10 research outputs found

    Transformada wavelet para análisis del motor de inducción: revisión

    Get PDF
    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

    Magnetoresistance sensor-based rotor fault detection in induction motor using non-decimated wavelet and streaming data

    Get PDF
    In this paper, the giant magnetoresistance broken rotor (GBR) method is used to diagnose the induction motor (IM) rotor bar fault at an early stage from outward magnetic flux developed by IM.The outward magnetic field signal has anti-clockwise radiation due to broken rotor bar current.In this paper, the outward magnetic signal is acquired using a giant magnetoresistance (GMR) sensor. In the GBR method, IM rotor fault is analysed with a non-decimated wavelet transform (NDWT)-based outward magnetic signal. Experimental result shows the difference in statistical features and energy levels of sub-bands of NDWT for healthy and faulty IM. Least square-support vector machine(LS-SVM)-based classification results are verified by confusion matrix based on 150 outward magnetic signals from a healthy and damaged rotor (broken rotor). The proposed method identifies IM rotor faults with 95% sensitivity, 90% specificity and 92.5% classification accuracy. Furthermore, run-time IM condition monitoring is performed through the ThinkSpeak internet of things (IoT) platform for collecting outer magnetic signal data. ThinkSpeak streaming data of outward magnetic field help detect rotor fault at the initial stage and understand the growth of rotor fault in the motor. The proposed GBR method overcomes sensitivity, translation-invariance limitations of existing IM rotor fault diagnosis methods

    Characteristics Analysis and Measurement of Inverter-Fed Induction Motors for Stator and Rotor Fault Detection

    Get PDF
    Inverter-fed induction motors (IMs) contain a serious of current harmonics, which become severer under stator and rotor faults. The resultant fault components in the currents affect the monitoring of the motor status. With this background, the fault components in the electromagnetic torque under stator faults considering harmonics are derived in this paper, and the fault components in current harmonics under rotor faults are analyzed. More importantly, the monitoring based on the fault characteristics (both in the torque and current) is proposed to provide reliable stator and rotor fault diagnosis. Specifically, the fault components induced by stator faults in the electromagnetic torque are discussed in this paper, and then, fault components are characterized in the torque spectrum to identify stator faults. To achieve so, a full-order flux observer is adopted to calculate the torque. On the other hand, under rotor faults, the sidebands caused by time and space harmonics in the current are analyzed and exploited to recognize rotor faults, being the motor current signature analysis (MCSA). Experimental tests are performed on an inverter-fed 2.2 kW/380 V/50 Hz IM, which verifies the analysis and the effectiveness of the proposed fault diagnosis methods of inverter-fed IMs

    Fault Diagnosis of Induction Machines in a Transient Regime Using Current Sensors with an Optimized Slepian Window

    Full text link
    [EN] The aim of this paper is to introduce a new methodology for the fault diagnosis of induction machines working in the transient regime, when time-frequency analysis tools are used. The proposed method relies on the use of the optimized Slepian window for performing the short time Fourier transform (STFT) of the stator current signal. It is shown that for a given sequence length of finite duration, the Slepian window has the maximum concentration of energy, greater than can be reached with a gated Gaussian window, which is usually used as the analysis window. In this paper, the use and optimization of the Slepian window for fault diagnosis of induction machines is theoretically introduced and experimentally validated through the test of a 3.15-MW induction motor with broken bars during the start-up transient. The theoretical analysis and the experimental results show that the use of the Slepian window can highlight the fault components in the current¿s spectrogram with a significant reduction of the required computational resourcesThis work was supported by the Spanish "Ministerio de Economia y Competitividad" in the framework of the "Programa Estatal de Investigacion, Desarrollo e Innovacion Orientada a los Retos de la Sociedad" (Project Reference DPI2014-60881-R).Burriel-Valencia, J.; Puche-Panadero, R.; Martinez-Roman, J.; Sapena-Bano, A.; Pineda-Sanchez, M. (2018). Fault Diagnosis of Induction Machines in a Transient Regime Using Current Sensors with an Optimized Slepian Window. Sensors. 18(1):1-24. https://doi.org/10.3390/s18010146S12418

