The integration of Sensing Machine Elements (SME) is a promising approach to obtain reliable data about relevant process and state variables of technical systems. However, the quality and reliability of the provided data strongly depends on the corresponding calculation model of the SME and the therein included uncertainty. Consequently, in this contribution, the calculation model of a sensory utilized rolling bearing, as exemplary SME, is systematically analyzed using existing methods and tools to identify uncertainty that critically affects the quality and reliability of the data provided.
The importance of considering disturbance factors in the product development process is often emphasized as one of the key factors to a functional and secure product. However, there is only a small number of tools to support the developer in the identification of disturbance factors and none of them yet ensures that the majority of occurring disturbance factors is considered. Thus, it is the aim of this contribution to provide a tool in form of a control list for the systematic identification of disturbance factors. At the beginning of this contribution, the terms “disturbance factor” and “uncertainty” are defined based on a literature review and different approaches for the classification of uncertainty are presented. Subsequently, the fundamentals of multipole based model theory are outlined. Moreover, a first approach in terms of a control list for a systematic identification of disturbance factors is discussed. Based on the discussed approach and taking the identified weaknesses as a starting point, a control list is presented that combines the existing basic concept of the control list with the fundamentals of multipole based model theory.
When integrating sensing machine elements for in-situ measurements in technical systems, special attention must be paid to uncertainty to ensure the reliability of the provided information. Therefore, a methodical framework for the identification, analysis and consideration of uncertainty was already developed in prior research, which still offers room for improvement regarding the included methods and tools. Therefore, in this contribution, the initially proposed methods and tools are adapted and extended to enhance their efficiency and applicability and to reduce their error proneness in order to increase the acceptance of the framework in practice. First, the identification of uncertainty is improved by means of an extended effect graph for an automated identification of disturbance factor induced data and model uncertainty. Second, the significance of the subsequent evaluation of uncertainty is enhanced by replacing the initially proposed local sensitivity analysis with a global sensitivity analysis. Finally, a flowchart is proposed that supports the identification of applicable and promising strategies for the development of measures to consider critical disturbance factor induced uncertainty.
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