Rodrigo Henrique Cunha Palácios

Evaluation of stator winding faults severity in inverter-fed induction motors

Graphical abstractDisplay Omitted HighlightsPresent a comprehensive evaluation of intelligent classifiers to identify stator faults in inverter-fed induction motors are presented.Proposed methodology uses the current signal in time domain as the inputs of the pattern classifiers for fault diagnosis.Experimental results with different inverters, operating frequencies and mechanical loads are presented.Three different intelligent methods are presented and compared for multiple faults under dynamic sampling rate. Three-phase induction motor are one of the most important elements of electromechanical energy conversion in the production process. However, they are subject to inherent faults or failures under operating conditions. The purpose of this paper is to present a comparative study among intelligent tools to classify short-circuit faults in stator windings of induction motors operating with three different models of frequency inverters. This is performed by analyzing the amplitude of the stator current signal in the time domain, using a dynamic acquisition rate according to machine frequency supply. To assess the classification accuracy across the various levels of faults severity, the performance of three different learning machine techniques were compared: (i) fuzzy ARTMAP network; (ii) multilayer perceptron network; and (iii) support vector machine. Results obtained from 2.268 experimental tests are presented to validate the study, which considered a wide range of operating frequencies and load conditions.

A novel multi-agent approach to identify faults in line connected three-phase induction motors

Graphical abstractDisplay Omitted HighlightsPresent a novel multi-agent approach to identify stator, rotor and bearing faults in three-phase induction motors.Proposed methodology uses the current amplitudes signal in time domain as the inputs of the multi-agent system for fault diagnosis.The multi-agent system incorporates pattern recognition techniques with better results for each type of fault.Experimental results gathered from three-phase induction motors operating with different load conditions and fed under unbalance voltage are provided. Three-phase induction motors (TIMs) are the key elements of electromechanical energy conversion in a variety of productive sectors. Identifying a defect in a running motor, before a failure occurs, can provide greater security in the decision-making processes for machine maintenance, reduced costs and increased machine operation availability. This paper proposes a new approach for identifying faults and improving performance in three-phase induction motors by means of a multi-agent system (MAS) with distinct behavior classifiers. The faults observed are related to faulty bearings, breakages in squirrel-cage rotor bars, and short-circuits between the coils of the stator winding. By analyzing the amplitudes of the current signals in the time domain, experimental results are obtained through the different methods of pattern classification under various sinusoidal power and mechanical load conditions for TIMs. The use of an MAS to classify induction motor faults allows the agents to work in conjunction in order to perform a specific set of tasks and achieve the goals. This technique proved its effectiveness in the evaluated situations with 1 and 2hp motors, providing an alternative tool to traditional methods to identify bearing faults, broken rotor bars and stator short-circuit faults in TIMs.

Fuzzy Logic Applied at Industrial Roasters in the Temperature Control

This paper presents an approach of temperature control in coffee roasters inserted in an industrial environment by using intelligent systems. The control is based on fuzzy logic. The linguistic variables of input and output and their respective membership functions, which will be used in the implementation of control system were created based on the experience of human operators. The fuzzy controller can be applicable to systems which have no adequate methodology for its control, due for instance, the difficulty or impossibility of obtaining a mathematical model that adequately describes the process. In this specific application, simulation results showed the feasibility of using a fuzzy controller for providing reference signals for PID controllers, thereby increasing operational efficiency of the process.

Applying mindstorm in teaching and learning process and software project management

Some problems related to software process and project management improvement programs are detected during their implementation. Companies from the software projects segment have difficulty dealing with software Project management and quality models (CMMI, MPS-BR and PM-BOK) since the ways to implement the key areas of the process and the good practices inherent to Project management are interpreted with difficulty. This can be attested by the analysis of CMMI and CHAOS reports. In order to minimize the difficulties detected by the reports, the aim of this paper is to apply Mindstorms to the teaching and learning of software process and project management (key concepts used during process improvement and implementation). To validate the efficacy of the technique introduced in this paper, 8 experiments were carried out including university, high school courses and companies from the software production sector. Experimental results attest that the process of knowledge transfer in process and management using Mindstorms led to greater motivation, satisfaction and concepts consolidation.

