Enhancing the diagnostic performance of Condition Based Maintenance through the fusion of sensor with maintenance data

Although Maintenance data is crucial for authoritative reporting reasons and is generally used to optimizemaintenance planning in terms of budget, scheduling and logistics, the potentials of the implicit given informationfor Prognostics and Health Management (PHM) frameworks are not yet completely leveraged. Traditional PHMframeworks typically rely only on sensor data to derive a system’s health status, while maintenance, repair andoverhaul (MRO) data is not investigated. However, maintenance data contains valuable information on which partof a system is checked, serviced or replaced. At the same time, maintenance data is necessary for the labelling ofsensor data, the differentiation of multiple failure modes and includes the expert knowledge of the worker. Theoverall goal of the presented work is enable a model update through the integration of this information into atraditional (sensor-based) PHM/condition monitoring framework.In this context, the underlying data bases and structures will be analyzed and a generalized methodology isproposed to include maintenance data directly into the forward-modelling phase of a PHM/condition monitoringframework. The main goal is not only to use the labels derived from maintenance data for evaluation purposes(which is a common practice in PHM research), but to use this data to build a memory of the maintenance andhealth state history and thereby enhance the diagnostic capabilities of the framework. Methods from the field ofProbabilistic Programming and Bayesian Statistics seem promising and are implemented in order to incorporatefor uncertainties and to enable a confidence level for the diagnosis. The proposed concept is developed, tested andassessed in a simulation environment, allowing to investigate the influence of data confidence and label uncertaintyon the results. Furthermore, this allows to derive specific requirements for the input data and hence for the dataacquisition in the real world. The proposed concept is described in a generic way to be applicable on differentengineering domains (e.g. wind turbine or production machinery industry), but it will be tested and evaluated on areal world aviation use case. This concluding use case is defined in the context of the project INDI at TU Darmstadt(Intelligent Data Utilization in Maintenance) in cooperation with the project partner Lufthansa Technik AG.