An application of machine learning approach to fault detection of a synchronous machine

Accurate fault diagnosis systems should consider both the historical performance and the assessment of the current state of a machine. Manufacturing, installation, operation and maintenance are part of the machine's history and should be taken into account. The paper focuses on experimental procedures to develop a multi-criteria methodology to classify up to ten machine conditions. Using machine learning for signal processing techniques, any deviation from a normal steady state might be categorized as an abnormal behavior and, when demonstrated, a fault. To take advantage of machine learning algorithms, a significant amount of data is needed. To demonstrate the procedure the authors examined a synchronous machine. The authors recorded currents and voltages primarily, in stator and rotor winding, well as rotational speed and electromechanical torque. The collected signals were filtered and pre-processed, and to 5038 features were calculated and transformed into a tidy dataset. The sparse Linear Discriminant Analysis algorithm was used to extract the most important defined features. The results are shown in 3D scatter plots; in which each machine condition is represented. It is then possible to visualize the ability of the model to identify the most discriminant features. The same method can be used for the diagnostic of other types of machine conditions.