Diagnosis of Stator Faults Severity in Induction Motors Using Two Intelligent Approaches

Three-phase induction motors are the primary means of transformation of electrical energy into mechanical energy in industry, since they are robust and present low cost. However, despite being robust, these machines are subject to electrical or mechanical faults. Thus, identifying a defect in a running motor may decrease the risk of possible damage. This paper proposes an alternative approach to identify defects in the stator of these motors, by analyzing current signals in the time domain. In addition, it presents the determination of the consequent fault severity by means of two proposals: 1) a multiagent system with a classifier behavior; and 2) a neural estimator. The faults observed are related to short circuits between turns in the stator coil of 1% to 10%. Experimental results are observed with motors of different powers, under various adverse situations of electrical feed and a wide range of load conditions on the machine shaft.

An application of artificial neural networks and PCA for stator fault diagnosis in inverter-fed induction motors

This paper presents a method for fast classification of stator short-circuit faults in a inverter-fed induction motor operating at steady-state, under a wide range of speed and load conditions. Induction motors are widely used in several industrial applications due to its robustness, low cost and high reliability. Early detection and proper fault diagnosis reduce the maintenance cost and also increase process effectiveness. The amplitude of the stator current signal, in the time domain is presented as input to a multilayer perceptron (MLP) network for the classification of stator faults. After a proper discretization of the current signal, the technique of principal components analysis (PCA) is applied allowing a reduction of the classifier complexity. Results obtained from 750 experimental tests are provided and compared to validate this study. The obtained results indicate that this approach can be employed to classify stator short-circuit faults in inverter-fed induction motors.

Intelligent systems applied on the estimation of bearing faults in inverter-fed induction motors

This paper proposes an approach based on intelligent systems for the classification and diagnosis of bearing fault evolution in inverter-fed induction motors, operating at steady state under a wide range of frequencies and load torque. Due to its robustness and low cost, induction motors are used in various industrial applications. In this work, the classifiers Fuzzy Artmap (FAM), Support Vector Machine - Sequential Minimal Optimization (SVM/SMO), k-Nearest Neighbours (k-NN) and Multilayer Perceptron (MLP) are used for the diagnosis and classification of bearing faults. Results obtained from 1173 experimental tests collected in the laboratory are presented to validate this proposal. The obtained results shows that this approach can accurately classify healthy and bearing defects in inverter-fed induction motors.

Bearing fault identification of three-phase induction motors bases on two current sensor strategy

Three-phase induction motors are the most commonly used devices for electromechanical energy conversion. This study proposes an alternative approach for identifying bearing faults in induction motors, using two current sensors and a pattern classifier, based on artificial neural networks. To validate the methodology, results are given from experiments carried out on a test bench where the motors operate with different types of bearing faults, under varying conditions of load torque and voltage unbalance. This paper also provides the comparative performance of neural network and random forest classifiers. This study also presents an analysis of the current signals in the time domain, applied to different neural structures.

Teaching crowdsourcing development in undergraduate courses a comparative study

The crowdsourcing (CS) is a software development practice that increases in the productive sector. This environment is supported by a platform which relates the crowd, the provider, the sponsor and the developer. Microtasks are executed by people, and these are joining in a large project. The teaching of crowdsourcing in undergraduate courses is minimal compared to the teaching of traditional Distributed Software Development (DSD). This paper shows the comparison among the teaching of DSD and the teaching of CS. We executed two experiments for verifying the differences among its. The results show these differences in the point of view of the teachers, students and content.

Time domain diagnosis of multiple faults in three phase induction motors using inteligent approaches

The three-phase induction motor is one of the most employed equipment in industrial premisses. Despite of its reliability and robustness, these machines can present faults due to the operation time, harsh operating conditions, voltage unbalance, among other factors. In this work, a methodology for intelligent diagnose of multiple faults in induction motors by using a discretization of currents and voltages amplitudes signals in the time domain is proposed. Three types of intelligent classifiers are employed to proper diagnose motor faults: artificial neural network type multilayer perceptron (ANN/MLP), algorithm k-nearest neighbour (k-NN) and support vector machine with sequential minimal optimization (SVM/SMO). The investigated faults are related to stator short-circuit, broken rotor bars and bearing defects. Experimental results are obtained with data gathered from a 1 hp motor under varied load and unbalanced voltage conditions. The MLP and k-NN classifiers are highlighted with accuracy above 89%.