    Artificial Intelligence Supported EV Electric Powertrain for Safety Improvement

    Get PDF
    As an environmentally friendly transport option, electric vehicles (EVs) are endowed with the characteristics of low fossil energy consumption and low pollutant emissions. In today's growing market share of EVs, the safety and reliability of the powertrain system will be directly related to the safety of human life. Reliability problems of EV powertrains may occur in any power electronic (PE) component and mechanical part, both sudden and cumulative. These faults in different locations and degrees will continuously threaten the life of drivers and pedestrians, bringing irreparable consequences. Therefore, monitoring and predicting the real-time health status of EV powertrain is a high-priority, arduous and challenging task. The purposes of this study are to develop AI-supported effective safety improvement techniques for EV powertrains. In the first place, a literature review is carried out to illustrate the up-to-date AI applications for solving condition monitoring and fault detection issues of EV powertrains, where recent case studies between conventional methods and AI-based methods in EV applications are compared and analysed. On this ground this study, then, focuses on the theories and techniques concerning this topic so as to tackle different challenges encountered in the actual applications. In detail, first, as for diagnosing the bearing system in the earlier fault period, a novel inferable deep distilled attention network is designed to detect multiple bearing faults. Second, a deep learning and simulation driven approach that combines the domain-adversarial neural network and the lumped-parameter thermal network (LPTN) is proposed for achieve IPMSM permanent magnet temperature estimation work. Finally, to ensure the use safety of the IGBT module, deep learning -based IGBT modules’ double pulse test (DPT) efficiency enhancement is proposed and achieved via multimodal fusion networks and graph convolution networks

    Semi-Supervised Learning for Diagnosing Faults in Electromechanical Systems

    Get PDF
    Safe and reliable operation of the systems relies on the use of online condition monitoring and diagnostic systems that aim to take immediate actions upon the occurrence of a fault. Machine learning techniques are widely used for designing data-driven diagnostic models. The training procedure of a data-driven model usually requires a large amount of labeled data, which may not be always practical. This problem can be untangled by resorting to semi-supervised learning approaches, which enables the decision making procedure using only a few numbers of labeled samples coupled with a large number of unlabeled samples. Thus, it is crucial to conduct a critical study on the use of semi-supervised learning for the purpose of fault diagnosis. Another issue of concern is fault diagnosis in non-stationary environments, where data streams evolve over time, and as a result, model-based and most of the data-driven models are impractical. In this work, this has been addressed by means of an adaptive data-driven diagnostic model

    Sviluppo di una Metodologia per la Selezione e il Controllo Qualità di Ventilatori per Cappe Aspiranti in Linea di Produzione Mediante Analisi Vibrazionale

    Get PDF
    Fin dai primi anni del secolo scorso i ricercatori hanno condotto ricerche e sviluppato soluzioni per diagnosticare l’insorgere di difettosità nei motori ad induzione per aumentarne l’affidabilità e la qualità. La letteratura è ricca di esempi nei quali vengono utilizzate le più conosciute tecniche di elaborazione del segnale e negli ultimi anni l’utilizzo di algoritmi di intelligenza artificiale ha portato ad ulteriori miglioramenti nella prevenzione dei guasti e delle loro conseguenze. In questo lavoro viene presentato un approccio differente per diagnosticare la presenza di difettosità nei motori ad induzione, una metodologia originale per il tipo di applicazione basata sul calcolo delle divergenze statistiche tra distribuzioni di probabilità e sul calcolo delle entropie e della cross-entropia. Vengono proposti e confrontati cinque diversi metodi per ottenere le distribuzioni di probabilità dai segnali misurati, due differenti formulazioni per il calcolo delle divergenze e quattro per il calcolo dell’entropia. L’efficacia e la maggiore robustezza degli indicatori calcolati con il metodo proposto rispetto ai tradizionali indicatori statistici sono dimostrate tramite le analisi condotte sulle misure accelerometriche acquisite durante lo sviluppo della procedura per il controllo qualità dei ventilatori per cappe aspiranti uscenti dalla linea di produzione di SIT S.p.A. Ne viene presentata inoltre una versione modificata utilizzando la trasmissibilità del banco di collaudo come filtro inverso, soluzione che la rende efficace anche quando applicata alle misure acquisite dal sensore accelerometrico di linea. La procedura proposta ha dimostrato capacità di classificazione con un accuratezza superiore al 95%. Infine, sfruttando le potenzialità del machine learning, viene proposta una soluzione che, utilizzando un Autoencoder, è in grado di migliorare i risultati ottenuti in precedenza, raggiungendo valori analoghi come accuratezza ma migliori in termini di falsi negativi.Since the early years of the last century, researchers have conducted research and developed solutions to diagnose the onset of defects in induction motors to increase their reliability and quality. The literature is full of examples in which the well-known signal processing techniques are used and in recent years the use of artificial intelligence algorithms has led to further improvements in the prevention of faults and their consequences. In this work a different approach is presented to diagnose the presence of defects in induction motors, an original methodology for the type of application based on the calculation of statistical divergences between probability distributions and on the calculation of entropies and cross-entropy. Five different methods for obtaining probability distributions from measured signals are proposed and compared, two different formulations for calculating divergences and four for calculating entropy. The effectiveness and greater robustness of the indicators calculated with the proposed method compared to traditional statistical indicators are demonstrated through the analyses conducted on the accelerometric measurements acquired during the development of the procedure for the quality control of the fans for extractor hoods leaving the production line of SIT S.p.A. A modified version is also presented using the transmissibility of the production bench as an inverse filter, a solution that makes it effective even when applied to the measurements acquired by the accelerometric sensor positioned on the production station. The proposed procedure has demonstrated classification capabilities with an accuracy greater than 95%. Finally, exploiting the potential of machine learning, a solution is proposed which, using an Autoencoder, is able to improve the results previously obtained, reaching similar values in terms of accuracy but better in terms of false negatives

