**6. Conclusions**

150 Fuzzy Inference System – Theory and Applications

result of a previous selection of tags formed by numerical extremes to differentiate the states

where the motor is.

Fig. 16. Mean under bearing failure

Fig. 17. Mean under broken bars failure

The study of vibration in rotating electrical machines through ANFIS requires the use of signal conditioning tools, which are introduced through the training and test arrays. Special care should be taken with some overlapping modes, especially in those failures that, due to their nature, do not generate large perturbations in oscillations, but represent an imminent risk to the engine's life.

The failures considered in the electrical machines studied, reflected changes in the three axes x, y and z. However, they are most noticeable in those that are axial to the axis of rotation, allowing the detection of failures through the analysis on a single axis, instead opening the way for the use of less sophisticated sensors, reducing the implementation costs.

In the inference process it is quite attractive to use pragmatic strategies to handle large amount of measured information, and able to identify the machinery's operating condition.

The errors between the check and learning curves for the two types of studied failures are satisfactory for identification purposes in both cases. Thus, ANFIS has been successfully applied to distinguish between such failures.
