Utilizing digitalization through heuristic risk-based blade maintenance for leading edge erosion

Description

We present a case study on how digitalization can be utilized to optimize inspection and maintenance decisions for leading edge erosion (LEE) of wind turbine blades. The repeated impact of raindrops and other particles on the leading edge of wind turbine blades leads to initiation of erosion and progressive damage development. The initiation time and mass loss rate can be predicted using the Springer model [1] and incorporating turbine and site data such as tip speed, material properties, wind and rainfall distributions. [2]. LEE negatively impacts aerodynamic performance, thereby decreasing the power production. Furthermore, untreated LEE will eventually impact the structural integrity of the wind turbine blades, and repairs must be performed to avoid this.

Digitalization has the potential to reform the process of inspection and maintenance planning, if the information available in inspection data, maintenance reports, and operational data is utilized for optimal decision making. Relevant decisions in this context are related to timing, location and extend of inspections and repairs. Optimal risk-based decision making aims to maximize utility, or equivalently, minimize expected costs including revenues losses [3].

Within the field of inspection and maintenance planning, advanced methods have been developed recently based on e.g. partially observable Markov processes (POMDP) [4] or deep reinforcement learning [5], where decisions are flexible, and information from past inspections are taken into consideration. However, the strategies recommended by these methods lack transparency, and they are consequently not easy to understand for practitioners. Therefore, heuristic strategies are often preferred by the industry due to the transparency and simplicity of use. For time-based inspections, where the inspection times are calendar based, Bayesian networks have proved to be very efficient for optimization, and this can often be done within few seconds [6]. We propose a novel Bayesian network approach, where age-based inspections can be included, by including a count-down node for the time to the next inspection (Figure 1). This allows for including strategies, where the time to the next inspection depends on the outcome of the latest inspection in terms of the size of the defect. Due to the computational efficiency, it is possible to consider a large number of different strategies. This also enables the use of adaptive strategies, where greedy optimization has been shown capable of improving the performance of heuristic strategies [7]. This study shows how this novel heuristic approach can be applied for blade maintenance in relation to LEE.

 
References:
1. Springer, G.S.; Yang, C.I. Model for the rain erosion of fiber reinforced composites. AIAA J. 1975, 13, 877–883, doi:10.2514/3.60463.
2. Verma, A.S.; Jiang, Z.; Caboni, M.; Verhoef, H.; van der Mijle Meijer, H.; Castro, S.G.P.; Teuwen, J.J.E. A probabilistic rainfall model to estimate the leading-edge lifetime of wind turbine blade coating system. Renew. Energy 2021, 178, 1435–1455, doi:10.1016/j.renene.2021.06.122.
3. Dimitrov, N. Risk-based approach for rational categorization of damage observations from wind turbine blade inspections. In Proceedings of the Journal of Physics: Conference Series; Institute of Physics Publishing, 2018; Vol. 1037.
4. Morato, P.G.; Papakonstantinou, K.G.; Andriotis, C.P.; Nielsen, J.S.; Rigo, P. Optimal inspection and maintenance planning for deteriorating structural components through dynamic Bayesian networks and Markov decision processes. Struct. Saf. 2022, 94, 102140, doi:10.1016/j.strusafe.2021.102140.
5. Andriotis, C.P.; Papakonstantinou, K.G. Managing engineering systems with large state and action spaces through deep reinforcement learning. Reliab. Eng. Syst. Saf. 2019, 191, doi:10.1016/j.ress.2019.04.036.
6. Nielsen, J.S.; Sørensen, J.D. Computational framework for risk-based planning of inspections, maintenance and condition monitoring using discrete Bayesian networks. Struct. Infrastruct. Eng. 2017, 1–13, doi:10.1080/15732479.2017.1387155.
7. Bismut, E.; Straub, D. Optimal adaptive inspection and maintenance planning for deteriorating structural systems. Reliab. Eng. Syst. Saf. 2021, 215, 107891, doi:10.1016/j.ress.2021.107891.

Period	25 May 2023
Event title	Wind Energy Science Conference 2023
Event type	Conference
Location	Glasgow, United KingdomShow on map
Degree of Recognition	International