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

Development of an induction motor condition monitoring test rig And fault detection strategies

Includes bibliographical references.This thesis sets out to develop an induction motor condition monitoring test rig to experimentally simulate the common faults associated with induction motors and to develop strategies for detecting these faults that employ signal processing techniques. Literature on basic concepts of induction motors and inverter drives, the phenomena of common faults associated with induction motors, the condition monitoring systems were intensively reviewed

Transformada wavelet para análisis del motor de inducción: revisión

This study makes a revision of the most recent investigations that have implemented the wavelet transform by analyzing the electrical and mechanical variables of the induction motors. The investigations can be grouped into three main topics: diagnosis and detection of faults, control and detection systems and the classification of electromagnetic disturbances.Este trabajo realiza una revisión de las investigaciones más recientes que han implementado la transformada wavelet analizando las variables eléctricas y mecánicas de los motores de inducción. Las investigaciones se pueden agrupar en tres temas principales: diagnóstico y detección de fallas; sistemas de control y detección y la clasificación de perturbaciones electromagnéticas

Support vector machine based classification in condition monitoring of induction motors

Continuous and trouble-free operation of induction motors is an essential part of modern power and production plants. Faults and failures of electrical machinery may cause remarkable economical losses but also highly dangerous situations. In addition to analytical and knowledge-based models, application of data-based models has established a firm position in the induction motor fault diagnostics during the last decade. For example, pattern recognition with Neural Networks (NN) is widely studied. Support Vector Machine (SVM) is a novel machine learning method introduced in early 90's. It is based on the statistical learning theory presented by V.N. Vapnik, and it has been successfully applied to numerous classification and pattern recognition problems such as text categorization, image recognition and bioinformatics. SVM based classifier is built to minimize the structural misclassification risk, whereas conventional classification techniques often apply minimization of the empirical risk. Therefore, SVM is claimed to lead enhanced generalisation properties. Further, application of SVM results in the global solution for a classification problem. Thirdly, SVM based classification is attractive, because its efficiency does not directly depend on the dimension of classified entities. This property is very useful in fault diagnostics, because the number of fault classification features does not have to be drastically limited. However, SVM has not yet been widely studied in the area of fault diagnostics. Specifically, in the condition monitoring of induction motor, it does not seem to have been considered before this research. In this thesis, a SVM based classification scheme is designed for different tasks in induction motor fault diagnostics and for partial discharge analysis of insulation condition monitoring. Several variables are compared as fault indicators, and forces on rotor are found to be important in fault detection instead of motor current that is currently widely studied. The measurement of forces is difficult, but easily measurable vibrations are directly related to the forces. Hence, vibration monitoring is considered in more detail as the medium for the motor fault diagnostics. SVM classifiers are essentially 2-class classifiers. In addition to the induction motor fault diagnostics, the results of this thesis cover various methods for coupling SVMs for carrying out a multi-class classification problem.reviewe

Bibliography on Induction Motors Faults Detection and Diagnosis

International audienceThis paper provides a comprehensive list of books, workshops, conferences, and journal papers related to induction motors faults detection and diagnosis

Induction Motors

AC motors play a major role in modern industrial applications. Squirrel-cage induction motors (SCIMs) are probably the most frequently used when compared to other AC motors because of their low cost, ruggedness, and low maintenance. The material presented in this book is organized into four sections, covering the applications and structural properties of induction motors (IMs), fault detection and diagnostics, control strategies, and the more recently developed topology based on the multiphase (more than three phases) induction motors. This material should be of specific interest to engineers and researchers who are engaged in the modeling, design, and implementation of control algorithms applied to induction motors and, more generally, to readers broadly interested in nonlinear control, health condition monitoring, and fault diagnosis

