Distance similarity matrix using ensemble of dimensional data reduction techniques: Vibration and aerocoustic case studies

Vibration and acoustic-based health-monitoring techniques are used in the literature to monitor structural health under dynamic environment. In this paper, we propose a damage detection and monitoring method based on a distance similarity matrix of dimensionally reduced data wherein redundancy therein is removed. The matrix similarity approach is generic in nature and has the capability of multiscale representation of datasets. To extract damage-sensitive features, dimensional reduction techniques are applied and compared. An ensemble method of dimensional reduction feature outputs is presented and applied to two case studies. The results supports why ensembles can often perform better than any single-feature extraction method. For the first case study, aeroacoustic datasets are collected from controlled scaled experimental tests of controlled known damaged subscale wing structure. For the second case study, a vibration experiment study is used for abrupt change detection and tracking. The results of the two case studies demonstrate that the proposed method is very effective in detecting abrupt changes and the ensemble method developed here can be used for deterioration tracking.