推进旋转机械振动特征分类的架构框架

IF 1.9 3区 工程技术 Q3 ENGINEERING, MANUFACTURING
Cole Yorston, Cheng Chen, Jaime Camelio
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引用次数: 0

摘要

数据驱动的预测性维护技术的进步极大地改进了旋转机械的数字孪生应用,为智能制造挑战提供了强大的解决方案。这些改进至关重要,因为设备故障会对维护计划和运营造成广泛而昂贵的干扰。由于精度和可靠性在生产流程中至关重要,未被发现的运行频率波动会迅速升级为完全的部件故障,导致长时间的维修和生产力损失。本研究探讨了集成数据流管道,特别是通过西门子的 MindSphere,以实现持续的预测性维护并加强数据采集和管理。特别是,为了比较结果,我们使用支持向量机 (SVM)、神经网络 (NN) 和 K-Nearest Neighbor (KNN) 方法对正常运行、质量平衡、旋转不平衡和机械松动等条件进行了分类。我们的结果凸显了组合技术在收集和诊断振动特征方面的功效,从而实现了主动维护。为了对各种故障特征进行分类,我们提出了一个解释时间序列和频率相关数据的框架,以确定故障类型。这项研究体现了如何将数据驱动方法与数字孪生技术相结合,从而提高状态监测的准确性和可靠性。此外,我们还利用应用程序接口(API)配置,为旋转机械的诊断引入了基于云的架构,并开发了用于流式传输和可视化分类数据的实时仪表板,以促进即时和明智的决策。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Advancing architectural frameworks for vibration signature classification in rotating machinery
Advancements in data-driven predictive maintenance have significantly improved digital twin applications for rotating machinery, offering robust solutions for smart manufacturing challenges. These improvements are crucial since equipment failures can cause extensive and costly disruptions to both maintenance schedules and operations. As precision and reliability are critical in production processes, undetected fluctuations in operating frequencies can swiftly escalate to complete part failure, leading to prolonged repairs and productivity loss. This study explores an integrated dataflow pipeline, specifically through Siemens’ MindSphere, to enable continuous predictive maintenance and enhance data acquisition and management. Particularly, conditions such as normal operation, mass balance, rotating imbalance, and mechanical looseness are classified using support vector machine (SVM), neural network (NN), and K-Nearest Neighbor (KNN) methods for the purpose of comparing results. Our results highlight the efficacy of ensemble techniques in collecting and diagnosing vibration signatures, thereby enabling proactive maintenance. To classify various failure signatures, we have proposed a framework to interpret time-series and frequency-dependent data for determining failure types. This research exemplifies how merging data-driven methods with digital twin can improve the accuracy and reliability of condition monitoring. Additionally, we introduce a cloud-based architecture for the diagnosis of rotating machinery, utilizing Application Programming Interface (API) configurations, and develop a real-time dashboard for streaming and visualizing classified data, fostering immediate and informed decision-making.
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来源期刊
CiteScore
5.10
自引率
30.80%
发文量
167
审稿时长
5.1 months
期刊介绍: Manufacturing industries throughout the world are changing very rapidly. New concepts and methods are being developed and exploited to enable efficient and effective manufacturing. Existing manufacturing processes are being improved to meet the requirements of lean and agile manufacturing. The aim of the Journal of Engineering Manufacture is to provide a focus for these developments in engineering manufacture by publishing original papers and review papers covering technological and scientific research, developments and management implementation in manufacturing. This journal is also peer reviewed. Contributions are welcomed in the broad areas of manufacturing processes, manufacturing technology and factory automation, digital manufacturing, design and manufacturing systems including management relevant to engineering manufacture. Of particular interest at the present time would be papers concerned with digital manufacturing, metrology enabled manufacturing, smart factory, additive manufacturing and composites as well as specialist manufacturing fields like nanotechnology, sustainable & clean manufacturing and bio-manufacturing. Articles may be Research Papers, Reviews, Technical Notes, or Short Communications.
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