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%.

In this paper, a radial position control approach of a bearingless induction motor is proposed. The rotor is supported by magnetic forces, which is complex to model. Generally, simplifications are adopted to determine a linearized model that hinders classical controllers’ performance. On the other hand, fuzzy controllers have non-linear characteristics and do not require precise mathematical models. Instead, it uses the experience and knowledge of human operators to build a knowledge base to be used on the control. The prototype under study is a three-phase induction motor without a rated power of 1 HP, 4-pole, 220/380V and rated current 3/1.75A. It operates on a vertical axis, so there is no need to compensate for the rotor weight. The paper proposes the implementation of a Mamdani Fuzzy PD controller in order to control the rotor radial position of a three-phase induction motor by means of a Texas Instruments TMS320F28335 Digital Signal Processor (DSP). We present experimental results that validate the good performance of the fuzzy controller.

The electrical submersible pump (ESP) is widely used in the oil industry as an artificial lift method in deep wells, and its primary application of ESP is to increase the oil flow rate. Information such as the pump intake and discharge pressure, the oil reservoir and motor winding temperature, etc., provide critical data necessary to ensure proper ESP system operation and optimize production. In this article, an ESP downhole parameter monitoring system is proposed. Potentiometers are used to generates analog signals, emulating the analog sensors. The information is sent up to the surface based on the current loop through the same power cable by DC signals overlapping to AC signals in the proposed design. The article details the construction of the communication protocol and electronic circuits used in telemetry. The tests were carried out in an oil-producing company to validate the proposed telemetry system’s reliability and accuracy. The good results of the robust design demonstrated it to be a promising downhole sensor for industrial applications.

In order to reduce the costs of implementing the radial position control system of a three-phase bearingless machine with split winding, this article proposes a driving method that uses only two phases of the system instead of the three-phase traditional one. It reduces from six to four the number of inverter legs, drivers, sensors, and current controllers necessary to drive and control the system. To justify the proposal, this new power and control configuration was applied to a 250 W machine controlled by a digital signal processor (DSP). The results obtained demonstrated that it is possible to carry out the radial position control through two phases, without loss of performance in relation to the conventional three-phase drive and control system.

This paper presents the development and implementation of a centralized industrial network for an automatic purified water production system used in the pharmaceutical industry. This implementation is part of a project to adapt an industrial plant to cope with advances in industrial technology to achieve the level of Industry 4.0. The adequacy of the instruments and the interconnection of the controllers made it possible to monitor the process steps by transforming a manual plant, with discontinuous production into an automated plant, improving the efficiency and quality of the produced water. The development of a supervisory system provides the operator with a panoramic view of the process, informing in real-time the behavior of the variables in the process steps, as well as storing data, event history and alarms. This system also prevented the collection of erroneous or manipulated data, making the process more transparent and reliable. Accordingly, we have been able to tailor this water treatment plant to operate within the minimum requirements required by the regulator.

This paper presents systematic computational algorithms for obtaining the output feedback gain matrix in linear systems stabilization problems. Based on the concept of (C,A,B)-invariant subspaces, introduced previously by the first author, that has related the existence of a gain matrix to the solution of coupled Sylvester equations, two algorithms are presented: 1) in the Syrmos-Lewis algorithm, a modification is proposed to provide a more adequate framework to numerical solution, and 2) by using orthogonal transformations, the Alexandridis-Paraskevopoulos algorithm is modified to overcome, in part, the Kimura condition. Numerical examples are provided to illustrate the application of the proposed algorithms.