Introducing the Filtered Park’s and Filtered Extended Park’s Vector Approach to Detect Broken Rotor Bars in Induction Motors Independently from the Rotor Slots Number

[EN] The Park's Vector Approach (PVA), together with its variations, has been one of the most widespread diagnostic methods for electrical machines and drives. Regarding the broken rotor bars fault diagnosis in induction motors, the common practice is to rely on the width increase of the Park's Vector (PV) ring and then apply some more sophisticated signal processing methods. It is shown in this paper that this method can be unreliable and is strongly dependent on the magnetic poles and rotor slot numbers. To overcome this constraint, the novel Filtered Park's/Extended Park's Vector Approach (FPVA/FEPVA) is introduced. The investigation is carried out with FEM simulations and experimental testing. The results prove to satisfyingly coincide, whereas the proposed advanced FPVA method is desirably reliable. (C) 2017 Elsevier Ltd. All rights reserved.The authors acknowledge the support of the Portuguese Foundation for Science and Technology under Project No. SFRH/BSAB/118741/2016, and also the support of the Spanish 'Ministerio de Economia y Competitividad' (MINECO) and FEDER program in the framework of the 'Proyectos I+D del Subprograma de Generacion de Conocimiento, Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia' (ref: DPI2014-52842-P).Gyftakis, KN.; Marques Cardoso, AJ.; Antonino-Daviu, J. (2017). Introducing the Filtered Park's and Filtered Extended Park's Vector Approach to Detect Broken Rotor Bars in Induction Motors Independently from the Rotor Slots Number. Mechanical Systems and Signal Processing. 93:30-50. https://doi.org/10.1016/j.ymssp.2017.01.046S30509

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

Stator Inter Turn Short Circuit Fault Diagnosis in Three Phase Induction Motor Using Neural Networks

In induction machine a number of faults occur namely bearing and insulation related faults, stator winding and rotor related faults. Among these, stator inter-turn fault is one of the most common faults.Therefore, this work deals with the diagnosis of inter turn short circuit fault in stator winding of an induction machine. These incipient faults need to be identified and cleared as soon as possible to reduce failures as well as maintenance cost.Conventional methods are time taking and require exact mathematical modelling of the machine. However, due to ageing effects the mathematical model has to be modified from time to time so that one can employ soft computing methods which are suitable in the situation where dynamics of the system is less understood such as the fault dynamics of an induction machine. In this thesis, one of the very popular soft computing techniques called artificial neural network is employed to diagnose the stator inter turn short-circuit fault in a three phase squirrel cage induction machine. Firstly, a multilayer perceptron neural network (MLPNN) has been applied for solving the above fault diagnosis problem.The root mean square error was plotted and the least value was found to be 0.065. In view of improving the training performance, a radial basis function neural network (RBFNN) with the same configuration as that of back propagation algorithm and Discrete Wavelet Transform was designed. Then the results of both the artificial neural networks and DWT were compared and it was found that RBFNN outperforms both the MLPNN and DWT based fault diagnosis approaches applied to the induction machine

Artificial Intelligence-based Technique for Fault Detection and Diagnosis of EV Motors: A Review

The motor drive system plays a significant role in the safety of electric vehicles as a bridge for power transmission. Meanwhile, to enhance the efficiency and stability of the drive system, more and more studies based on AI technology are devoted to the fault detection and diagnosis of the motor drive system. This paper reviews the application of AI techniques in motor fault detection and diagnosis in recent years. AI-based FDD is divided into two main steps: feature extraction and fault classification. The application of different signal processing methods in feature extraction is discussed. In particular, the application of traditional machine learning and deep learning algorithms for fault classification is presented in detail. In addition, the characteristics of all techniques reviewed are summarized. Finally, the latest developments, research gaps and future challenges in fault monitoring and diagnosis of motor faults are discussed

Online Condition Monitoring of Electric Powertrains using Machine Learning and Data Fusion

Safe and reliable operations of industrial machines are highly prioritized in industry. Typical industrial machines are complex systems, including electric motors, gearboxes and loads. A fault in critical industrial machines may lead to catastrophic failures, service interruptions and productivity losses, thus condition monitoring systems are necessary in such machines. The conventional condition monitoring or fault diagnosis systems using signal processing, time and frequency domain analysis of vibration or current signals are widely used in industry, requiring expensive and professional fault analysis team. Further, the traditional diagnosis methods mainly focus on single components in steady-state operations. Under dynamic operating conditions, the measured quantities are non-stationary, thus those methods cannot provide reliable diagnosis results for complex gearbox based powertrains, especially in multiple fault contexts. In this dissertation, four main research topics or problems in condition monitoring of gearboxes and powertrains have been identified, and novel solutions are provided based on data-driven approach. The first research problem focuses on bearing fault diagnosis at early stages and dynamic working conditions. The second problem is to increase the robustness of gearbox mixed fault diagnosis under noise conditions. Mixed fault diagnosis in variable speeds and loads has been considered as third problem. Finally, the limitation of labelled training or historical failure data in industry is identified as the main challenge for implementing data-driven algorithms. To address mentioned problems, this study aims to propose data-driven fault diagnosis schemes based on order tracking, unsupervised and supervised machine learning, and data fusion. All the proposed fault diagnosis schemes are tested with experimental data, and key features of the proposed solutions are highlighted with comparative studies.publishedVersio

