Estimation of bearing fault severity in line-connected and inverter-fed three-phase induction motors

Producción CientíficaThis paper addresses a comprehensive evaluation of a bearing fault evolution and its consequent prediction concerning the remaining useful life. The proper prediction of bearing faults in their early stage is a crucial factor for predictive maintenance and mainly for the production management schedule. The detection and estimation of the progressive evolution of a bearing fault are performed by monitoring the amplitude of the current signals at the time domain. Data gathered from line-fed and inverter-fed three-phase induction motors were used to validate the proposed approach. To assess classification accuracy and fault estimation, the models described in this paper are investigated by using Artificial Neural Networks models. The paper also provides process flowcharts and classification tables to present the prognostic models used to estimate the remaining useful life of a defective bearing. Experimental results confirmed the method robustness and provide an accurate diagnosis regardless of the bearing fault stage, motor speed, load level, and type of supply.CAPES (process BEX552269/2011-5)National Council for Scientific and Technological Development (grant #474290/2008-3, #473576/2011-2, #552269/2011-5, #307220/2016-8

Application of intelligent techniques with analysis in time domain to defect recognition in three-phase induction motors

Os motores de indução trifásicos são os principais elementos de conversão de energia elétrica em mecânica motriz aplicados em vários setores produtivos. Identificar um defeito no motor em operação pode fornecer, antes que ele falhe, maior segurança no processo de tomada de decisão sobre a manutenção da máquina, redução de custos e aumento de disponibilidade. Nesta tese são apresentas inicialmente uma revisão bibliográfica e a metodologia geral para a reprodução dos defeitos nos motores e a aplicação da técnica de discretização dos sinais de correntes e tensões no domínio do tempo. É também desenvolvido um estudo comparativo entre métodos de classificação de padrões para a identificação de defeitos nestas máquinas, tais como: Naive Bayes, k-Nearest Neighbor, Support Vector Machine (Sequential Minimal Optimization), Rede Neural Artificial (Perceptron Multicamadas), Repeated Incremental Pruning to Produce Error Reduction e C4.5 Decision Tree. Também aplicou-se o conceito de Sistemas Multiagentes (SMA) para suportar a utilização de múltiplos métodos concorrentes de forma distribuída para reconhecimento de padrões de defeitos em rolamentos defeituosos, quebras nas barras da gaiola de esquilo do rotor e curto-circuito entre as bobinas do enrolamento do estator de motores de indução trifásicos. Complementarmente, algumas estratégias para a definição da severidade dos defeitos supracitados em motores foram exploradas, fazendo inclusive uma averiguação da influência do desequilíbrio de tensão na alimentação da máquina para a determinação destas anomalias. Os dados experimentais foram adquiridos por meio de uma bancada experimental em laboratório com motores de potência de 1 e 2 cv acionados diretamente na rede elétrica, operando em várias condições de desequilíbrio das tensões e variações da carga mecânica aplicada ao eixo do motor.The three-phase induction motors are the key elements of electromechanical energy conversion in a variety of productive sectors. Identify a defect in an operating motor can provide, before it fails, greater safety for decision making on machine maintenance, reduce costs and increase process availability. This thesis initially presents a literature review and the general methodology for reproduction of defects in the motors and the application of discretization technique of current and voltage signals in the time domain. It was also developed a comparative study of methods of pattern classification for the identification of defects has been developed in these machines, such as Naive Bayes, k-Nearest Neighbor, Support Vector Machine (Sequential Minimal Optimization), Artificial Neural Network (Multilayer Perceptron), Repeated incremental Pruning to Produce Error Reduction and C4.5 Decision Tree. Also applied the concept of Multi-Agent Systems (MAS) to support the use of multiple competing methods in a distributed manner to pattern recognition of faults in bearings, broken rotor bars and stator short-circuit in induction motors. Additionally, some strategies for the definition of the severity of the aforementioned defects in engines have been explored, including making an investigation of the influence of voltage unbalance in the machine feed for the determination of these anomalies. Experimental data are acquired from 1 and 2 cv motors under sinusoidal supply, operating in various unbalance conditions and under a wide range of mechanical load applied to the motor shaft

Neural Classification of Rotor Faults in Three-Phase Induction Motors using Electric Current Signals in the Frequency Domain

Three-phase induction motors are widely used in different applications in the industry due to their robustness, low cost, and reliability. Untimely identification and correct diagnosis of incipient faults reduce cost and improve the maintenance management of these machines. This paper explores a new method for robust classification of rotor failures in three-phase induction motors (MITs) connected directly to the electrical network, operating in a steady-state, under unbalanced voltages and load conditions. Through an innovative methodology, an analysis of the electrical current signals from 1 hp and 2 hp motors in the frequency domain was performed. Such analysis was applied in constructing input matrices for a Multilayer Perceptron Neural Network (MLPNN) to detect faults. Furthermore, this methodology proved to be robust because the samples of the failing and healthy motors include voltage unbalance conditions in the electrical supply and a significant variation in the load applied to the motor shaft. Such load variation was used for the detection of failures of 1, 2, and 4 broken bars consecutively on the rotor and in the condition of 2 broken bars and 2 other broken bars diametrically opposite. The results were promising and were obtained using 847 real samples from an experimental bench used to construct the neural model and its respective validation