improving reliability assessment of degrading linear assets by aligning inspection measurement

Effective reliability and maintenance analysis require proper data management, including collecting, analyzing, and using models for decision-making. when dealing with the reliability analysis of degrading linear assets, it is important to pre-process the data to ensure the quality and alignment of the measurements. in the context of railway, geometry defects data suffer from an uncontrolled shift owing to the stretching or compression of the measurements, which is called positional errors. positional errors negatively affect the prediction of the reliability of track geometry. this paper aims to reduce positional errors by aligning the measurements using the recursive segment-wise peak alignment (rspa) method. this method is a featured-based method that only focuses on the alignment of peaks with high amplitudes in the geometry measurement data. concentrating on only aligning peaks instead of all the data points would substantially reduce the computational complexity, which is one of the most important objectives of any alignment method. a case study was conducted to implement and assess the performance of these methods in reducing the positional errors in geometry measurements of the track as a linear asset. the results revealed that rspa not only aligns the geometry defects precisely with a very high speed but also maintains the original value and shape of the peaks. the results of this study can strengthen the analysis and prediction of the evolution of the geometry defects in linear assets.