Online remaining lifetime estimation for structures

Heinrich, Ch.; Khalil, Mohamed; Martynov, Kirill; Wever, Utz

doi:10.1016/j.ymssp.2018.09.028

Cited by 13 publications

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, for online monitoring and control, a complete Rainflow analysis for each newly measured stress sample is computationally infeasible. As a solution, Heinrich et al (2019) showed that Rainflow analysis can be performed batchwise if a so-called residue is used for carrying along the half-cycle stress samples. Residue, therefore, denotes a set of stress samples that occured in the past and have not formed full cycles as yet.…”

Section: Batchwise Cycle Identification and Residuementioning

confidence: 99%

Lidar-assisted model predictive control of wind turbine fatigue via online rainﬂow-counting considering stress history

Loew

Bottasso

2021

Preprint

View full text Add to dashboard Cite

Abstract. The formulation of Parametric Online Rainflow Counting implements the standard fatigue estimation process and a stress history in the cost function of a Model Predictive Controller. The formulation is tested in realistic simulation scenarios where the states are estimated by a Moving Horizon Estimator and the wind is predicted by a lidar simulator. The tuning procedure for the controller toolchain is carefully explained. In comparison to a conventional MPC in a turbulent wind setting, the novel formulation is especially superior with low lidar quality, benefits more from the availability of a wind prediction, and exhibits a more robust performance with shorter prediction horizons. A simulation excerpt with the novel formulation provides deeper insight into the update of the stress history and the fatigue cost parameters. Finally, in a deterministic gust setting, both the conventional and the novel MPC - despite their completely different fatigue cost - exhibit similar pitch behavior and tower oscillation.

show abstract

Section: Batchwise Cycle Identification and Residuementioning

confidence: 99%

Lidar-assisted model predictive control of wind turbine fatigue via online rainﬂow-counting considering stress history

Loew

Bottasso

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…In traditional PHM methods, data volume is limit because this data is collected from a single element or data acquisition isn't a continuing process. Then, these methods can not satisfy Industry 4.0 requirements and new and modern methods should be developed to use big data and predict system future, such as deep learning [53], health index similarity [54], on-line method [55]. Another problem is drift concept because we deal with a dynamic and resilience system and the relation between inputs and outputs of the system continuously changed.…”

Section: Prognostics and Health Management And Big Datamentioning

confidence: 99%

Industry 4.0: Some Challenges and Opportunities for Reliability Engineering

Farsi

Zio

2019

Int J Reliabl Risk Safe Theory App

View full text Add to dashboard Cite

According to the development of Industry 4.0 and increase the integration of digital, physical and human worlds, reliability engineering must evolve for addressing the existing and future challenges about that. In this paper, the principle of Industry 4.0 is presented and some of these challenges and opportunities for reliability engineering are discussed. New directions for research in system modeling, big data analysis, health management, cyber-physical system, human-machine interaction, uncertainty, jointly optimization, communication, and interfaces are proposed. Each topic can be investigated individually, but this paper summarizes them and prepared a vision about reliability engineering for consideration and discussion by the interested scientific community.

show abstract