Utilizing the Value of Smart Sensor Data in Motor Manufacturing

The topic of this master’s thesis was the utilizing of the value of smart sensor data in motor manufacturing at an electric motor factory in Vaasa. The target company has developed and manufactured smart sensors to customers for years and is now implementing them in its own production. In the thesis’s the possibilities of utilization of the data of smart sensor utilization in the Vaasa factory has been studied through a specified case study. The motors included in the pilot were selected by their usage in the process. The target was to identify improvement potentials for smart sensors in discrete industries and to create a plan for sensor data usage and further development. With the increased value of the efficient data usage, the target is to reduce unplanned stoppages, improve safety, and minimize the penalties and other costs paid because of delayed motors. An additional target was to improve the service and spare part activities with better utilization of data from the sensors as well as define the suitability of current motors in use to their current intended use. The concrete target of this thesis was to create reporting tools to the target company. First, a literature study was carried out for the basics in sensors, smart sensors, and electric motors. Special focus was on the target company’s smart motor sensors and their competitors on the market. With the information available the products were compared to each other. Conclusion were made on the basis of the pilot installation on the target factory’s premises on the value of the smart sensors on discrete production. Through the case study the additional value created to the target company of the smart sensor data was analyzed. A roadmap was created for the smart sensor data for further analyses, storing data and user experience. The outcome of this thesis was several Power BI templates, a fleet report and several recommendations for further development. Through a comparison it was noted that the ABB Ability Smart Sensor TM as a product is slightly more advanced than the smart sensors of the competitors. It was observed that only measuring the data does not bring value to the company, but the data has to be transformed into understandable and visual information and only after that it can bring real value to the company. In the case study it was also noted that actions based on the data created by the sensors can be performed in order to reduce unplanned outages in the motor manufacturing with the predictive data for timing of services and repairs. It was also observed that the data and information provided by the smart sensors can also be utilized in discrete manufacturing on motors that are not continuously used. This is additionally to the knowledge compared to the motors used in process industries where the value of the sensor data was already confirmed in earlier studies.Diplomityön aiheena on kohdeyrityksen älykkään anturin tuottama lisäarvo yhtiön Vaasan moottoritehtaan moottorivalmistuksessa. Kohdeyritys on valmistanut älykkäitä antureita asiakkaille myytäväksi jo useita vuosia ja on nyt käyttöönottamassa tuotteen omassa prosessissaan. Työssä tutkitaan älykkään anturin tuottaman datankäytön mahdollisuuksia kohdeyrityksen Vaasan yksikössä, määritellyn pilotin avulla. Pilottiin valittiin moottorit käyttötarkoituksen mukaan. Tavoitteena on tunnistaa kehittämiskohteita älykkäälle sensorille kappaletavaratuotannossa sekä luoda suunnitelma sensoridatan käytölle ja sen kehittämiseen. Datan tehokkaan käytön tavoitteina on pienentää suunnittelemattomia käyttökatkoksia, parantaa työturvallisuutta ja minimoida moottorien viivästyneiden toimitusten aiheuttamat lisääntyneet rahti ja muut ylimääräiset kustannukset. Lisäksi datankäytön tuomalla lisäarvolla pyritään kehittämään huolto- ja varaosatoimintaa, sekä määrittelemään käytössä olevien moottorien soveltuvuus nykyiseen käyttötarkoitukseensa. Konkreettisena tavoitteena on luoda toimeksiantoyritykselle raportointityökaluja. Työ alussa on kirjallisuuskatsaus anturien, älykkäiden anturien ja sähkömoottoreiden perusteisiin. Erityisesti tarkasteltiin kohdeyrityksen älykkäitä antureita ja sen kilpailijoita. Näitä vertailtiin toisiinsa saatavilla olevan tiedon avulla. Älykkään anturin pilottiasennuksen perusteella tehtiin johtopäätöksiä antureiden soveltuvuudesta kappaletavaratuotantoon esimerkkiyrityksen kautta. Pilotin perusteella pyrittiin analysoimaan tuotteen käyttöönoton kannattavuutta kohdeyritykselle. Lopuksi luotiin tiekartta antureista saatavan datan analysoinnille, tallettamiselle ja esittämiselle. Työn tuloksena syntyi Power BI raportointipohjia ja fleet report sekä kehitysehdotuksia jatkotoimenpiteille. Lisäksi työssä havaittiin, että ABB:n Ability Smart Sensor TM on tuotteena kilpailijoitaan hieman edellä tuotteen ominaisuuksien ja käytettävyyden osalta. Työssä havaittiin, että pelkkä datan mittaaminen sensoreilla ei itsessään tuo lisäarvoa yritykselle, vaan data pitää muuttaa ymmärrettäväksi informaatioksi ja vasta sen jälkeen se tuo lisäarvoa kohdeyritykselle. Edellisten lisäksi pilotissa havaittiin, että anturien tuottaman datan perusteella voidaan tehdä toimenpiteitä, jotta tuotannon suunnittelemattomia katkoksia pystyttäisiin vähentämään käyttämällä Smart Sensorin tuottamia tietoja ennakoivien huoltojen ja korjausten ajastamisessa. Ilmi tuli myös, että Smart Sensoreiden tuottamaa dataa voi käyttää myös kappaletavarateollisuudessa käytössä olevissa moottoreissa, jotka eivät ole jatkuvassa käytössä. Näin on siis prosessiteollisuudessa jatkuvassa käytössä olevien moottorien lisäksi, jossa sensorien tuotta-man datan hyöty on vahvistettu jo aikaisemmin

