Uncertainty Quantification in Industrial Systems Using Deep Gaussian Process for Accurate Degradation Modeling
Several factors, such as human error, environmental factors, and maintenance practices, contribute to the degradation of real-world industrial systems. Predicting system dynamics is challenging and necessitates high user confidence, as these factors contribute to both aleatoric uncertainty (inherent...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10744003/ |
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| author | Richard Nasso Toumba Achille Eboke Giscard Ombete Tsimi Timothee Kombe |
| author_facet | Richard Nasso Toumba Achille Eboke Giscard Ombete Tsimi Timothee Kombe |
| author_sort | Richard Nasso Toumba |
| collection | DOAJ |
| description | Several factors, such as human error, environmental factors, and maintenance practices, contribute to the degradation of real-world industrial systems. Predicting system dynamics is challenging and necessitates high user confidence, as these factors contribute to both aleatoric uncertainty (inherent system variability) and epistemic uncertainty (due to limited information). Decision-making and risk assessment are frequently hindered by the inability of current artificial intelligence methods to generate interpretable uncertainty estimates. To address these constraints, we propose an analysis that employs Deep Gaussian Processes (DGPs), a robust framework for generating interpretable uncertainty distributions and capturing system variability. A rigorous mathematical foundation is essential to our approach, because it enables the selection of metrics that effectively capture the system’s degradation aspects. In addition to predicting the remaining useful life, these metrics, when used in conjunction with DGPs, facilitate the creation of a degradation model that is both accurate and dependable. This model also contributes to the improvement of system reliability and proactive maintenance. We demonstrate our approach’s practical efficacy by validating it on a real-world industrial semolina plant with four mills. |
| format | Article |
| id | doaj-art-376e83307a254a049ae130f199644f53 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-376e83307a254a049ae130f199644f532024-11-14T00:01:37ZengIEEEIEEE Access2169-35362024-01-011216457616458710.1109/ACCESS.2024.349186610744003Uncertainty Quantification in Industrial Systems Using Deep Gaussian Process for Accurate Degradation ModelingRichard Nasso Toumba0https://orcid.org/0009-0006-7596-1860Achille Eboke1Giscard Ombete Tsimi2https://orcid.org/0009-0002-8304-3710Timothee Kombe3Laboratory of Technology and Applied Sciences, University of Douala, Douala, CameroonLaboratory of Technology and Applied Sciences, University of Douala, Douala, CameroonLaboratory of Technology and Applied Sciences, University of Douala, Douala, CameroonLaboratory of Technology and Applied Sciences, University of Douala, Douala, CameroonSeveral factors, such as human error, environmental factors, and maintenance practices, contribute to the degradation of real-world industrial systems. Predicting system dynamics is challenging and necessitates high user confidence, as these factors contribute to both aleatoric uncertainty (inherent system variability) and epistemic uncertainty (due to limited information). Decision-making and risk assessment are frequently hindered by the inability of current artificial intelligence methods to generate interpretable uncertainty estimates. To address these constraints, we propose an analysis that employs Deep Gaussian Processes (DGPs), a robust framework for generating interpretable uncertainty distributions and capturing system variability. A rigorous mathematical foundation is essential to our approach, because it enables the selection of metrics that effectively capture the system’s degradation aspects. In addition to predicting the remaining useful life, these metrics, when used in conjunction with DGPs, facilitate the creation of a degradation model that is both accurate and dependable. This model also contributes to the improvement of system reliability and proactive maintenance. We demonstrate our approach’s practical efficacy by validating it on a real-world industrial semolina plant with four mills.https://ieeexplore.ieee.org/document/10744003/Industrial systemdeep Gaussian processuncertainty quantificationdegradation modeling |
| spellingShingle | Richard Nasso Toumba Achille Eboke Giscard Ombete Tsimi Timothee Kombe Uncertainty Quantification in Industrial Systems Using Deep Gaussian Process for Accurate Degradation Modeling IEEE Access Industrial system deep Gaussian process uncertainty quantification degradation modeling |
| title | Uncertainty Quantification in Industrial Systems Using Deep Gaussian Process for Accurate Degradation Modeling |
| title_full | Uncertainty Quantification in Industrial Systems Using Deep Gaussian Process for Accurate Degradation Modeling |
| title_fullStr | Uncertainty Quantification in Industrial Systems Using Deep Gaussian Process for Accurate Degradation Modeling |
| title_full_unstemmed | Uncertainty Quantification in Industrial Systems Using Deep Gaussian Process for Accurate Degradation Modeling |
| title_short | Uncertainty Quantification in Industrial Systems Using Deep Gaussian Process for Accurate Degradation Modeling |
| title_sort | uncertainty quantification in industrial systems using deep gaussian process for accurate degradation modeling |
| topic | Industrial system deep Gaussian process uncertainty quantification degradation modeling |
| url | https://ieeexplore.ieee.org/document/10744003/ |
| work_keys_str_mv | AT richardnassotoumba uncertaintyquantificationinindustrialsystemsusingdeepgaussianprocessforaccuratedegradationmodeling AT achilleeboke uncertaintyquantificationinindustrialsystemsusingdeepgaussianprocessforaccuratedegradationmodeling AT giscardombetetsimi uncertaintyquantificationinindustrialsystemsusingdeepgaussianprocessforaccuratedegradationmodeling AT timotheekombe uncertaintyquantificationinindustrialsystemsusingdeepgaussianprocessforaccuratedegradationmodeling |