Non-proportionality quantification of noisy stress-time signals using a new rainflow-based noise removal method for fatigue assessment

Abstract

Non-proportional stresses near welded joints are well known to cause increased fatigue damages as compared to proportional stresses. In this paper, a new method to remove noise from stress-time signals is developed and implemented in a principal component analysis-based approach for non-proportionality quantification. The noise removal method makes it possible to remove noise from stress-time signals causing low stress ranges, while still keeping the overall shape of the signal. When the signal shape is kept it is possible to accurately predict the levels of non-proportionality. The noise removal method is based on the standard rainflow counting method. By choosing a lower stress range limit, the stress ranges assumed to be caused by noise can be excluded and the original signal shape is kept by utilizing simple polynomial fitting. This makes the approach easy to implement and easy to control as it requires only two inputs. The noise removal method and non-proportionality quantification approach are then validated against simulated signals with noise and a simple experiment with proportional loading. The results show that the newly developed method for noise reduction accurately removes noise while keeping the signal shape.