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用于预见来源不明故障的监控与数据采集（SCADA）系统中的传感器数据分析

Sensors Data Analysis in Supervisory Control and Data Acquisition (SCADA) Systems to Foresee Failures with an Undetermined Origin.

作者信息

Maseda F Javier, López Iker, Martija Itziar, Alkorta Patxi, Garrido Aitor J, Garrido Izaskun

机构信息

Automatic Control Group (ACG), Institute of Research and Development of Processes, Faculty of Engineering, University of the Basque Country (UPV/EHU), 48013 Bilbao, Spain.

Intenance, RDT Company, 48100 Munguia, Spain.

出版信息

Sensors (Basel). 2021 Apr 14;21(8):2762. doi: 10.3390/s21082762.

DOI:10.3390/s21082762
PMID:33919787
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8070775/
Abstract

This paper presents the design and implementation of a supervisory control and data acquisition (SCADA) system for automatic fault detection. The proposed system offers advantages in three areas: the prognostic capacity for preventive and predictive maintenance, improvement in the quality of the machined product and a reduction in breakdown times. The complementary technologies, the Industrial Internet of Things (IIoT) and various machine learning (ML) techniques, are employed with SCADA systems to obtain the objectives. The analysis of different data sources and the replacement of specific digital sensors with analog sensors improve the prognostic capacity for the detection of faults with an undetermined origin. Also presented is an anomaly detection algorithm to foresee failures and to recognize their occurrence even when they do not register as alarms or events. The improvement in machine availability after the implementation of the novel system guarantees the accomplishment of the proposed objectives.

摘要

本文介绍了一种用于自动故障检测的监控与数据采集（SCADA）系统的设计与实现。所提出的系统在三个方面具有优势：预防性和预测性维护的预后能力、加工产品质量的提高以及故障时间的减少。互补技术，即工业物联网（IIoT）和各种机器学习（ML）技术，与SCADA系统一起使用以实现这些目标。对不同数据源的分析以及用模拟传感器替代特定数字传感器，提高了对来源不明故障的检测预后能力。还提出了一种异常检测算法，以预见故障并识别其发生，即使它们未注册为警报或事件。新系统实施后机器可用性的提高保证了所提出目标的实现。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e148/8070775/b531a2528587/sensors-21-02762-g011.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e148/8070775/22c65d960746/sensors-21-02762-g008.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e148/8070775/b531a2528587/sensors-21-02762-g011.jpg

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