    Advanced Power Loss Modeling and Model-Based Control of Three-Phase Induction Motor Drive Systems

    Get PDF
    Three-phase induction motor (IM) drive systems are the most important workhorses of many industries worldwide. This dissertation addresses improved modeling of three-phase IM drives and model-based control algorithms for the purpose of designing better IM drive systems. Enhancements of efficiency, availability, as well as performance of IMs, such as maximum torque-per-ampere capability, power density, and torque rating, are of major interest. An advanced power loss model of three-phase IM drives is proposed and comprehensively validated at different speed, load torque, flux and input voltage conditions. This model includes a core-loss model of three-phase IMs, a model of machine mechanical and stray losses, and a model of power electronic losses in inverters. The drive loss model shows more than 90% accuracy and is used to design system-level loss minimization control of a motor drive system, which is integrated with the conventional volts-per-hertz control and indirect field-oriented control as case studies. The designed loss minimization control leads to more than 13% loss reduction than using rated flux for the testing motor drive under certain conditions. The proposed core-loss model is also used to design an improved model-based maximum torque-per-ampere control of IMs by considering core losses. Significant increase of torque-per-ampere capability could be possible for high-speed IMs. A simple model-based time-domain fault diagnosis method of four major IM faults is provided; it is nonintrusive, fast, and has excellent fault sensitivity and robustness to noise and harmonics. A fault-tolerant control scheme for sensor failures in closed-loop IM drives is also studied, where a multi-controller drive is proposed and uses different controllers with minimum hand-off transients when switching between controllers. A finite element analysis model of medium-voltage IMs is explored, where electromagnetic and thermal analyses are co-simulated. The torque rating and power density of the simulated machine could be increased by 14% with proper change of stator winding insulation material. The outcome of this dissertation is an advanced three-phase IM drive that is enhanced using model-based loss minimization control, fault detection and diagnosis of machine faults, fault-tolerant control under sensor failures, and performance-enhancement suggestions

    Dynamic model of three-phase squirrel-cage induction machine based onfinite elements method

    No full text
    Тема докторске дисертације је развој и верификација новог динамичког модела трофазне кавезне асинхроне машине, који је заснован на методи коначних елемената. Применом линеарних магнетостатичких симулација и time-harmonic нелинеарних симулација се реконструишу индуктивности динамичког модела машине заснованог на вишеструко спрегнутим електричним колима. Модел се затим користи у истраживању феномена везаних за појаву жљебних хармоника, сатурацијом индукованих хармоника, као и у развоју нове методе детекције сломљних шипки у роторском кавезу.Tema doktorske disertacije je razvoj i verifikacija novog dinamičkog modela trofazne kavezne asinhrone mašine, koji je zasnovan na metodi konačnih elemenata. Primenom linearnih magnetostatičkih simulacija i time-harmonic nelinearnih simulacija se rekonstruišu induktivnosti dinamičkog modela mašine zasnovanog na višestruko spregnutim električnim kolima. Model se zatim koristi u istraživanju fenomena vezanih za pojavu žljebnih harmonika, saturacijom indukovanih harmonika, kao i u razvoju nove metode detekcije slomljnih šipki u rotorskom kavezu.Topic of this thesis is development and verification of the novel dynamical model of the three phase squirrel-cage induction machine, which is based on the finite elements method. Linear magnetostatic simulations and nonlinear time-harmonic simulations are used for calculation of the inductances of the dynamic model of the machine based on the moultiple coupled circuit approach. Model is then used for investigation of the rotor slot harmonics, saturation induced harmonics and in the development of the novel method for broken rotor bars detection
    corecore