Rotor Bar Fault Monitoring Method Based on Analysis of Air-Gap Torques of Induction Motors

A robust method to monitor the operating conditions of induction motors is presented. This method utilizes the data analysis of the air-gap torque profile in conjunction with a Bayesian classifier to determine the operating condition of an induction motor as either healthy or faulty. This method is trained offline with datasets generated either from an induction motor modeled by a time-stepping finite-element (TSFE) method or experimental data. This method can effectively monitor the operating conditions of induction motors that are different in frame/class, ratings, or design from the motor used in the training stage. Such differences can include the level of load torque and operating frequency. This is due to a novel air-gap torque normalization method introduced here, which leads to a motor fault classification process independent of these parameters and with no need for prior information about the motor being monitored. The experimental results given in this paper validate the robustness and efficacy of this method. Additionally, this method relies exclusively on data analysis of motor terminal operating voltages and currents, without relying on complex motor modeling or internal performance parameters not readily available

Condition monitoring of induction motors in the nuclear power station environment

The induction motor is a highly utilised electrical machine in industry, with the nuclear industry being no exception. A typical nuclear power station usually contains more than 1000 motors, where they are used in safety and non-safety application. The efficient and fault-free operation of this machine is critical to the safe and economical operation of any plant, including nuclear power stations. A comprehensive literature review was conducted that covered the functioning of the induction machine, its common faults and methods of detecting these faults. The Condition Based Maintenance framework was introduced in which condition monitoring of induction machines is an essential component. The main condition monitoring methods were explained with the main focus being on Motor Current Signature Analysis (MCSA) and the various methods associated with it. Three analysis methods were selected for further study, namely, Current Signature Analysis, Instantaneous Power Signature Analysis (IPSA) and Motor Square Current Signature Analysis (MSCSA). Essentially, the methodology used in this dissertation was to study the three common motor faults (bearings, stator and rotor cage) in isolation and compare the results to that of the healthy motor of the same type. The test loads as well as fault severity were varied where possible to investigate its effect on the fault detection scheme. The data was processed using an FFT based algorithm programed in MATLAB. The results of the study of the three spectral analysis techniques showed that no single technique is able to detect motor faults under all tested circumstances. The MCSA technique proved the most capable of the three techniques as it was able to detect faults under most conditions, but generally suffered poor results in inverter driven motor applications. The IPSA and MSCSA techniques performed selectively when compared to MCSA and were relatively successful when detecting the mechanical faults. The fact that the former techniques produce results at unique points in the spectrum would suggest that they are more suitable for verifying results. As part of a comprehensive condition monitoring scheme, as required by a large population of the motors on a nuclear power station, the three techniques presented in this study could readily be incorporated into the Condition Based Maintenance framework where the strengths of each could be exploited

Induction Machine Broken Bar and Stator Short-Circuit Fault Diagnostics Based on Three Phase Stator Current Envelopes

A new method for the fault diagnosis of a broken rotor bar and interturn short circuits in induction machines (IMs) is presented. The method is based on the analysis of the three-phase stator current envelopes of IMs using reconstructed phase space transforms. The signatures of each type of fault are created from the three-phase current envelope of each fault. The resulting fault signatures for the new so-called ldquounseen signalsrdquo are classified using Gaussian mixture models and a Bayesian maximum likelihood classifier. The presented method yields a high degree of accuracy in fault identification as evidenced by the given experimental results, which validate this method

Diagnosis of broken bar fault in three-phase induction motors using fibre bragg grating strain sensors assisted by an algorithm

