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Journal article
A method of sensor fault detection and identification
Journal of process control, v 15(3), pp 321-339
2005
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
A method of Bayesian belief network (BBN)-based sensor fault detection and identification is presented. It is applicable to processes operating in transient or at steady-state. A single-sensor BBN model with adaptable nodes is used to handle cases in which process is in transient. The single-sensor BBN model is used as a building block to develop a multi-stage BBN model for all sensors in the process under consideration. In the context of BBN, conditional probability data represents correlation between process measurable variables. For a multi-stage BBN model, the conditional probability data should be available at each time instant during transient periods. This requires generating and processing a massive data bank that reduces computational efficiency. This paper presents a method that reduces the size of the required conditional probability data to one set. The method improves the computational efficiency without sacrificing detection and identification effectiveness. It is applicable to model- and data-driven techniques of generating conditional probability data. Therefore, there is no limitation on the source of process information. Through real-time operation and simulation of two processes, the application and performance of the proposed BBN method are shown. Detection and identification of different sensor fault types (bias, drift and noise) are presented. For one process, a first-principles model is used to generate the conditional probability data, while for the other, real-time process data (measurements) are used.
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Details
- Title
- A method of sensor fault detection and identification
- Creators
- Nasir Mehranbod - Drexel UniversityMasoud Soroush - Drexel UniversityChanin Panjapornpon - Drexel University
- Publication Details
- Journal of process control, v 15(3), pp 321-339
- Publisher
- Elsevier
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Chemical and Biological Engineering
- Web of Science ID
- WOS:000227315700007
- Scopus ID
- 2-s2.0-10444224682
- Other Identifier
- 991019168962604721
UN Sustainable Development Goals (SDGs)
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Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Automation & Control Systems
- Engineering, Chemical