Model reduction based on sparse identification techniques for induction machines: Towards the real time and accuracy-guaranteed simulation of faulty induction machines

The development of condition monitoring (CM) systems of induction machines (IMs) is essential for the industry because the early fault detection would help engineers to optimise maintenance plans. However, the use of several IMs to test and validate the fault diagnosis methods developed requires also costly test benches that, anyway, often face limitations in the range of faults and operating conditions to be tested. To avoid it, the use of accurate models such as those based on finite element method (FEM) would reduce the major drawbacks of test benches but their inability to execute FEM models in real time largely reduces their application in the development of on-line continuous monitoring systems. To alleviate this problem a hybrid FEM-analytical model has been proposed. It uses an analytical model that can be run in real-time in a hardware in the loop (HIL) system, after its parameters have been computed through FEM simulations. In this way, the proposed model provides high accuracy but at the cost of long simulation times and high computational costs (both computing power and memory resources) to compute the IM parameters. This work aims at reducing these drawbacks. In particular, a model based on sparse identification techniques is proposed. The method balances complexity and accuracy by selecting a sparse model that reduces the number of FEM simulations to accurately compute the coupling parameters of an IM model with different fault severity degrees. Particularly, the proposed methodology has been applied to develop models with abnormal eccentricity levels as this fault is related to development of mechanical faults that produce most of IM breakdowns