This thesis introduces a modelling framework, which is developed for risk and performance assessment of large and complex systems with dynamic behaviours. The framework supports the most common reliability and operation modelling techniques, and permits their customisation. This ensures a high degree of freedom for the modeller to describe accurately the system without limitations imposed by an individual technique. The use of an object-oriented paradigm increases flexibility and decreases the semantics gap between the model and real world, which are issues with traditional techniques. The framework is named as Analysis of Things (AoT) to emphasise its universal nature and wide application possibilities. The AoT models are defined by using a Triplets data format, which is platform-independent and tabular. A single table declares the applied modelling techniques, creates the model structure, and assigns the parameter values. The format enables straightforward manual model edit while maintaining direct database compatibility. This thesis also documents a calculation engine, which has been developed for analysis of AoT models. The engine compiles dynamically the most efficient simulation algorithm for each modelling technique. A catalogue of built-in techniques is included in this thesis to demonstrate the application of the framework. The configuration of the simulation algorithm is presented for each technique. The AoT model creation is illustrated by using simple example models. Various techniques can be combined to build a comprehensive risk and performance model that systematically includes all essential details. The advanced features of the AoT framework have wide-ranging applications for analysis of reliability, availability, and operational performance of complex industrial products and processes.
|Place of Publication
|Published - 2020
|G4 Doctoral dissertation (monograph)
|Tampere University Dissertations - Tampereen yliopiston väitöskirjat