Although Maintenance data is crucial for authoritative reporting reasons and is generally used to optimize maintenance planning in terms of budget, scheduling and logistics, the potentials of the implicit given information for Prognostics and Health Management (PHM) frameworks are not yet completely leveraged. Traditional PHM frameworks typically rely exclusively on sensor data to derive a system’s health status, while maintenance, repair and overhaul (MRO) data is not investigated. However, maintenance data contains valuable information on which part of a system is checked, serviced or replaced. In the presented work, a novel approach to fusion maintenance data into a traditional (sensor-based) PHM/condition monitoring framework is introduced. This fusion enables a model update of the condition monitoring framework and hence improves its diagnostics performance in terms of classification accuracy. The presented work uses data from a simulation framework to develop and evaluate the method. A sensitivity analysis shows influences of various sources of uncertainty and constraints of the approach. First results do not show significant improvements compared to a benchmark approach, but the variety of setting parameters in the simulation environment and their influence on uncertainty are subject of further research.
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Probabilistic Modelling, Data fusion, Model Update, CBM, Maintenance Data
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