论文标题

通过数据挖掘检测工业物联网系统中的异常和故障:基督渗透净水系统的研究

Detection of Anomalies and Faults in Industrial IoT Systems by Data Mining: Study of CHRIST Osmotron Water Purification System

论文作者

Garmaroodi, Mohammad Sadegh Sadeghi, Farivar, Faezeh, Haghighi, Mohammad Sayad, Shoorehdeli, Mahdi Aliyari, Jolfaei, Alireza

论文摘要

工业4.0将使制造过程变得更加聪明,但是这种智能需要更多的环境意识,而在工业互联网的情况下,它是在传感器的帮助下实现的。本文涉及工业药物系统,更具体地说是净水系统。具有一定电导率的纯净水是许多药品中的重要成分。几乎每个制药公司都有一个净水单元作为相互依存系统的一部分。在边缘的早期发现故障可以大大降低维护成本并提高安全性和产出质量,从而导致产生更好的药物。在本文的帮助下,借助几种传感器和数据挖掘方法,为基督渗透净水器构建了异常检测系统。这是一项对从Sinadarou Labs公司收集的现实数据的实践研究。数据收集是通过在系统大修之前和之后使用六个传感器在两周的间隔内完成的。这为我们提供了正常和错误的操作样本。鉴于数据,我们提出了两种构建边缘故障检测系统的异常检测方法。第一种方法是基于监督的学习和数据挖掘,例如通过支持向量机。但是,由于我们无法收集所有可能的故障数据,因此基于正常系统识别提出了一种异常检测方法,该方法通过人工神经网络对系统组件进行建模。对本研究中生成的数据集进行了广泛的实验,以显示数据驱动和基于模型的异常检测方法的准确性。

Industry 4.0 will make manufacturing processes smarter but this smartness requires more environmental awareness, which in case of Industrial Internet of Things, is realized by the help of sensors. This article is about industrial pharmaceutical systems and more specifically, water purification systems. Purified water which has certain conductivity is an important ingredient in many pharmaceutical products. Almost every pharmaceutical company has a water purifying unit as a part of its interdependent systems. Early detection of faults right at the edge can significantly decrease maintenance costs and improve safety and output quality, and as a result, lead to the production of better medicines. In this paper, with the help of a few sensors and data mining approaches, an anomaly detection system is built for CHRIST Osmotron water purifier. This is a practical research with real-world data collected from SinaDarou Labs Co. Data collection was done by using six sensors over two-week intervals before and after system overhaul. This gave us normal and faulty operation samples. Given the data, we propose two anomaly detection approaches to build up our edge fault detection system. The first approach is based on supervised learning and data mining e.g. by support vector machines. However, since we cannot collect all possible faults data, an anomaly detection approach is proposed based on normal system identification which models the system components by artificial neural networks. Extensive experiments are conducted with the dataset generated in this study to show the accuracy of the data-driven and model-based anomaly detection methods.

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