Incipient fault detection and isolation with Cauchy–Schwarz divergence: A probabilistic approach Article Swipe
Feiya Lv
,
Shujian Yu
,
Huawei Ye
,
Jinsong Zhao
,
Chenglin Wen
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.jfranklin.2024.107114
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.jfranklin.2024.107114
To monitor the dynamics and non-stationarity inherent in industrial processes, we propose a novel incipient fault detection and isolation scheme grounded in a probabilistic perspective, using the Cauchy–Schwarz (CS) divergence. Our innovation lies in the utilization of marginal CS divergence for incipient fault detection and the conditional CS divergence for fault isolation. This approach neither require prior parametric assumptions about the underlying data distribution nor depend on historical fault data, while simultaneously providing explanatory diagnostics. Beyond this, we develop a change point detection-base diagnosis technique for practical engineering applications. This online process monitoring technique guarantees timely intervention to uphold process stability and safety. We demonstrate the compelling performance, higher detection rate and lower alarm rate, of the CS divergence over prevalent divergence-based approaches, such as Kullback–Leibler divergence, Wasserstein distance and Mahalanobis distance. We also illustrate the explanatory insights offered by conditional CS divergence in fault isolation on synthetic data, benchmarks of continuous stirred-tank reactor process and continuous stirred-tank heater process and even a real-world continuous catalytic reforming process. Code of this CS divergence based fault detection and isolation is available at https://github.com/Feiya-Lv/Incipient-Fault-Detection-and-Isolation-with-Cauchy--Schwarz-Divergence.
Related Topics
Concepts
Divergence (linguistics)
Fault detection and isolation
Mahalanobis distance
Probabilistic logic
Computer science
Kullback–Leibler divergence
Isolation (microbiology)
Cauchy distribution
Parametric statistics
Data mining
Mathematics
Artificial intelligence
Statistics
Biology
Microbiology
Philosophy
Linguistics
Actuator
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.jfranklin.2024.107114
- OA Status
- hybrid
- Cited By
- 4
- References
- 52
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4401305378
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4401305378Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.jfranklin.2024.107114Digital Object Identifier
- Title
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Incipient fault detection and isolation with Cauchy–Schwarz divergence: A probabilistic approachWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-08-03Full publication date if available
- Authors
-
Feiya Lv, Shujian Yu, Huawei Ye, Jinsong Zhao, Chenglin WenList of authors in order
- Landing page
-
https://doi.org/10.1016/j.jfranklin.2024.107114Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1016/j.jfranklin.2024.107114Direct OA link when available
- Concepts
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Divergence (linguistics), Fault detection and isolation, Mahalanobis distance, Probabilistic logic, Computer science, Kullback–Leibler divergence, Isolation (microbiology), Cauchy distribution, Parametric statistics, Data mining, Mathematics, Artificial intelligence, Statistics, Biology, Microbiology, Philosophy, Linguistics, ActuatorTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
4Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2, 2024: 2Per-year citation counts (last 5 years)
- References (count)
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52Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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