Time‐domain analysis of signal morphology and features as suitable surrogates for structural monitoring

The holy grail of signal processing is getting suitable features that act as surrogates for damage conditions in structural monitoring. These features, which describe the characteristic shape and pattern of signals (i.e., their morphology), can be descriptive or predictive of the condition of the structure and help in the effective management of repairs and damage control. The signal measurement data and the features derived from them are highly affected by a myriad of on‐ground conditions referred to as noise factors. In this study, we collect data from accelerometers using a factorial design in a laboratory setting with a shaker that are put under the effect of some noise factors that can potentially have a large influence on the sensor outcome. We gather a compendium of candidate time‐domain signal features and quantitatively evaluate using the accelerometer data. Of the many possible noise factors, we vary cable length, vibration of cable, and electromagnetic interference and investigate the measurement dispersion effects on the signal features. We introduce and plot “normalized profiles” a methodology for extracting key morphological characteristics from periodic signals. We propose the calculation and use of simple S/N ratio metrics to evaluate the suitability of a feature and how robust they are to the noise factors. This study contributes to advancing SHM methodologies by expanding the range of tools available to civil engineers for assessing signal morphology and identifying suitable surrogates describing the condition of a structure.