title: Prognosis based decision support for improved system availability
Creators: name: Heier, Henrik
Publisher: name: UNSPECIFIED
Year: 2024

description: Complex technical systems such as aircraft, power plants or manufacturing systems play a vital role in our modern world as large portions of society rely on their correct functioning every day. The achievement of high system availabilities is therefore an important goal for system operators. Still, an accurate estimation of a system's future capability is not trivial as modern systems are often composed of many individual parts and subsystems and are often operated in varying modes or changing environments. Traditional statistics-based methods from Reliability Engineering reach their limits here as they struggle to capture the true current system state. An alternative approach is given with the discipline of Prognostics and Health Management (PHM) that uses onboard sensors to estimate the current and future health status based on real measurements. A still open research question in this context is how to aggregate PHM results of a multi-component system and integrate them into the exiting reliability frameworks in order to obtain a practical and useful decision support that provides accurate system state predictions.

With this thesis, the described problem is addressed and a Dynamic Hybrid Reliability Model (DHRM) is developed. Therefore, the DHRM combines traditional methods from Reliability Engineering with the novel PHM approach. Based on the V-Model methodology, it is described how the DHRM is constructed, implemented, verified and evaluated. The overall concept is described on three different aggregation levels, namely the part-, component- and system-level. On the part-level the integration of PHM results in a reliability context is highlighted, while on the component-level a fault-tree model is used for further aggregation. For the system-level, a state-space model is then used to represent the individual states of the considered system. The conception of the DHRM method concludes by showing how uncertainties are considered throughout the model and by the implementation of a software prototype for the calculation of the DHRM.
 
The DHRM is then applied to the Control Surface Actuation System (CSAS) of a hybrid drone to examine its applicability. The degradation of the CSAS is simulated with generated failure data based on a stochastic differential equation framework and predicted with a PHM algorithm based on a Gaussian Process regression model. To assess the capabilities of the novel method, the CSAS is calculated by the DHRM for different parameter combinations and compared to a reference case without any dynamic PHM input data. During an evaluation phase, the results of the DHRM are then quantified based on a set of predefined metrics and compared to the reference case. It is shown that the DHRM outperforms the reference method in accuracy and precision. 

The thesis concludes with a discussion of the results. The main advantages of the novel DHRM are seen in its capability of not only accurately predicting upcoming state transitions, but also providing a precise estimation of the actual new state the system will enter. This is seen as a major advantage of this method as it allows operators to plan ahead with a precise knowledge about the future system capabilities. However, the approach also has its merits and limits as the quality of the DHRM results largely depend on the accuracy and availability of the required underlying PHM data.