Modal analysis under jittering and kernel clock distribution: single-output identification

Smartphones have attracted attention in the structural health monitoring community due to their embedded multisensory platforms suitable for crowdsourcing innovation. Despite their advantages, smartphone sensors are originally designed for user utility rather than technical engineering applications (e.g. vibration analysis and modal identification). Sampling jitter is among those problems adversely influencing smartphone performance as a scientific device that can be used for characterising civil infrastructure dynamic characteristics. With inconsistencies in the sampling period due to jitter effects, the identified modal frequencies of a structure can deviate from the actual value, which is artificially introduced by smartphone clock errors. In this study, the authors formulate the statistical characteristics of the smartphone sampling period through kernel distribution and apply a digital reconstruction remedy to the sampling jitter problem. Through introductory simulations of a sine wave and physical implementations through shaking table tests equipped with smartphones, the efficiency of kernel distribution diagnosis and the signal-reconstruction remedy is presented. Following the simulation and laboratory applications, the proposed techniques are applied to vibration monitoring of a steel pedestrian bridge in terms of the auto power spectral density and short-time Fourier transform of single-output signals. The results show successful rehabilitation of accelerometer data from smartphones, removing jitter-induced errors to a significant extent and accordingly improving the identification accuracy currently in a single-output setting.