Fatigue damage prognosis always requires a degradation model describing the damage evolution with time; thus, the prognostic performance highly depends on the selection of such a model. The best model should probably be case specific, calling for the fusion of multiple degradation models for a robust prognosis. In this context, this paper proposes a scheme of online fusing multiple models in a particle filter (PF)-based damage prognosis framework. First, each prognostic model has its process equation built through a physics-based or data-driven degradation model and has its measurement equation linking the damage state and the measurement. Second, each model is independently processed through one PF to provide one group of particles. Then, the particles from all models are adopted for remaining useful life prediction. Finally, the particles from each PF are fused with those from all the other PFs to improve their particle diversity, and consequently, to provide better estimation and prognostic performance. The feasibility and robustness of the proposed method are validated by an experimental study, where an aluminum lug structure subject to fatigue crack growth is monitored by a guided wave measurement system.

Institutions governing multi-agent systems (MASs) are a pervasive means to guide agents towards the aims of the MAS (e.g. collecting data) with regulations on the outcomes of agents’ behaviour. Yet, wider organisations/governments often intend to guide the design of institutions governing MAS in meeting different aims (e.g. preserving the rights of agents). A pervasive means to guide the design of MAS-governing institutions (or any institution, for that matter) is to use institutions at higher tiers of governance (e.g. directives, constitutions) to regulate the regulations of institutions at lower tiers of governance (e.g. national legislation, software policies). A recent innovation has been an automated means to determine the compliance of a lower-tier institution’s regulations with a higher-tier’s. However, for a designer of a non-compliant institution there remains a dilemma: be punished for non-compliant regulations or arduously determine and rectify the underlying causes of non-compliance. In this paper we propose a way to automatically determine how to revise an institution to be compliant that also minimises the change in the regulations’ outcomes thus keeping as closely as possible to the institution designers’ original intentions.