[7A7] A critical overview of machine learning approaches in structural health monitoring and assessment: smart composites
I Omar, M Khan and A Starr
Cranfield University, UK
Structural health monitoring is exceptionally essential for preserving and sustaining any mechanical structure's service life. A successful assessment should provide reliable and resolute information to maintain the continuous performance of the structure. This information can effectively determine damage progression and its overall impact on the structural operation. However, the available sensing techniques and methods for performing SHA generate raw measurements that require significant data processing before making any valuable predictions. Machine learning (ML) algorithms (supervised and unsupervised learning) have been extensively used for such data processing. These algorithms extract damage sensitive features from the raw data to identify structural condition and performance. As per the available published literature, the extraction of these features has been quite random and used by academic researchers without a suitability justification. In this paper, a comprehensive literature review is performed to emphasise the influence of damaging sensitive features on ML algorithms. The selection and suitability of these features are critically reviewed while processing raw data obtained from composite material. It has been found that an accurate damage prediction is only possible if the selection of damage sensitive features and ML algorithms is performed based on available raw data and structure material type. This paper also highlights the current challenges and limitations during the mentioned selection.
