Diagnosis methodology based on statistical-time features and linear discriminant analysis applied to induction motors

The development of condition monitoring strategies is necessary to ensure the efficiency and reliability of the operation on electric machines. The feature calculation is an important signal processing step used to obtain a characterization related to the working condition of machinery. In order to address this issue, this work proposes a diagnosis methodology based on the calculation of a statistical-time set of features applied to identify the appearance of different faults in an induction motor. In the proposed methodology three acquired stator current signals are characterized by calculating its statistical-time features. Then, such statistical-time sets of features are compressed and represented into a 2-dimentional space through Linear Discriminant Analysis. And, finally a Neuro Fuzzy-based classifier is used to diagnose the different considered conditions. The performance of the proposed diagnosis methodology is evaluated in an experimental test bench; the obtained results make the proposed methodology suitable to be applied in industrial processes.