Modelling and detection of faults in axial-flux permanent magnet machines

The development of various topologies and configurations of axial-flux permanent magnet machine has spurred its use for electromechanical energy conversion in several applications. As it becomes increasingly deployed, effective condition monitoring built on reliable and accurate fault detection techniques is needed to ensure its engineering integrity. Unlike induction machine which has been rigorously investigated for faults, axial-flux permanent magnet machine has not. Thus in this thesis, axial-flux permanent magnet machine is investigated under faulty conditions. Common faults associated with it namely; static eccentricity and interturn short circuit are modelled, and detection techniques are established. The modelling forms a basis for; developing a platform for precise fault replication on a developed experimental test-rig, predicting and analysing fault signatures using both finite element analysis and experimental analysis. In the detection, the motor current signature analysis, vibration analysis and electrical impedance spectroscopy are applied. Attention is paid to fault-feature extraction and fault discrimination. Using both frequency and time-frequency techniques, features are tracked in the line current under steady-state and transient conditions respectively. Results obtained provide rich information on the pattern of fault harmonics. Parametric spectral estimation is also explored as an alternative to the Fourier transform in the steady-state analysis of faulty conditions. It is found to be as effective as the Fourier transform and more amenable to short signal-measurement duration. Vibration analysis is applied in the detection of eccentricities; its efficacy in fault detection is hinged on proper determination of vibratory frequencies and quantification of corresponding tones. This is achieved using analytical formulations and signal processing techniques. Furthermore, the developed fault model is used to assess the influence of cogging torque minimization techniques and rotor topologies in axial-flux permanent magnet machine on current signal in the presence of static eccentricity. The double-sided topology is found to be tolerant to the presence of static eccentricity unlike the single-sided topology due to the opposing effect of the resulting asymmetrical properties of the airgap. The cogging torque minimization techniques do not impair on the established fault detection technique in the single-sided topology. By applying electrical broadband impedance spectroscopy, interturn faults are diagnosed; a high frequency winding model is developed to analyse the impedance-frequency response obtained

Reliable Multiphase Induction Motor Drives

A motor is said to be reliable if it can run at its rated operating condition for a specified period of time. With the widespread use of electric motors in newer applications, reliability is a major concern in terms of safety as well as revenue. About 30-40% of reported failures in induction motors are due to stator faults. It is well known that a stator fault starts as an inter-turn fault within a phase and then propagates into phase-to-phase and phase-to-ground faults that can then lead to complete shutdown of the motor. Two approaches have been taken in this dissertation to make an induction motor drive system more tolerant to stator faults; integration of an inter-turn fault detection method into a five-phase induction motor drive and design of fault-tolerant induction motors. The phase redundancy of five-phase motors makes it possible to achieve continued operation of the motor with an open phase. However, for true fault tolerance the drive must be able to detect an incipient fault and then transition to post fault operation. A low-cost diagnostic method based on DC voltage injection has been developed for detection of inter-turn faults in five-phase induction motor drive systems. It has been shown that difference in DC current response to an injected voltage before and after an inter-turn fault serves as a reliable fault indicator. The diagnostic is non-intrusive, requires no additional hardware and effectively integrates both fault detection and fault-tolerant control into the motor controller. The method has been successfully implemented and tested on low-cost microcontroller. The propagation of a stator inter-turn fault into a phase-to-phase fault is worsened in distributed winding induction motors where the different phase windings overlap each other at the end connections. Tooth wound or fractional slot concentrated winding (FSCW) stators have non-overlapping end connections and hence more physical and thermal isolation between the phases as compared to distributed winding stators. While FSCW configurations have been widely used for permanent magnet motors, their adoption for induction motors is a challenge. An FSCW configuration has been designed for outer rotor induction motors by using a dual slot layer stator structure and multilayer windings. Comparison with a conventional induction motor shows an 11% reduction in the copper usage in addition to having non-overlapping phase windings

Modeling and fault diagnosis of broken rotor bar faults in induction motors

Due to vast industrial applications, induction motors are often referred to as the “workhorse” of the industry. To detect incipient faults and improve reliability, condition monitoring and fault diagnosis of induction motors are very important. In this thesis, the focus is to model and detect broken rotor bar (BRB) faults in induction motors through the finite element analysis and machine learning approach. The most successfully deployed method for the BRB fault detection is Motor Current Signature Analysis (MSCA) due to its non-invasive, easy to implement, lower cost, reliable and effective nature. However, MSCA has its own limitations. To overcome such limitations, fault diagnosis using machine learning attracts more research interests lately. Feature selection is an important part of machine learning techniques. The main contributions of the thesis include: 1) model a healthy motor and a motor with different number of BRBs using finite element analysis software ANSYS; 2) analyze BRB faults of induction motors using various spectral analysis algorithms (parametric and non-parametric) by processing stator current signals obtained from the finite element analysis; 3) conduct feature selection and classification of BRB faults using support vector machine (SVM) and artificial neural network (ANN); 4) analyze neighbouring and spaced BRB faults using Burg and Welch PSD analysis