Part of the book: Induction Motors
In this chapter, motor current signature analysis based on squared envelope spectrum is applied in order to identify and to estimate the severity of outer race bearing faults in induction machine. This methodology is based on conventional vibration analysis techniques, however, it is, non-invasive, low cost, and easier to implement. Bearing fault detection and identification in induction machines is of utmost importance in order to avoid unexpected breakdowns and even a catastrophic event. Thus, bearing fault characteristic components are extracted combining summation of phase currents, prewhitening, spectral kurtosis and squared envelope spectrum analysis. Experimental results with a 0.37 W, 60 Hz, and three-phase induction machine demonstrated the methodology effectiveness.
Part of the book: Bearing Technology
The evolution of communication technology and the reduction of its costs have driven several advances in measurement systems. Points that could not be measured before can now be monitored. Points with difficulty to reach or with major security restrictions can begin to have their quantities measured and informed to control centers. This chapter presents one of these evolutions showing a current transducer (CT), which can measure this magnitude, make an initial treatment of the signal, and transmit it to a panel or control center. Besides, this current transducer does not require an energy source to operate, being self-powered by the current it is measuring. Because it is inexpensive, it can be spread through the facilities, supplying the current at various points of the observed electrical network. With signal treatment, useful information can be inserted in this device so that it informs already preprocessed elements to reading devices, becoming part of the world of IoT. This article presents its use in motor condition monitoring at the Pimental hydroelectric power plant.
Part of the book: New Trends in the Use of Artificial Intelligence for the Industry 4.0