Machine learning-based fault detection and diagnosis in electric motors

Fault diagnosis is critical to any maintenance industry, as early fault detection can prevent catastrophic failures as well as a waste of time and money. In view of these objectives, vibration analysis in the frequency domain is a mature technique. Although well established, traditional methods involve a high cost of time and people to identify failures, causing machine learning methods to grow in recent years. The Machine learning (ML) methods can be divided into two large learning groups: supervised and unsupervised, with the main difference between them being whether the dataset is labeled or not. This study presents a total of four different methods for fault detection and diagnosis. The frequency analysis of the vibration signal was the first approach employed. This analysis was chosen to validate the future results of the ML methods. The Gaussian Mixture model (GMM) was employed for the unsupervised technique. A GMM is a probabilistic model in which all data points are assumed to be generated by a finite number of Gaussian distributions with unknown parameters. For supervised learning, the Convolution neural network (CNN) was used. CNNs are feedforward networks that were inspired by biological pattern recognition processes. All methods were tested through a series of experiments with real electric motors. Results showed that all methods can detect and classify the motors in several induced operation conditions: healthy, unbalanced, mechanical looseness, misalignment, bent shaft, broken bar, and bearing fault condition. Although all approaches are able to identify the fault, each technique has benefits and limitations that make them better for certain types of applications, therefore, a comparison is also made between the methods.O diagnóstico de falhas é fundamental para qualquer indústria de manutenção, a detecção precoce de falhas pode evitar falhas catastróficas, bem como perda de tempo e dinheiro. Tendo em vista esses objetivos, a análise de vibração através do domínio da frequência é uma técnica madura. Embora bem estabelecidos, os métodos tradicionais envolvem um alto custo de tempo e pessoas para identificar falhas, fazendo com que os métodos de aprendizado de máquina cresçam nos últimos anos. Os métodos de Machine learning (ML) podem ser divididos em dois grandes grupos de aprendizagem: supervisionado e não supervisionado, sendo a principal diferença entre eles é o conjunto de dados que está rotulado ou não. Este estudo apresenta um total de quatro métodos diferentes para detecção e diagnóstico de falhas. A análise da frequência do sinal de vibração foi a primeira abordagem empregada. foi escolhida para validar os resultados futuros dos métodos de ML. O Gaussian Mixture Model (GMM) foi empregado para a técnica não supervisionada. O GMM é um modelo probabilístico em que todos os pontos de dados são considerados gerados por um número finito de distribuições gaussianas com parâmetros desconhecidos. Para a aprendizagem supervisionada, foi utilizada a Convolutional Neural Network (CNN). CNNs são redes feedforward que foram inspiradas por processos de reconhecimento de padrões biológicos. Todos os métodos foram testados por meio de uma série de experimentos com motores elétricos reais. Os resultados mostraram que todos os métodos podem detectar e classificar os motores em várias condições de operação induzida: íntegra, desequilibrado, folga mecânica, desalinhamento, eixo empenado, barra quebrada e condição de falha do rolamento. Embora todas as abordagens sejam capazes de identificar a falha, cada técnica tem benefícios e limitações que as tornam melhores para certos tipos de aplicações, por isso, também e feita uma comparação entre os métodos