公式得到。PSO-SVDD模型的仿真结果表明，PSO-SVDD模型在多相流系统故障检测中具有良好的应用前景。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Flow Measurement and Instrumentation 工程技术-工程：机械

CiteScore

4.30

自引率

13.60%

发文量

123

审稿时长

6 months

期刊介绍： Flow Measurement and Instrumentation is dedicated to disseminating the latest research results on all aspects of flow measurement, in both closed conduits and open channels. The design of flow measurement systems involves a wide variety of multidisciplinary activities including modelling the flow sensor, the fluid flow and the sensor/fluid interactions through the use of computation techniques; the development of advanced transducer systems and their associated signal processing and the laboratory and field assessment of the overall system under ideal and disturbed conditions. FMI is the essential forum for critical information exchange, and contributions are particularly encouraged in the following areas of interest: Modelling: the application of mathematical and computational modelling to the interaction of fluid dynamics with flowmeters, including flowmeter behaviour, improved flowmeter design and installation problems. Application of CAD/CAE techniques to flowmeter modelling are eligible. Design and development: the detailed design of the flowmeter head and/or signal processing aspects of novel flowmeters. Emphasis is given to papers identifying new sensor configurations, multisensor flow measurement systems, non-intrusive flow metering techniques and the application of microelectronic techniques in smart or intelligent systems. Calibration techniques: including descriptions of new or existing calibration facilities and techniques, calibration data from different flowmeter types, and calibration intercomparison data from different laboratories. Installation effect data: dealing with the effects of non-ideal flow conditions on flowmeters. Papers combining a theoretical understanding of flowmeter behaviour with experimental work are particularly welcome.