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Industry 4.0 will make manufacturing processes smarter but this smartnessrequires more environmental awareness, which in case of Industrial Internet ofThings, is realized by the help of sensors. This article is about industrialpharmaceutical systems and more specifically, water purification systems.Purified water which has certain conductivity is an important ingredient inmany pharmaceutical products. Almost every pharmaceutical company has a waterpurifying unit as a part of its interdependent systems. Early detection offaults right at the edge can significantly decrease maintenance costs andimprove safety and output quality, and as a result, lead to the production ofbetter medicines. In this paper, with the help of a few sensors and data miningapproaches, an anomaly detection system is built for CHRIST Osmotron waterpurifier. This is a practical research with real-world data collected fromSinaDarou Labs Co. Data collection was done by using six sensors over two-weekintervals before and after system overhaul. This gave us normal and faultyoperation samples. Given the data, we propose two anomaly detection approachesto build up our edge fault detection system. The first approach is based onsupervised learning and data mining e.g. by support vector machines. However,since we cannot collect all possible faults data, an anomaly detection approachis proposed based on normal system identification which models the systemcomponents by artificial neural networks. Extensive experiments are conductedwith the dataset generated in this study to show the accuracy of thedata-driven and model-based anomaly detection methods.

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