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 sensing technologies. The main goal is the prediction of the remaining useful life (RUL) of the engineering system while it is in-service. Lately, there is an effort to study and predict the future status of engineering systems that exhibit a complex degradation process. The availability of condition monitoring (CM) data, the constantly increasing computational power, the development of machine learning algorithms and the advancements on the physics/mechanics for several engineering systems form a solid foundation to achieve that goal. Among the engineering systems that exhibit a complex degradation process are composite structures. Composite structures have made a significant mark in numerous industries, driven by advantages in structural efficiency, performance, versatility and cost. It is well known that the damage accumulation process of composite structures depends on several parameters, i.e. the type of material and the lay-up, the loading frequency and sequence, the manufacturing process. Additionally, the multi-phase nature of composites and the variation of defects result in a stochastic activation of the different failure mechanisms. So, one expects that the long-term behaviour of two comparable composites structures, subjected to comparable environmental and loading conditions, will differ and that makes the fatigue damage analysis, and consequently the prediction of RUL, very complex tasks. This difference is profound especially when unexpected phenomena may occur. The goal of this research is to develop a new RUL prediction model that is able to learn from unexpected phenomena and adapt its parameters accordingly. The model is composed of three elements; 1) sensing techniques to acquire online CM data, 2) machine learning algorithm for developing a damage modelling strategy and 3) stochastic modelling for uncertainty quantification. Based on the literature review, it was concluded that a frequentist data-driven model has the potential to fulfil the research goal and an extension of the Non-Homogenous Hidden Semi Markov model (NHHSMM) is a good candidate. The first step was to design the structure of the RUL prediction model and define its elements. The next step was to develop the extension of the NHHSMM, and verify its correctness and robustness, utilizing simulated Monte-Carlo (MC) data. A series of assumptions was necessary in order to frame the applicability of the model towards composite structures and to achieve an efficient prediction process.