Mestrado de dupla diplomação com a UTFPR - Universidade Tecnológica Federal do ParanáIn this study, we developed an algorithm for identifying failures caused by broken bars in the rotors of three-phase induction motors through the analysis of their dynamic deformation using fibre optic Bragg gratings (FBGs) as sensing elements. The analysis of dynamic deformation enables the detection and diagnosis of various mechanical faults, such as misalignment, imbalance, bearing failures, and mechanical looseness. Furthermore, it allows for the identification of electrical faults, such as fractures or cracks in the rotor rings or bars To measure the dynamic deformation, we employed FBG-based sensors known for their key features, including high multiplexing capability, electromagnetic radiation immunity, and long-distance operation. Experimental tests were conducted on a small-scale induction motor (3 HP) to validate the method and explore its applicability to medium and large-scale machines. The motor was powered by two different supply sources: the utility power grid and a controled power sources, under load conditions of 75% and 100% of the rated load. During the tests, we used a rotor without any bar defects and subsequently a rotor with a broken bar. The presence of a broken bar was successfully identified under both load conditions and across all two power supply sources. The fault caused by the broken bar in the rotor was detected in two frequency regions obtained from the three sets of experiments. The first region was centred around the mechanical rotational frequency of the rotor, while the second region was approximately twice the electrical frequency of the power supply. The system demonstrated high sensitivity with a good signal-to-noise ratio and showcased advantages over conventional methods and sensors commonly used for identifying broken bar faults in induction motors.Neste estudo, desenvolvemos um algoritmo para identificar falhas em barras quebradas no rotor de motores de indução trifásicos por meio da análise da deformação dinâmica do estator usando grades de Bragg em fibras ópticas (FBGs) com assitência de um algoritmo. Essa análise possibilita a detecção e o diagnóstico de várias falhas mecânicas, como desalinhamento, desbalanceamento e folga mecânica. Além disso, permite a identificação de falhas elétricas, como fraturas ou rachaduras nos anéis ou barras do rotor. Para medir a deformação dinâmica, empregamos sensores baseados em FBG conhecidos por suas principais características, incluindo alta capacidade de multiplexação, imunidade à radiação eletromagnética e operação a longa distância. Testes experimentais foram conduzidos em um motor de indução em pequena escala (3 HP) para validar o método e explorar sua aplicabilidade em máquinas de médio e grande porte. O motor foi alimentado por duas fontes de alimentação diferentes: a rede elétrica de utilidade pública e por uma fonte controlada, sob condições de carga de 75% e 100% da carga nominal. Durante os testes, utilizamos um rotor sem defeitos no rotor e, posteriormente, um rotor com uma barra quebrada. A presença da barra quebrada foi identificada com sucesso em ambas as condições de carga e em todas as duas fontes de alimentação. A barra quebrada no rotor foi detectada em duas regiões de frequência obtidas a partir dos três conjuntos de experimentos. A primeira região estava centrada em torno da frequência rotacional mecânica do rotor, enquanto a segunda região era aproximadamente o dobro da frequência elétrica da fonte de alimentação. O sistema demonstrou alta sensibilidade com uma boa relação sinal-ruído e apresentou vantagens sobre os métodos convencionais e sensores comumente usados para identificar falhas em barras quebradas em motores de indução

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

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

New Method for Spectral Leakage Reduction in the FFT of Stator Currents: Application to the Diagnosis of Bar Breakages in Cage Motors Working at Very Low Slip

[EN] Motor current signature analysis has become a widespread fault diagnosis technique for induction machines (IMs), because it is noninvasive and requires low resources of hardware (a current sensor) and software (a fast Fourier transform). Nevertheless, its industrial application faces practical problems. One of its most challenging scenarios is the detection of broken bars in IMs working at very low slip, like large machines with a very small rated slip, or unloaded induction motors in off-line tests. In these cases, the leakage of the main supply component can hide the fault harmonics, even with a severe fault. Diverse solutions to this problem have been proposed, such as the use of smoothing windows, advanced spectral estimators, or the removal of the supply component. Nevertheless, these methods modify the spectral content of the current signal or add a high computational burden. In this work, a new approach is proposed, based on the analysis of the current with a very fine spectrum, obtained via simple zero padding, followed by the extraction of a practically leakage-free conventional, coarse spectrum. The method is experimentally validated by the diagnosis of a broken bar fault in a 3.15-MW induction motor.This work was supported in part by the Spanish "Ministerio de Ciencia, Innovacion y Universidades (MCIU)," in part by the "Agencia Estatal de Investigacion (AEI)," and in part by the "Fondo Europeo de Desarrollo Regional (FEDER)" in the framework of the "Proyectos I+D+i-Retos Investigacion 2018," Project under Grant RTI2018-102175-BI00 (MCIU/AEI/FEDER, UE). The Associate Editor coordinating the review process was Hongrui Wang. (Corresponding author: Manuel Pineda-Sanchez.)Puche-Panadero, R.; Martinez-Roman, J.; Sapena-Bano, A.; Burriel-Valencia, J.; Pineda-Sanchez, M.; Pérez-Cruz, J.; Riera-Guasp, M. (2021). New Method for Spectral Leakage Reduction in the FFT of Stator Currents: Application to the Diagnosis of Bar Breakages in Cage Motors Working at Very Low Slip. IEEE Transactions on Instrumentation and Measurement. 70:1-11. https://doi.org/10.1109/TIM.2021.30567411117

