Modeling Discrete Coating Degradation Events via Hawkes Processes

• URL: http://arxiv.org/abs/2504.09706v1
• Date: Sun, 13 Apr 2025 19:57:10 GMT
• Title: Modeling Discrete Coating Degradation Events via Hawkes Processes
• Abstract summary: We propose novel metrics for representing material degradation, taking the form of discrete degradation events.<n>These events maintain the statistical properties of continuous sensor readings, but are composed of orders of magnitude fewer measurements.<n>We use the forecast of degradation to predict a future time of failure, exhibiting superior performance to the approach based on direct modeling of galvanic corrosion.
• Score: 5.695027198038298
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Forecasting the degradation of coated materials has long been a topic of critical interest in engineering, as it has enormous implications for both system maintenance and sustainable material use. Material degradation is affected by many factors, including the history of corrosion and characteristics of the environment, which can be measured by high-frequency sensors. However, the high volume of data produced by such sensors can inhibit efficient modeling and prediction. To alleviate this issue, we propose novel metrics for representing material degradation, taking the form of discrete degradation events. These events maintain the statistical properties of continuous sensor readings, such as correlation with time to coating failure and coefficient of variation at failure, but are composed of orders of magnitude fewer measurements. To forecast future degradation of the coating system, a marked Hawkes process models the events. We use the forecast of degradation to predict a future time of failure, exhibiting superior performance to the approach based on direct modeling of galvanic corrosion using continuous sensor measurements. While such maintenance is typically done on a regular basis, degradation models can enable informed condition-based maintenance, reducing unnecessary excess maintenance and preventing unexpected failures.

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