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

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

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


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 article, 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 data set generated in this study to show the accuracy of the data-driven and model-based anomaly detection methods.

Original languageEnglish
Pages (from-to)10280-10287
Number of pages8
JournalIEEE Internet of Things Journal
Issue number13
Publication statusPublished - 1 Jul 2021
Externally publishedYes


  • Anomaly detection
  • Data mining
  • Data set generation
  • Edge processing
  • Fault detection
  • Industrial Internet of Things (IoT)
  • Machine learning (ML)
  • System identification
  • Water purification system


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