Anomaly detection and prediction of sensors faults in a refinery using data mining techniques and fuzzy logic
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Like all manufacturing companies, refineries use many sensors to monitor and control the process of refining, therefore it is very crucial to detect any sensor faults or anomalies as early as possible, and to be able to replace or repair a sensor well in advance of any fault. Objective of this paper is to present a method for detecting anomalies in a sensor data, as well as to predict next occurance of a sensor failure. Data mining techniques to detect anomaly in sensor data and predict the occurrence of next faulty event were introduced. For anomaly detection, this research used MATLAB’s fuzzy logic toolbox tools to find clusters which uses subtractive fuzzy clustering algorithm and generates a model, a Sugeno-type fuzzy inference system. The same toolbox was used to evaluate the model with a promising result. To predict sensor fault, the original time series were used to create a new ‘derived time series’. Two prediction models known as ‘auto regressive integrated moving average’ and ‘autoregressive tree models’ were used against the new time series to predict next occurrence of sensor failure. The results oft hese models were compared. The model developed and introduced in this paper serves as an additional tool, which helps not only engineers and operators of oil refineries, but also other engineers of other disciplines to work more efficiently. Key words: Data mining, derived time series, sensor fault detection and prediction, fuzzy clustring, machine learning, oil refinery.
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The most frequently mentioned reasons for data quality issues in the reviewed literature are heterogeneous multiple data sources, sensor malfunctions, network problems, high sampling frequency, and cyber attacks for data tampering.
“The most mentioned reasons are heterogeneous multiple data sources [4,12,30,44,50], sensor malfunctions [15,31,35], network problems (connection failures, communication delays) [16,37,40], high sampling frequency [18], and cyber attacks for data tampering [7,31].”
From A Systematic Review of Data Quality in CPS and IoT for Industry 4.0 (2023) · cites this paper as [35]
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A Systematic Review of Data Quality in CPS and IoT for Industry 4.0
Goknil, A., Nguyen, P., Sen, S., Politaki, D., Niavis, H., Pedersen, K. J., Suyuthi, A., Anand, A., & Ziegenbein, A. (2023) · ACM Computing Surveys · doi:10.1145/3593043 · Cited by 68 · see citing works
- Authors
- Saybani, M. R.
- Corresponding author
- Mahmoud Reza Saybani
- ORCID
- Mahmoud Reza Saybani
- Venue
- Scientific Research and Essays
- Publisher
- Academic Journals
- Published
- 2011-11-16
- Volume
- 6
- Issue
- 27
- ISSN
- 1992-2248 (ISSN-L)
- OpenAlex
- W2200863436
- Cited by
- 7
- References
- 47
- Citations by year
- 2025: 1 · 2023: 1 · 2019: 2 · 2018: 1 · 2017: 2
- FWCI
- 0.00
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Scientific Research and Essays (publishedVersion)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
- Physical Sciences › Engineering › Control and Systems Engineering › Machine Fault Diagnosis Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Keywords
- sensor fault detection, anomaly detection, oil refinery, Takagi-Sugeno fuzzy model, ARIMA, process monitoring
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