@inproceedings{47116,
  abstract     = {{This paper presents a comprehensive study on diagnosing a spacecraft propulsion system utilizing data provided by the Prognostics and Health Management (PHM) society, specifically obtained as part of the Asia-Pacific PHM conference’s data challenge 2023. The objective of the challenge is to identify and diagnose known faults as well as unknown anomalies in the spacecraft’s propulsion system, which is critical for ensuring the spacecraft’s proper functionality and safety. To address this challenge, the proposed method follows a systematic approach of feature extraction, feature selection, and model development. The models employed in this study are kMeans clustering and decision trees combined to ensembles, enriched with expert knowledge. With the method presented, our team was capable of reaching high accuracy in identifying anomalies as well as diagnosing faults, resulting in attaining the seventh place with a score of 93.08 %.}},
  author       = {{Aimiyekagbon, Osarenren Kennedy and Löwen, Alexander and Bender, Amelie and Muth, Lars and Sextro, Walter}},
  booktitle    = {{Proceedings of the Asia Pacific Conference of the PHM Society 2023 }},
  keywords     = {{PHM, Fault Diagnostics, Multiple Fault Modes, Expert-Informed Diagnostics, Anomaly Detection}},
  number       = {{1}},
  title        = {{{Expert-Informed Hierarchical Diagnostics of Multiple Fault Modes of a Spacecraft Propulsion System}}},
  doi          = {{10.36001/phmap.2023.v4i1.3596}},
  volume       = {{4}},
  year         = {{2023}},
}

@inproceedings{17810,
  abstract     = {{In all fields, the significance of a reliable and accurate predictive model is almost unquantifiable. With deep domain knowledge, models derived from first principles typically outperforms other models in terms of reliability and accuracy. When it may become a cumbersome or an unachievable task to build or validate such models of complex (non-linear) systems, machine learning techniques are employed to build predictive models. However, the accuracy of such techniques is not only dependent on the hyper-parameters of the chosen algorithm, but also on the amount and quality of data. This paper investigates the application of classical time series forecasting approaches for the reliable prognostics of technical systems, where black box machine learning techniques might not successfully be employed given insufficient amount of data and where first principles models are infeasible due to lack of domain specific data. Forecasting by analogy, forecasting by analytical function fitting, an exponential smoothing forecasting method and the long short-term memory (LSTM) are evaluated and compared against the ground truth data. As a case study, the methods are applied to predict future crack lengths of riveted aluminium plates under cyclic loading. The performance of the predictive models is evaluated based on error metrics leading to a proposal of when to apply which forecasting approach.}},
  author       = {{Aimiyekagbon, Osarenren Kennedy and Bender, Amelie and Sextro, Walter}},
  booktitle    = {{PHM Society European Conference}},
  keywords     = {{PHM 2019, crack propagation, forecasting, unevenly spaced time series, step ahead prediction, short time series}},
  number       = {{1}},
  title        = {{{Evaluation of time series forecasting approaches for the reliable crack length prediction of riveted aluminium plates given insufficient data}}},
  volume       = {{5}},
  year         = {{2020}},
}

@inproceedings{9879,
  abstract     = {{Application of prognostics and health management (PHM) in the field of Proton Exchange Membrane (PEM) fuel cells is emerging as an important tool in increasing the reliability and availability of these systems. Though a lot of work is currently being conducted to develop PHM systems for fuel cells, various challenges have been encountered including the self-healing effect after characterization as well as accelerated degradation due to dynamic loading, all which make RUL predictions a difficult task. In this study, a prognostic approach based on adaptive particle filter algorithm is proposed. The novelty of the proposed method lies in the introduction of a self-healing factor after each characterization and the adaption of the degradation model parameters to fit to the changing degradation trend. An ensemble of five different state models based on weighted mean is then developed. The results show that the method is effective in estimating the remaining useful life of PEM fuel cells, with majority of the predictions falling within 5\% error. The method was employed in the IEEE 2014 PHM Data Challenge and led to our team emerging the winner of the RUL category of the challenge.}},
  author       = {{Kimotho, James Kuria  and Meyer, Tobias and Sextro, Walter}},
  booktitle    = {{Prognostics and Health Management (PHM), 2014 IEEE Conference on}},
  keywords     = {{ageing, particle filtering (numerical methods), proton exchange membrane fuel cells, remaining life assessment, PEM fuel cell prognostics, PHM, RUL predictions, accelerated degradation, adaptive particle filter algorithm, dynamic loading, model parameter adaptation, prognostics and health management, proton exchange membrane fuel cells, remaining useful life estimation, self-healing effect, Adaptation models, Data models, Degradation, Estimation, Fuel cells, Mathematical model, Prognostics and health management}},
  pages        = {{1--6}},
  title        = {{{PEM fuel cell prognostics using particle filter with model parameter adaptation}}},
  doi          = {{10.1109/ICPHM.2014.7036406}},
  year         = {{2014}},
}