Qualitative Simulation (QS) is a common reasoning technique that represents the system performance based on qualitative reasoning. Reasoning is made to determine qualitative values with changing directions of the system variables. The QS approaches can be implemented with minimal knowledge of system parameters and even when the system model is incomplete, which are common scenarios for most machine users. Bond graph modelling simulates the dynamic systems based on cause and effect reasoning with the help of energy flow. Therefore this paper plans to use bond graph modelling for the purpose of fault detection based on the energy flow without the need for the specific information of machine's parameters. The bond graph based QS was examined on an AC induction motor (ACIM) which had confirmed the high efficiency of QS based approach in achieving diagnostic results for broken rotor bar. The QS approach uses the temporal causal graph (TCG) and qualitative equations of the system. The applied qualitative analysis predicts the effects of parameter changes (increased-decreased) on the whole system behavior. The results indicate that the proposed QS technique is an effective method for extracting diagnostic information, leading to an accurate diagnosis by combining TCG and qualitative equations with qualitative reasoning.
|Conference||25th IEEE International Conference on Automation and Computing|
|Abbreviated title||ICAC 2019|
|Period||5/09/19 → 7/09/19|