@article{GrajewskiKleefeld2023,
  author    = {Grajewski, Matthias and Kleefeld, Andreas},
  title     = {Detecting and approximating decision boundaries in low-dimensional spaces},
  series = {Numerical Algorithms},
  volume    = {93},
  journal   = {Numerical Algorithms},
  number    = {4},
  publisher = {Springer Science+Business Media},
  address   = {Dordrecht},
  issn      = {1572-9265},
  pages     = {35 Seiten},
  year      = {2023},
  abstract  = {A method for detecting and approximating fault lines or surfaces, respectively, or decision curves in two and three dimensions with guaranteed accuracy is presented. Reformulated as a classification problem, our method starts from a set of scattered points along with the corresponding classification algorithm to construct a representation of a decision curve by points with prescribed maximal distance to the true decision curve. Hereby, our algorithm ensures that the representing point set covers the decision curve in its entire extent and features local refinement based on the geometric properties of the decision curve. We demonstrate applications of our method to problems related to the detection of faults, to multi-criteria decision aid and, in combination with Kirsch's factorization method, to solving an inverse acoustic scattering problem. In all applications we considered in this work, our method requires significantly less pointwise classifications than previously employed algorithms.},
  language  = {en}
}
@article{SchopenShahEschetal.2024,
  author    = {Schopen, Oliver and Shah, Neel and Esch, Thomas and Shabani, Bahman},
  title     = {Critical quantitative evaluation of integrated health management methods for fuel cell applications},
  series = {International Journal of Hydrogen Energy},
  volume    = {70},
  journal   = {International Journal of Hydrogen Energy},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0360-3199},
  doi       = {10.1016/j.ijhydene.2024.05.156},
  pages     = {370 -- 388},
  year      = {2024},
  abstract  = {Online fault diagnostics is a crucial consideration for fuel cell systems, particularly in mobile applications, to limit downtime and degradation, and to increase lifetime. Guided by a critical literature review, in this paper an overview of Health management systems classified in a scheme is presented, introducing commonly utilised methods to diagnose FCs in various applications. In this novel scheme, various Health management system methods are summarised and structured to provide an overview of existing systems including their associated tools. These systems are classified into four categories mainly focused on model-based and non-model-based systems. The individual methods are critically discussed when used individually or combined aimed at further understanding their functionality and suitability in different applications. Additionally, a tool is introduced to evaluate methods from each category based on the scheme presented. This tool applies the technique of matrix evaluation utilising several key parameters to identify the most appropriate methods for a given application. Based on this evaluation, the most suitable methods for each specific application are combined to build an integrated Health management system.},
  language  = {en}
}