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Salient Pole Synchronous Generators (SPSG) are known for their robustness and stability; However, internal faults like rotor interturn short circuits (ITSC) might still occur and lead to unscheduled machine shutdowns if not caught early. The literature focuses mainly on high-speed SPSGs and is short on studies covering the diagnosis of low-speed machines. To bridge this gap, this paper presents a non-invasive diagnosis method for low-speed SPSG used by Hydro-Québec. The proposed approach is based on real measurements of stray flux signals and faulty synthetic signals, obtained by FEM simulations. The Convolutional Variational AutoEncoder (CVAE) is used to cluster signals according to the fault severity, and to visualize them in 2D space. Furthermore, two studies were conducted to compare the performance and robustness of the CVAE against the ${\bm{RMS}}$ standard method. The results demonstrate that the CVAE is more sensitive and reliable in detecting ITSCs in large hydrogenerators. Finally, a case study was conducted to validate the proposed method using a real faulty dataset, confirming the obtained results.

This article presents a non-intrusive technique for detecting the rotor inter-turn short circuit (RITSC) of a hydrogenerator using an artificial intelligence (AI) based variational AutoEncoder (VAE). The technique is applied to a large hydrogenerator of 74 MVA and 76 poles, to test its health monitoring and classification potential. The model is trained and validated based on the acquisition of real vibratory data collected in situ from a healthy machine. The frequency pattern of the fault in the vibration signal is obtained based on finite element methods (FEM). Then, to test the sensitivity of the model in early fault detection, the signature is injected into another set of real healthy vibration signals, and the results are compared to those obtained using the traditional vibration monitoring technique. Furthermore, clustering in the latent space of the model is explored. The obtained results prove the ability of this technique and its potential in detecting anomalies at earlier stages as well as its capacity to cluster different degrees of severity of the fault in a 3D user-friendly space.