Model reduction based on sparse identification techniques for induction machines: Towards the real time and accuracy-guaranteed simulation of faulty induction machines
TypeArticles dans des revues avec comité de lecture
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.
Showing items related by title, author, creator and subject.
Induction machine model with finite element accuracy for condition monitoring running in real time using hardware in the loop system SAPENA-BAÑÓ, Angel; CHINESTA, Francisco; AGUADO, Jose Vicente; BORZACCHIELLO, Domenico; PUCHE-PANADERO, Rubén (Elsevier Ltd, 2019)Most industrial processes are run by induction machines (IMs). Condition monitoring of IM assures their continuity of service, and it may avoid highly costly breakdowns. Among the methods for condition monitoring, on-line ...
Tape surface characterization and classification in automated tape placement processability: Modeling and numerical analysis ARGERICH, Clara; IBÁÑEZ, Rubén; LEÓN, Angel; ABISSET-CHAVANNE, Emmanuelle; CHINESTA, Francisco (AIMS Press, 2018)Abstract: Many composite forming processes are based on the consolidation of preimpregnated preforms of different types, e.g., sheets, tapes, .... Composite plies are put in contact using different technologies and ...
ANGEL, Leon; BARASINSKI, Anais; ABISSET-CHAVANNE, Emmanuelle; CUETO, Elias; CHINESTA, Francisco (Elsevier, 2018)Separated representations at the heart of Proper Generalized Decomposition are constructed incrementally by minimizing the problem residual. However, the modes involved in the resulting decomposition do not exhibit a clear ...
Multi-scale modeling and simulation of thermoplastic automated tape placement: Effects of metallic particles reinforcement on part consolidation LEÓN, Angel; PEREZ, Marta; BARASINSKI, Anaïs; ABISSET-CHAVANNE, Emmanuelle; DEFOORT, Brigitte; CHINESTA, Francisco (MDPI AG, 2019)This paper concerns engineered composites integrating metallic particles to enhance thermal and electrical properties. However, these properties are strongly dependent on the forming process itself that determines the ...
LEÓN, Angel; ARGERICH MARTÍN, Clara; BARASINSKI, Anaïs; SOCCARD, Eric; CHINESTA, Francisco (Springer, 2019)Automated tape placement - ATP - is a recent manufacturing technology for composite materials. Therefore, a correct modeling of the multi-physical process is critical in order to make possible in-situ consolidation. In ...