Similarity learning hidden semi-Markov model for adaptive prognostics of composite structures

Data-driven methodologies have found increasing usage in the last decade for remaining useful life (RUL) prognostics of composite materials utilizing structural health monitoring (SHM) data. Of particular interest is the reliable RUL prediction in cases where the end-of-life is not in between the extreme values within the testing dataset. For...

Acoustic emission-based remaining useful life prognosis of aeronautical structures subjected to compressive fatigue loading

An increasing interest for Structural Health Monitoring has emerged in the last decades. Acoustic emission (AE) is one of the most popular and widely studied methodologies employed for monitoring, due to its capabilities of detecting, locating and capturing the evolution of damage. Most literature so far, has employed AE for characterizing...

A novel strain-based health indicator for the remaining useful life estimation of degrading composite structures

We present a generic methodology for developing a Health Indicator out of strain-based Structural Health Monitoring data suitable for implementation in prognostic tasks. For this purpose, an in-house test campaign is launched. Single-stringered composite panels are subjected to compression-compression fatigue with the strains being monitored...

Adaptive prognostics for remaining useful life of composite structures

Prognostics is an emerging field of research that enables the real-time health assessment of an engineering system and the prediction of its future state based on up-to-date information. This field integrates various scientific disciplines including physics/mechanics, computational statistics and probabilistic modeling, machine learning and...

An adaptive probabilistic data-driven methodology for prognosis of the fatigue life of composite structures

Data driven probabilistic methodologies have found increasing use the last decade and provide a platform for the remaining useful life (RUL) prediction of composite structures utilizing health-monitoring data. Of particular interest is the RUL prediction of composite structures that either underperform or outperform due to unexpected...

Structural health monitoring data fusion for in-situ life prognosis of composite structures

A novel framework to fuse structural health monitoring (SHM) data from different in-situ monitoring techniques is proposed aiming to develop a hyper-feature towards more effective prognostics. A state-of-the-art Non-Homogenous Hidden Semi Markov Model (NHHSMM) is utilized to model the damage accumulation of composite structures, subjected to...

A novel approach towards fatigue damage prognostics of composite materials utilizing SHM data and stochastic degradation modeling

A prognostic framework is proposed in order to estimate the remaining useful life of composite materials under fatigue loading based on acoustic emission data and a sophisticated Non Homogenous Hidden Semi Markov Model. Bayesian neural networks are also utilized as an alternative machine learning technique for the non-linear regression task. A...