A Review of Modeling and Diagnostic Techniques for Eccentricity Fault in Electric Machines

Research on the modeling and fault diagnosis of rotor eccentricities has been conducted during the past two decades. A variety of diagnostic theories and methods have been proposed based on different mechanisms, and there are reviews following either one type of electric machines or one type of eccentricity. Nonetheless, the research routes of modeling and diagnosis are common, regardless of machine or eccentricity types. This article tends to review all the possible modeling and diagnostic approaches for all common types of electric machines with eccentricities and provide suggestions on future research roadmap. The paper indicates that a reliable low-cost non-intrusive real-time online visualized diagnostic method is the trend. Observer-based diagnostic strategies are thought promising for the continued research

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

A Fast Diagnosis Method for Both IGBT Faults and Current Sensor Faults in Grid-Tied Three-Phase Inverters With Two Current Sensors

© 1986-2012 IEEE. This article considers fault detection in the case of a three-phase three-wire (3P3W) inverter, when only two current sensors are used to save cost or due to a faulty current sensor. With two current sensors, there is no current method addressing the diagnosis of both IGBT open-circuit (OC) faults and current sensor faults. In order to solve this problem, this article proposes a method which innovatively combines two kinds of diagnosis variables, line voltage deviations and phase voltage deviations. The unique faulty characteristics of diagnosis variables for each fault are extracted and utilized to distinguish the fault. Using an average model, the method only needs the signals already available in the controller. Both IGBT OC faults and current sensor faults can be detected quickly in inverter mode and rectifier mode, so that the converter can be protected in a timely way to avoid further damages. In addition, error-adaptive thresholds are adopted to make the method robust. Effects such as system unbalance are analyzed to ensure that the method is robust and feasible. Simulation and experimental results are used to verify and validate the effectiveness of the method

Diagnosis of broken bars in wind turbine squirrel cage induction generator: Approach based on current signal and generative adversarial networks

Producción CientíficaTo ensure the profitability of the wind industry, one of the most important objectives is to minimize maintenance costs. For this reason, the components of wind turbines are continuously monitored to detect any type of failure by analyzing the signals measured by the sensors included in the condition monitoring system. Most of the proposals for the detection and diagnosis of faults based on signal processing and artificial intelligence models use a fault-free signal and a signal acquired on a system in which a fault has been provoked; however, when the failures are incipient, the frequency components associated with the failures are very close to the fundamental component and there are incomplete data, the detection and diagnosis of failures is difficult. Therefore, the purpose of this research is to detect and diagnose failures of the electric generator of wind turbines in operation, using the current signal and applying generative adversarial networks to obtain synthetic data that allow for counteracting the problem of an unbalanced dataset. The proposal is useful for the detection of broken bars in squirrel cage induction generators, which, according to the control system, were in a healthy state

An investigation into current and vibration signatures of three phase induction motors

This research aimed at investigating the relationship between three phase induction motors vibration (MVS) and current signatures (MCS). This is essential due to the cost of vibration measuring equipment and in cases where vibration of interest point is not accessible; such as electrical submersible pumps (ESP) used in oil industry. A mathematical model was developed to understand the effects of two types of induction motors common faults; rotor bar imperfections and phase imbalance on the motor vibration and current signatures. An automated test facility was developed in which 1.1 kW three phase motor could be tested under varying shaft rotation speeds and loads for validating the developed model. Time and frequency domains statistical parameters of the measured signals were calculated for fault detection and assessing its severity. The measured signals were also processed using the short time Fourier transform (STFT), the Wigner-Ville distribution (WVD), the continuous wavelet transform (CWT) and discrete wavelet transform (DWT) and wavelet multi-resolution analysis (MRA). The non-stationary components, representing faults within induction motor measured vibration and current signals, were successfully detected using wavelet decomposition technique. An effective alternative to direct vibration measurement scheme, based on radial basis function networks, was developed to the reconstruction of motor vibration using measurements of one phase of the motor current. It was found that this method captured the features of induction motor faults with reasonable degrees of accuracy. Another method was also developed for the early detection and diagnosis of faults using an enhanced power factor method. Experimental results confirmed that the power factor can be used successfully for induction motor fault diagnosis and is also promising in assessing fault severity. The suggested two methods offer inexpensive, reliable and non-intrusive condition monitoring tools that suits real-time applications. Directions for further work were also outlined

An Assessment on the Non-Invasive Methods for Condition Monitoring of Induction Motors

The ability to forecast motor mechanical faults at incipient stages is vital to reducing maintenance costs, operation downtime and safety hazards. This paper synthesized the progress in the research and development in condition monitoring and fault diagnosis of induction motors. The motor condition monitoring techniques are mainly classified into two categories that are invasive and non-invasive techniques. The invasive techniques are very basic, but they have some implementation difficulties and high cost. The non-invasive methods, namely MCSA, PVA and IPA, overcome the disadvantages associated to invasive methods. This book chapter reviews the various non-invasive condition monitoring methods for diagnosis of mechanical faults in induction motor and concludes that the instantaneous power analysis (IPA) and Park vector analysis (PVA) methods are best suitable for the diagnosis of small fault signatures associated to mechanical faults. Recommendations for the future research in these areas are also presented

Advances in Electrical Machine, Power Electronic, and Drive Condition Monitoring and Fault Detection: State of the Art

