Background: Bug-fixing is the crux of software maintenance. It entails tending to heaps of bug reports using limited resources. Using historical data, we can ask questions that contribute to better-informed allocation heuristics. The caveat here is that often there is not enough data to provide a sound response. This issue is especially prominent for young projects. Also, answers may vary from project to project. Consequently, it is impossible to generalize results without assuming a notion of relatedness between projects.

This work is an investigation about the use of a Multilayer Perceptron Artificial Neural Network (MLP ANN) to detect stator winding short-circuit faults in a converter-fed induction motor. The algorithm uses six frequency components from the current spectrum as input variables. The data (samples) was acquired varying: (1) the frequencies imposed by inverter drive, (2) the load level, and (3) the fault extension of the induction motor. This articles approach is to investigate the influence of several aspects related to fault emulation and attributes selection over the categorization capacity of the classifier. Several hypotheses about those aforementioned influences are raised and analyzed. At the end, a classifier capable to identify the fault evolution is proposed and evaluated.