Edge-based intelligent fault diagnosis for centrifugal pumps in microbreweries

IF 2.3 3区工程技术 Q2 ENGINEERING, MECHANICAL

Flow Measurement and Instrumentation Pub Date : 2024-11-22 DOI:10.1016/j.flowmeasinst.2024.102730

Andre Luis Dias , Marcio Rafael Buzoli , Vinicius Rodrigues da Silva , Jean Carlos Rodrigues da Silva , Afonso Celso Turcato , Guilherme Serpa Sestito

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Abstract

The beer sector is a significant market worldwide and the number of small breweries is increasing. Centrifugal pumps are essential components for the proper functioning of the production system. However, failures in these equipment can be detected early by Intelligent Fault Diagnosis (IFD) Systems. In this context, this article aims to develop an IFD capable of detecting cavitation and dry-running faults. The proposed method explored the use of data provided by centrifugal pump drives, such as current, torque, and power factor. It was investigated two approaches: using the Shapley value as a feature selector and the Support Vector Machine (SVM) as the classifier, and applying the raw signal data to 1D Convolutional Neural Networks (CNN). The SVM-based model presented better results, with an accuracy of 100% for dry running and 99.3% for cavitation. The CNN-based model presented 97.4% and 80.2% respectively. It is also identified that torque is the most relevant variable.

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基于边缘的微型酿酒厂离心泵智能故障诊断

啤酒行业是全球重要的市场，小型啤酒厂的数量也在不断增加。离心泵是生产系统正常运行的重要组成部分。然而，智能故障诊断（IFD）系统可以及早发现这些设备的故障。在这种情况下，本文旨在开发一种能够检测气蚀和干运行故障的智能故障诊断系统。所提出的方法探索了如何使用离心泵驱动器提供的数据，如电流、扭矩和功率因数。研究了两种方法：使用 Shapley 值作为特征选择器和支持向量机 (SVM) 作为分类器，以及将原始信号数据应用于一维卷积神经网络 (CNN)。基于 SVM 的模型取得了更好的结果，对干运行的准确率为 100%，对气蚀的准确率为 99.3%。基于 CNN 的模型的准确率分别为 97.4% 和 80.2%。此外，还发现扭矩是最相关的变量。

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.