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Journal article
Coupling data-driven and model-based methods to improve fault diagnosis
Computers in Industry, Vol.128
128
2021
Web of Science ID: WOS:000636296000003
Abstract
Monitoring a system is often not an easy task and the best approach to address it would be to develop a monitoring system that uses data, expert knowledge, and mathematical models. Combining these three sources of information on the system is often unpractical of various reasons such as in complex systems. In this paper, a hybrid method for diagnosing single and multiple simultaneous faults, while considering unknown operating conditions, is proposed. This method consists of a Bayesian classifier combining statistical decisions and fault signature matrix. Several scenarios of operating conditions are simulated for illustrative purposes. The results, in terms of classification rates, show the interest of the hybrid classifier. It demostrates higher capabilities to isolating multiple simultaneous faults that the purely data-driven classifier fails to accomplish.
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Details
- Title
- Coupling data-driven and model-based methods to improve fault diagnosis
- Publication Details
- Computers in Industry, Vol.128
- Resource Type
- Journal article
- Publisher
- Elsevier; Netherlands
- Series
- 128
- Copyright
- 2021 Elsevier B.V. All rights reserved.
- Identifiers
- WOS:000636296000003; 99380090781306600
- Academic Unit
- Mathematics and Statistics; Hal Marcus College of Science and Engineering
- Language
- English