Optimal principal component and measurement interval selection for PCA reconstruction-based anomaly detection in uncontrolled structural health monitoring

Abstract

PCA reconstruction-based techniques are widely used in guided wave structural health monitoring to facilitate unsupervised damage detection. The measurement interval of collecting evaluation data significantly influences the correlation among the data points, impacting principal component values and, consequently, the accuracy of damage detection. Despite its importance, there has been limited research on the selection of suitable components and measurement intervals to reduce false alarms. This paper seeks to develop strategies for identifying the optimal number of principal components and measurement intervals for PCA reconstruction-based damage detection methods. Our results indicate that the patterns of change in reconstruction coefficients, based on the number of components used in PCA reconstruction and the measurement interval for collecting evaluation data, are effective indicators for determining the optimal principal components and measurement intervals for damage detection, without using any damage information. The effectiveness of the indicators for determining optimal components and measurement intervals is validated using evaluation sets collected under uncontrolled and dynamic monitoring conditions, with measurement intervals ranging from 86 to 8600 s per measurement.