Growing Curvilinear Component Analysis (GCCA) for Stator Fault Detection in Induction Machines

Giansalvo Cirrincione (1) , Vincenzo Randazzo (2) , Rahul R. Kumar , Maurizio Cirrincione (3) , Eros Pasero (4)

1 LTI - Laboratoire des technologies innovantes - UR UPJV 3899
2 Politechnico di Torino
3 USP - University of the South Pacific
4 DET - Department of Electronics and Telecommunications [Torino]

Fault diagnostics for electrical machines is a very difficult task because of the non-stationarity of the input information. Also, it is mandatory to recognize the pre-fault condition in order not to damage the machine. Only techniques like the principal component analysis (PCA) and its neural variants are used at this purpose, because of their simplicity and speed. However, they are limited by the fact they are linear. The GCCA neural network addresses this problem; it is nonlinear, incremental, and performs simultaneously the data quantization and projection by using the curvilinear component analysis (CCA), a distance-preserving reduction technique. Using bridges and seeds, it is able to fast adapt and track changes in the data distribution. Analyzing bridge length and density, it is able to detect a pre-fault condition. This paper presents an application of GCCA to a real induction machine on which a time-evolving stator fault in one phase is simulated.

Domaines: Sciences de l'ingénieur [physics]

Giansalvo Cirrincione, Vincenzo Randazzo, Rahul R. Kumar, Maurizio Cirrincione, Eros Pasero. Growing Curvilinear Component Analysis (GCCA) for Stator Fault Detection in Induction Machines. Esposito, A and FaundezZanuy, M and Morabito, FC and Pasero, E. NEURAL APPROACHES TO DYNAMICS OF SIGNAL EXCHANGES, 151, pp.235-244, 2020, Smart Innovation, Systems and Technologies, 978-981-13-8950-4; 978-981-13-8949-8. ⟨10.1007/978-981-13-8950-4\_22⟩. ⟨hal-03631435⟩