This paper shows the analysis and study of a bearingless ac Motor type divided winding for a conventional squirrel cage induction motor, (IM). The conventional IM is used as a bearingless motor for artificial-lift oil proposes, allowing a reduction in maintenance and operation costs. The goal of this paper is improving electromagnetic forces performance, such as the Lorentz and Maxwell forces, allowing rotor positioning at the rotation axis. It was used a simulator based on the finite element method for acquiring load results for a 3,75 kW, 380 V, 4-poles, 60 Hz, 1.04 Nm, IM motor. Experimental results show that is possible to use a conventional IM as a bearingless motor, achieving a 80% efficiency. This paper presents simulated and experimental results that demonstrated the operation of the IM bearingless-type split coil built from a conventional induction motor.

This paper shows some characteristics of a bearingless three-phase induction motor with divided windings independently controlled to generate radial and rotational forces. The analysis of this device has been performed by a two-dimensional finite-element model which has allowed the prediction of the machine performances in terms of current, torque, and force behavior. Some preliminary experimental results have been used to validate the model, then computational results have been considered to provide a radial force prediction in order to improve the control performances.

Three-phase induction motors play a key role in industrial operations. However, their failure can result in serious operational problems. This study focuses on the early identification of faults through the accurate diagnosis and classification of faults in three-phase induction motors using artificial intelligence techniques by analyzing current, temperature, and vibration signals. Experiments were conducted on a test bench, simulating real operating conditions, including stator phase unbalance, bearing damage, and shaft unbalance. To classify the faults, an Auto-Regressive Neural Network with Exogenous Inputs (NARX) was developed. The parameters of this network were determined through a process of selecting the best network by using the scanning method with multiple training and validation iterations with the introduction of new data. The results of these tests showed that the network exhibited excellent generalization across all evaluated situations, achieving the following accuracy rates: motor without fault = $94.2$%, unbalanced fault = 95%, bearings with fault = 98%, and stator with fault = 95%.

A device known as a pipeline inspection gauge (PIG) runs through oil and gas pipelines which performs various maintenance operations in the oil and gas industry. The PIG velocity, which plays a role in the efficiency of these operations, is usually determined indirectly from odometers installed in it. Although this is a relatively simple technique, the loss of contact between the odometer wheel and the pipeline results in measurement errors. To help reduce these errors, this investigation employed neural networks to estimate the speed of a prototype PIG, using the pressure difference that acts on the device inside the pipeline and its acceleration instead of using odometers. Static networks (e.g., multilayer perceptron) and recurrent networks (e.g., long short-term memory) were built, and in addition, a prototype PIG was developed with an embedded system based on Raspberry Pi 3 to collect speed, acceleration and pressure data for the model training. The implementation of the supervised neural networks used the Python library TensorFlow package. To train and evaluate the models, we used the PIG testing pipeline facilities available at the Petroleum Evaluation and Measurement Laboratory of the Federal University of Rio Grande do Norte (LAMP/UFRN). The results showed that the models were able to learn the relationship among the differential pressure, acceleration and speed of the PIG. The proposed approach can complement odometer-based systems, increasing the reliability of speed measurements.