© 2015 IEEE. Personal use of this material is permitted. Permissíon from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertisíng or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.[EN] Recently, research concerning electrical machines and drives condition monitoring and fault diagnosis has experienced extraordinarily dynamic activity. The increasing importance of these energy conversion devices and their widespread use in uncountable applications have motivated significant research efforts. This paper presents an analysis of the state of the art in this field. The analyzed contributions were published in most relevant journals and magazines or presented in either specific conferences in the area or more broadly scoped events.Riera-Guasp, M.; Antonino-Daviu, J.; Capolino, G. (2015). Advances in Electrical Machine, Power Electronic, and Drive Condition Monitoring and Fault Detection: State of the Art. IEEE Transactions on Industrial Electronics. 62(3):1746-1759. doi:10.1109/TIE.2014.2375853S1746175962

An Intelligent Automated Method to Diagnose and Segregate Induction Motor Faults

In the last few decades, various methods and alternative techniques have been proposed and implemented to diagnose induction motor faults. In an induction motor, bearing faults account the largest percentage of motor failure. Moreover, the existing techniques related to current and instantaneous power analysis are incompatible to diagnose the distributed bearing faults (race roughness), due to the fact that there does not exist any fault characteristics frequency model for these type of faults. In such a condition to diagnose and segregate the severity of fault is a challenging task. Thus, to overcome existing problem an alternative solution based on artificial neural network (ANN) is proposed. The proposed technique is harmonious because it does not oblige any mathematical models and the distributed faults are diagnosed and classified at incipient stage based on the extracted features from Park vector analysis (PVA). Moreover, the experimental results obtained through features of PVA and statistical evaluation of automated method shows the capability of proposed method that it is not only capable enough to diagnose fault but also can segregate bearing distributed defects

Machine Learning based Early Fault Diagnosis of Induction Motor for Electric Vehicle Application

Electrified vehicular industry is growing at a rapid pace with a global increase in production of electric vehicles (EVs) along with several new automotive cars companies coming to compete with the big car industries. The technology of EV has evolved rapidly in the last decade. But still the looming fear of low driving range, inability to charge rapidly like filling up gasoline for a conventional gas car, and lack of enough EV charging stations are just a few of the concerns. With the onset of self-driving cars, and its popularity in integrating them into electric vehicles leads to increase in safety both for the passengers inside the vehicle as well as the people outside. Since electric vehicles have not been widely used over an extended period of time to evaluate the failure rate of the powertrain of the EV, a general but definite understanding of motor failures can be developed from the usage of motors in industrial application. Since traction motors are more power dense as compared to industrial motors, the possibilities of a small failure aggravating to catastrophic issue is high. Understanding the challenges faced in EV due to stator fault in motor, with major focus on induction motor stator winding fault, this dissertation presents the following: 1. Different Motor Failures, Causes and Diagnostic Methods Used, With More Importance to Artificial Intelligence Based Motor Fault Diagnosis. 2. Understanding of Incipient Stator Winding Fault of IM and Feature Selection for Fault Diagnosis 3. Model Based Temperature Feature Prediction under Incipient Fault Condition 4. Design of Harmonics Analysis Block for Flux Feature Prediction 5. Flux Feature based On-line Harmonic Compensation for Fault-tolerant Control 6. Intelligent Flux Feature Predictive Control for Fault-Tolerant Control 7. Introduction to Machine Learning and its Application for Flux Reference Prediction 8. Dual Memorization and Generalization Machine Learning based Stator Fault Diagnosi

Fault Diagnostic System for Cascaded H-bridge Multilevel Inverter Drives Based on Artificial Intelligent Approaches Incorporating a Reconfiguration Technique

A fault diagnostic and reconfiguration system in a multilevel inverter drive (MLID) using artificial intelligent based techniques is developed in this dissertation. Output phase voltages of a MLID can be used as valuable information to diagnose faults and their locations. It is difficult to diagnose a MLID system using a mathematical model because MLID systems consist of many switching devices and their system complexity has a nonlinear factor. Therefore, a neural network (NN) classification is applied to the fault diagnosis of a MLID system. Multilayer perceptron (MLP) networks are used to identify the type and location of occurring faults. The principal component analysis (PCA) is utilized in the feature extraction process to reduce the NN input size. A lower dimensional input space will also usually reduce the time necessary to train a NN, and the reduced noise may improve the mapping performance. The genetic algorithm is also applied to select the valuable principal components. The comparison among MLP neural network (NN), principal component neural network (PC-NN), and genetic algorithm based selective principal component neural network (PC-GA-NN) are performed. Proposed neural networks are evaluated with simulation test set and experimental test set. The PC-NN has improved overall classification performance from NN by about 5% points, whereas PC-GA-NN has better overall classification performance from NN by about 7.5% points. Therefore, the application of a genetic algorithm improves the classification from PC-NN by about 2.5% point. The overall classification performance of the proposed networks is more than 90%. A reconfiguration technique is also developed. The effects of using the developed reconfiguration technique at high modulation index are addressed. The developed fault diagnostic system is validated with experimental results. The developed fault diagnostic system requires about 6 cycles at 60 Hz to clear an open circuit and about 9 cycles at 60 Hz to clear a short circuit fault. The experimental results show that the developed system performs satisfactorily to detect the fault type, fault location, and reconfiguration