Fault Diagnosis using Shallow Neural Networks for Voltage Source Inverters in SynRM Drives

This paper presents a neural-based fault diagnosis system for a two-level Voltage Source Inverter that is used to drive the Synchronous Reluctance Motors. In particular, three classes are considered: Healthy, Open Circuit Fault (OSF) and Short Circuit Fault (SCF). The proposed strategy relies on the data generated by the mathematical models of OSF and SCF together with the healthy configuration. For each category of fault, multi-switch faults have been emulated. Following the acquisition of healthy and faulty three-phase currents, exploratory analysis of the data is conducted using Principal Component Analysis. Thereafter, various Machine Learning techniques have been utilized to develop different types of classifiers. The best classification model after considering factors like time complexity, storage, confidence level of outputs was the shallow Long-Short-Term-Memory neural network.