Wear rate–state interactions within a multi-component system: a study of a gearbox-accelerated life testing platform

Abstract

The degradation process of complex multi-component systems is highly stochastic in nature. A major side effect of this complexity is that components of such systems may have unexpected reduced life and faults and failures that decrease the reliability of multi-component systems in industrial environments. In this work we provide maintenance practitioners with an explanation of the nature of some of these unpredictable events, namely the degradation interactions that take place between components. We begin by presenting a general wear model where the degradation process of a component may be dependent on the operating conditions, the component's own state, and the state of the other components. We then present our methodology for extracting accurate health indicators from multi-component systems by means of a time-frequency domain analysis. Finally we present a multi-component system degradation analysis of experimental data generated by a gearbox accelerated life testing platform. In so doing, we demonstrate the importance of modelling the interactions between the system components by showing their effect on component lifetime reduction.

A Diagnostics and Prognostics Framework for Multi-Component Systems With Wear Interactions: Application to a Gearbox-Platform

“…We now describe our approach for performing prognosis for multi-component systems. We start with the multivariate wear model Assaf et al (2018), and then show how this is coupled with particle filter PF to enable effective prognostics of multi-component systems.…”

Planning the restoration of membranes in RO desalination using a digital twin

“…This rate-state dependence is captured in our model in a way that is different to wear models for multicomponent systems that have been considered to date. For a recent review of such models, see de Jonge and Scarf [20]. The degradation processes of membranes are mathematically developed and presented in Section 3.…”

“… models the wear in an element that accrues because the trailing elements (elements further along the vessel) are worn. This wear interaction is the multi-component rate-state effect [19,20,28]. The parameter  is the strength of this effect.…”

Modelling condition-based maintenance for nonidentical two-component systems considering four types of dependencies

“…However, assets nowadays are complex and composed of many components. The modeling of multicomponents maintenance systems presents various challenges, especially if interactions between components are considered (Nicolai and Dekker, 2008; Assaf et al , 2018). Dependencies among components can be divided into four main categories: stochastic, structural, economic and resource dependencies (Thomas, 1986; Olde Keizer et al , 2017).…”