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

• URL: http://arxiv.org/abs/2009.03645v1
• Date: Tue, 8 Sep 2020 11:31:43 GMT
• Title: Detection of Anomalies and Faults in Industrial IoT Systems by Data Mining: Study of CHRIST Osmotron Water Purification System
• Authors: Mohammad Sadegh Sadeghi Garmaroodi, Faezeh Farivar, Mohammad Sayad Haghighi, Mahdi Aliyari Shoorehdeli, Alireza Jolfaei
• Abstract summary: This article is about industrial pharmaceutical systems and more specifically, water purification systems. 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.
• Score: 15.06694204377327
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Progressing from Anomaly Detection to Automated Log Labeling and Pioneering Root Cause Analysis [53.24804865821692]
  This study introduces a taxonomy for log anomalies and explores automated data labeling to mitigate labeling challenges. The study envisions a future where root cause analysis follows anomaly detection, unraveling the underlying triggers of anomalies.
  arXiv  Detail & Related papers  (2023-12-22T15:04:20Z)
• Anomaly Detection in Industrial Machinery using IoT Devices and Machine Learning: a Systematic Mapping [0.0]
  Internet of Things (IoT) has enabled the collection of large volumes of data from industrial machinery. However, the volume and complexity of data generated by the Internet of Things ecosystems make it difficult for humans to detect anomalies manually. Machine learning (ML) algorithms can automate anomaly detection in industrial machinery by analyzing generated data.
  arXiv  Detail & Related papers  (2023-07-28T20:58:00Z)
• Graph Neural Networks with Trainable Adjacency Matrices for Fault Diagnosis on Multivariate Sensor Data [69.25738064847175]
  It is necessary to consider the behavior of the signals in each sensor separately, to take into account their correlation and hidden relationships with each other. The graph nodes can be represented as data from the different sensors, and the edges can display the influence of these data on each other. It was proposed to construct a graph during the training of graph neural network. This allows to train models on data where the dependencies between the sensors are not known in advance.
  arXiv  Detail & Related papers  (2022-10-20T11:03:21Z)
• Deep Learning based pipeline for anomaly detection and quality enhancement in industrial binder jetting processes [68.8204255655161]
  Anomaly detection describes methods of finding abnormal states, instances or data points that differ from a normal value space. This paper contributes to a data-centric way of approaching artificial intelligence in industrial production.
  arXiv  Detail & Related papers  (2022-09-21T08:14:34Z)
• Anomaly Detection and Inter-Sensor Transfer Learning on Smart Manufacturing Datasets [6.114996271792091]
  In many cases, the goal of the smart manufacturing system is to rapidly detect (or anticipate) failures to reduce operational cost and eliminate downtime. This often boils down to detecting anomalies within the sensor date acquired from the system. The smart manufacturing application domain poses certain salient technical challenges. We show that predictive failure classification can be achieved, thus paving the way for predictive maintenance.
  arXiv  Detail & Related papers  (2022-06-13T17:51:24Z)
• Smart IoT-Biofloc water management system using Decision regression tree [0.0]
  Biofloc technology turns traditional farming into a sophisticated infrastructure that enables the utilization of leftover food by turning it into bacterial biomass. This article introduced a system that gathers data from sensors, store data in the cloud, analyses it using a machine learning model to predict the water condition, and provides real-time monitoring through an android app.
  arXiv  Detail & Related papers  (2021-12-05T14:12:07Z)
• Data-based Design of Inferential Sensors for Petrochemical Industry [0.0]
  Inferential (or soft) sensors are used in industry to infer the values of imprecisely and rarely measured (or completely unmeasured) variables from variables measured online. This work is focused on the design of inferential sensors for product composition of an industrial distillation column in two oil refinery units.
  arXiv  Detail & Related papers  (2021-06-25T08:48:50Z)
• Anomaly Detection through Transfer Learning in Agriculture and Manufacturing IoT Systems [4.193524211159057]
  In this paper, we analyze data from sensors deployed in an agricultural farm with data from seven different kinds of sensors, and from an advanced manufacturing testbed with vibration sensors. We show how in these two application domains, predictive failure classification can be achieved, thus paving the way for predictive maintenance.
  arXiv  Detail & Related papers  (2021-02-11T02:37:27Z)
• Real-time detection of uncalibrated sensors using Neural Networks [62.997667081978825]
  An online machine-learning based uncalibration detector for temperature, humidity and pressure sensors was developed. The solution integrates an Artificial Neural Network as main component which learns from the behavior of the sensors under calibrated conditions. The obtained results show that the proposed solution is able to detect uncalibrations for deviation values of 0.25 degrees, 1% RH and 1.5 Pa, respectively.
  arXiv  Detail & Related papers  (2021-02-02T15:44:39Z)
• No Need to Know Physics: Resilience of Process-based Model-free Anomaly Detection for Industrial Control Systems [95.54151664013011]
  We present a novel framework to generate adversarial spoofing signals that violate physical properties of the system. We analyze four anomaly detectors published at top security conferences.
  arXiv  Detail & Related papers  (2020-12-07T11:02:44Z)
• Adaptive Anomaly Detection for IoT Data in Hierarchical Edge Computing [71.86955275376604]
  We propose an adaptive anomaly detection approach for hierarchical edge computing (HEC) systems to solve this problem. We design an adaptive scheme to select one of the models based on the contextual information extracted from input data, to perform anomaly detection. We evaluate our proposed approach using a real IoT dataset, and demonstrate that it reduces detection delay by 84% while maintaining almost the same accuracy as compared to offloading detection tasks to the cloud.
  arXiv  Detail & Related papers  (2020-01-10T05:29:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.