**4. Case study**

The machine used in this study is a 1 HP induction motor, where the load is represented by an alternator coupled to the motor through a band. The alternator feeds a bank of resistors, Fig. 8. Vibration measurements of machines in good condition and under fault conditions are captured and processed in ANFIS to simulate an inference process to identify the occurrence of a specific failure.

Fig. 8. Induction motor's arrangement

In this application, measurements are taken by a 12-bit LIS3L02ASA vibration sensor (accelerometer based on MEMS - Microelectromechanical system), which provides measurement of displacement in three axes. Additionally, to reduce the noise/signal

Thus, the triaxial accelerometer, is one of the most important parts of the instrumentation system, being located in the engine body, which measures vibrations based on three axes (x, y, z) using a sampling rate of 1500Hz. The ADS7841 is a converter equipped with serial synchronous communication interface with 200KHz conversion rate. After the digitalized data is sent via the RS232 card to capture, the system data

The sensor provides vibration measurements in three axes. In this research, it was noticed that the perpendicular axes to the axis of rotation have more useful information to identify a failure occurrence. Thus, in order to optimize the computational load, data from the x-axis

The machine used in this study is a 1 HP induction motor, where the load is represented by an alternator coupled to the motor through a band. The alternator feeds a bank of resistors, Fig. 8. Vibration measurements of machines in good condition and under fault conditions are captured and processed in ANFIS to simulate an inference process to identify the

Fig. 7. Stages of the inference system

acquisition uses a MAX3243 circuit.

occurrence of a specific failure.

were used.

**4. Case study** 

proportion, filtering is added.

Fig. 9. Vibrations under different conditions

The proposed hybrid method aims to identify the fault states in rotating machines, distinguishing the smooth operation from failure conditions by measuring vibration signals. Vibration measurements have been monitored in three axes: x, y, and z under the following operating conditions:

Fuzzy Inference Systems Applied to the Analysis of Vibrations in Electrical Machines 147

differentiable through level changes observed in the data. Labels are selected by the user to

have a reference, which is the state that the machine is undergoing.

Fig. 10. Bearing failure ANFIS without wavelet decomposition

Fig. 11. A machine in good condition: wavelet decomposition, n=2.

Additionally, Figures 14-15 display the Root Mean Squared Error (RMSE) between the checking and training curves, for both failures, where the RMSE is a quadratic scoring rule, which measures the average magnitude of the error. Expressing the expression in words, the difference between the forecasted and the corresponding observed values are each squared


In the case of bearing failure, there is a minimally invasive phenomenon in the machine's vibration, contrary to a broken bars failure, which gives rise to notorious vibration, Fig 9. The preliminary coarse filtering process is performed by the assembled sensor.

Likewise, at a first glance, the vibrations in the axial direction are more noticeable. Thus, their measurements are employed in the following analysis.
