A New Approach to Study the Effect of Complexity on an External Gear Pump Model to Generate Data Source for AI-Based Condition Monitoring Application

The external gear pump, like any other hydraulic component, is vulnerable to failure, which may lead to downtime as well as the failure of other components linked to it, thereby causing production loss. Therefore, establishing a condition monitoring system is crucial in identifying failure at an early stage. Traditional condition monitoring approaches rely on experimental data that are collected by means of sensors. However, the sensors utilized in the experiments may have calibration issues, which lead to inaccurate measurements. The availability of experimental data is also limited as it is difficult and expensive to create and detect a fault in a component. Hence, it is essential to develop a simulation model that mimics the performance of the actual system. The data generated from the model can be utilized to create the data source required for automated condition monitoring. A new methodology based on a detailed geometric model for simulating the External Gear Pump is described and compared to two models analyzed in the authors' previous work, namely Schlosser's loss model and simple geometric model. In this paper, the three models are compared with experimental data and the method utilized for fault injection. Schlosser's loss model, as well as the detailed geometric model, are found to be suitable in terms of validation; however, the latter is a better candidate in terms of fault injection. Hence, the detailed geometric model can be implemented as a tool to generate the data source for condition monitoring applications.