# Identifying the Initial Corrosion Fatigue Failure Based on Dropping Electrochemical Potential
## 4 Conclusions  
Predicting early failure based on the electrochemical potential of steel subjected to both highly corrosive environments and mechanical stress could significantly lower maintenance needs and reduce costs in geothermal energy production facilities.  
The corrosion fatigue behavior of hour-glass shaped specimens of duplex stainless-steel X2 CrNiMoN22-5-3 was investigated in an individual test set-up. Artificial thermal water as the corrosive medium setting the geothermal environment of the Northern German Basin was heated to 369 K. The electric potential of an in-house manufactured Ag/AgCl wire electrode was measured versus the reference Ag/AgCl glass electrode, and failure of corrosion fatigue specimens in CCS environment was correlated with the drop of the curves of the electrochemical potential.  
The measured potential values were divided into ten time series, each containing ten data points, to establish a test algorithm. This process combined splitting with calculations of mean values, slopes, and regressions, successfully initiating the algorithm. Time courses consisting of ten consecutive numbers, drawn every five seconds, were analyzed under three main conditions and two sub-conditions. The algorithm's functionality was evaluated using fifteen potential functions.  
During the initial test run, the algorithm demonstrated reliability for 14 out of 15 specimens, achieving a functionality level of approximately 93%, with a predictive time lead of up to 435 s. These findings suggest that it is feasible to predict corrosion fatigue failure in test specimens under geothermal conditions--similar to those in geothermal energy production--using in situ electrochemical potential measurements. In the future, refinement of the algorithm will focus on improving the reliability to a higher percentage.  
An algorithm predicting early failure during corrosion fatigue can enhance the reliability and safety of geothermal energy production and carbon capture and storage (CCS) sights by enabling proactive maintenance and reducing the risk of unexpected equipment failure. This predictive capability minimizes downtime and operational costs while extending the lifespan of critical infrastructure exposed to highly corrosive geothermal or CCS conditions. Additionally, it supports sustainable energy practices by ensuring consistent system performance and mitigating environmental risks associated with material degradation.  
Conceptualization, A.P. and R.S.; methodology, A.P. and R.S.; software, R.S.; validation, R.S. and A.P.; formal analysis, R.S.; investigation, R.S.; resources, A.P. and R.S.; data curation, R.S.; writing--original draft preparation, A.P.; writing--review and editing, A.P.; visualization, R.S. and A.P.; supervision, A.P.; project administration, A.P.; funding acquisition, A.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors would like to thank [PERSON] for the design of the corrosion chamber and contribution to fatigue testing as well as discussion during project progress.  
The authors declare no conflicts of interest.