Induction Machine Stator Fault Tracking Using the Growing Curvilinear Component Analysis

• Main Authors: Rahul R Kumar, Vincenzo Randazzo, Giansalvo Cirrincione, Maurizio Cirrincione, Eros Pasero, Andrea Tortella, Mauro Andriollo
• Format: Article
• Language: English
• Published: IEEE 2021-01-01
• Series: IEEE Access
• Subjects: Data streaming analysis; growing curvilinear component analysis; induction machine; neural networks; on-line fault diagnosis; principal component analysis
• Online Access: https://ieeexplore.ieee.org/document/9306809/

Detection of stator-based faults in Induction Machines (IMs) can be carried out in numerous ways. In particular, the shorted turns in stator windings of IM are among the most common faults in the industry. As a matter of fact, most IMs come with pre-installed current sensors for the purpose of control and protection. At this aim, using only the stator current for fault detection has become a recent trend nowadays as it is much cheaper than installing additional sensors. The three-phase stator current signatures have been used in this study to observe the effect of stator inter-turn fault with respect to the healthy condition of the IM. The pre-processing of the healthy and faulty current signatures has been done via the in-built DSP module of dSPACE after which, these current signatures are passed into the MATLAB^® software for further analysis using AI techniques. The authors present a Growing Curvilinear Component Analysis (GCCA) neural network that is capable of detecting and follow the evolution of the stator fault using the stator current signature, making online fault detection possible. For this purpose, a topological manifold analysis is carried out to study the fault evolution, which is a fundamental step for calibrating the GCCA neural network. The effectiveness of the proposed method has been verified experimentally.