The mutual and simultaneous action of external variable forces on (offshore) wind support structures causes fatigue. Fatigue analysis in this context relies on 10-minute-long strain signals. Cycle-counting allows capturing the fatigue cycles nested within these signals but inevitably leaves some open loops called half-cycles or residuals. Some other loops are not even cycle-counted because the algorithm cannot catch low-frequency (LF) cycles spanning more than 10 minutes, as caused, e.g., by wind speed variations. Notwithstanding, LF cycles are also the most damaging since they always contain the highest range in the variable amplitude signal. Therefore, counting multiple 10-minute signals and merging the resulting histograms into one inevitably has some non-conservative effects, for it leaves the LF cycles uncounted. In this work, we avoid signal concatenation to recover the LF effect. To do so, we use the residuals sequence from the 10-minute signals as it embeds the LF information. As method validation, we compare the impact of LF fatigue dynamics recovery on the linearly accumulated damage using a real-life dataset measured at a Belgian offshore wind turbine. By defining a factor that incorporates the LF effect, we observe that after 300 days of observation, the factor converges to a fixed value.

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Bridges are critical infrastructures subjected to cyclic loading and require fatigue monitoring to prevent high maintenance costs due to fatigue failure. This paper, part of OWI-lab's research activities within the SafeLife-Infrabel project, presents a fatigue survey on four months of strain measurements of a steel railway bridge in Belgium, comprising 98 Fiber Bragg Gratings (FBGs). This study aims to develop a data-driven case/event detection scheme including measurements and operational data (e.g., train type and passage time). The first objective is to develop a Python package to separate the events by automatically selecting the train passage events from the strain time series to analyze them and also reduce the dataset size. Over the studied period, a total of 5000 events were detected. Then, the available operational data is complemented with properties estimated from the strain measurements, including the axle number, speed, and direction. Finally, the relation between the fatigue damage and different train features is studied by calculating the attributed fatigue damage using the Rainflow cycle counting method and the Palmgren-Miner rule. A notable damage difference existed between freight and passenger trains. In addition, the damage difference between loaded and empty freight trains was completely distinguishable, while the effect of occupancy was not very visible in passenger trains. Axle number had the highest impact among the passenger trains and had linear relation with damage. Also, the difference in fatigue damage between various types of passenger trains was less distinctive. Finally, the speed and direction affected the damage very slightly.

Abstract. Offshore wind turbine support structures are fatigue-driven designs subjected to a wide variety of cyclic loads from wind, waves, and turbine controls. While most wind turbine loads and metocean data are collected at short-term 10-minute intervals, some of the largest fatigue cycles have periods over one day. Therefore, these low-frequency fatigue dynamics (LFFD) are not fully considered when working with the industry-standard short-term window. To recover these LFFDs in the state-of-the-industry practices, the authors implemented a short-to-long-term factor applied to the accumulated short-term damages, while maintaining the ability to work with the 10-minute data. In the current work, we study the LFFD impact on the damage from the Fore-Aft and Side-Side bending moments and the sensors' strain measurements and their variability within and across wind farms. For an S-N curve slope of m=5, up to 65 % of damage is directly related to LFFD.