Search results

The purpose of this paper is to solve an inverse problem of structure recognition arising in eddy current testing (ECT) – type NDT. For this purpose, the space mapping (SM) technique with an extraction based on the Gauss‐Newton algorithm with Tikhonov regularization is applied.

The aim is to have a computationally fast recognition procedure of defects since the monitoring results in a large amount of data points that need to be analyzed by 3D eddy current model. According to the SM optimization, the finite element method (FEM) is used as a fine model, while the model based on an integral method such as the volume integral method (VIM) serves as a coarse model. This approach, being an example of a two‐level optimization method, allows shifting the optimization load from a time consuming and accurate model to the less precise but faster coarse surrogate.

The application of this method enables shortening of the evaluation time that is required to provide the proper parameter estimation of surface defects.

In this work only the specific kinds of surface defects were considered. Therefore, the reconstruction of arbitrary shapes of defects when using real measurement data from ECT system can be treated in further research.

The paper investigated the eddy current inverse problem. According to aggressive space mapping method, a suitable coarse model is needed. In this case, for the purpose of 3D defect reconstruction, the reduced VIM approach was applied. From a practical view point, the authors demonstrated that the two‐level inversion procedures allow saving of up to 50 percent CPU time in comparison with the optimization by means of regularized Gauss‐Newton algorithm in the same FE model.

Analysis of test data monitored for a number of electric machines from the low volume production line can lead to useful conclusions. The purpose of this paper is to trace the machine performance to find quality-related issues and/or identify assembly process ones. In this paper, the monitoring of experimental data is related to the axial flux motor (AFM) used in hybrid electric vehicle (HEV) and in electric vehicle (EV) traction motors in the global automobile market.

Extensive data analyses raised questions like what could be the causes of possible performance deterioration of the AFM and how many electric motors may not pass requirements during operation tests. In small and medium research units of AFM for HEV or EV, engineers came across a number of serious issues that must be resolved. A number of issues can be eliminated by implementing methods for reducing the number of failing AFMs. For example, improving the motor assembly precision leads to reduction of the machine parameters deterioration.

Assembly tolerances on electric motor characteristics should be investigated during motor design. The presented measurements can be usable and can point out the weakest parts of the motor that can be a reason for the reduced efficiency and/or lifetime of the AFM. Additionally, the paper is addressed to electric motor engineers designing and/or investigating electric AFMs.

Performance of AFM was monitored for a number of identical motors from low volume production line. All tested motors were operated continuously for a long period of time and the tests were repeated every few weeks for half a year to check the reliability of motor design and indicate how much the motor parameters may change. The final results point how many motors fail the requirements of motor performance. A few batches of AFM were selected for testing. Each batch represents a different size (nominal power) of the same type of AFM.