Fault Diagnosis and Detection in Industrial Motor Network Environment Using Knowledge-Level Modelling Technique

In this paper, broken rotor bar (BRB) fault is investigated by utilizing the Motor Current Signature Analysis (MCSA) method. In industrial environment, induction motor is very symmetrical, and it may have obvious electrical signal components at different fault frequencies due to their manufacturing errors, inappropriate motor installation, and other influencing factors. The misalignment experiments revealed that improper motor installation could lead to an unexpected frequency peak, which will affect the motor fault diagnosis process. Furthermore, manufacturing and operating noisy environment could also disturb the motor fault diagnosis process. This paper presents efficient supervised Artificial Neural Network (ANN) learning technique that is able to identify fault type when situation of diagnosis is uncertain. Significant features are taken out from the electric current which are based on the different frequency points and associated amplitude values with fault type. The simulation results showed that the proposed technique was able to diagnose the target fault type. The ANN architecture worked well with selecting of significant number of feature data sets. It seemed that, to the results, accuracy in fault detection with features vector has been achieved through classification performance and confusion error percentage is acceptable between healthy and faulty condition of motor

Broken Bar Detection in Synchronous Machines Based Wind Energy Conversion System

Electrical machines are subject to different types of failures. Early detection of the incipient faults and fast maintenance may prevent costly consequences. Fault diagnosis of wind turbine is especially important because they are situated at extremely high towers and therefore inaccessible. For offshore plants, bad weather can prevent any repair actions for several weeks. In some of the new wind turbines synchronous generators are used and directly connected to the grid without the need of power converters. Despite intensive research efforts directed at rotor fault diagnosis in induction machines, the research work pertinent to damper winding failure of synchronous machines is very limited. This dissertation is concerned with the in-depth study of damper winding failure and its traceable symptoms in different machine signals and parameters. First, a model of a synchronous machine with damper winding based on the winding function approach is presented. Next, simulation and experimental results are presented and discussed. A specially designed inside-out synchronous machine with a damper winding is employed for the experimental setup. Finally, a novel analytical method is developed to predict the behavior of the left sideband amplitude for different numbers and locations of the broken bars. This analysis is based on the magnetic field theory and the unbalanced multiphase circuits. It is found that due to the asymmetrical structure of damper winding, the left sideband component in the stator current spectrum of the synchronous machine during steady state asynchronous operation is not similar to that of the induction machine with broken bars. As a result, the motor current signature analysis (MCSA) for detection rotor failures in the induction machine is usable to detect broken damper bars in synchronous machines. However, a novel intelligent-systems based approach is developed that can identify the severity of the damper winding failure. This approach potentially can be used in a non-invasive condition monitoring system to monitor the deterioration of a synchronous motor damper winding as the number of broken bars increase over time. Some other informative features such as speed spectrum, transient time, torque-speed curve and rotor slip are also found for damper winding diagnosis

Stray Flux Monitoring and Multi-Sensor Fusion Condition Monitoring for Squirrel Cage Induction Machines

This research work investigates the ability of external magnetic flux-based condition monitoring to detect rotor-related faults and incipient stage bearing faults in squirrel-cage induction machines (SCIMs). This work also discusses the multisensory synergy of the external magnetic flux measurement with other measurements. To investigate the stray flux-based monitoring technique, this dissertation presents a theoretical analysis of the characteristic components in the stray flux spectrum of SCIMs as well as experimental validations. A wavelet packet decomposition (WPD) denoising method is proposed for flux-based incipient bearing fault detection. Additionally, a sensor fusion method to efficiently utilize the information from heterogeneous sensor measurements (external magnetic flux and stator current) to achieve higher rotor-related fault detection sensitivity and a higher fault type recognition rate is presented. Instead of using all the characteristic components directly, the proposed fusion method groups the features of several rotor abnormalities and then draws a conclusion on machine health status based on the abnormalities that are present in the machine. Finally, a novel sensor fusion-based rotor vibration observer method is proposed for incipient bearing fault detection. The observer can reject the electrical disturbances from the supply side. Meanwhile, the proposed observer is less affected by the mechanical noise from lousy environment than using vibration-based monitoring.Ph.D