Current-Based Detection of Mechanical Unbalance in an Induction Machine Using Spectral Kurtosis with Reference

This article explores the design, on-line, of an electrical machine’s healthy reference by means of statistical tools. The definition of a healthy reference enables the computation of normalized fault indicators whose value is independent of the system’s characteristics. This is a great advantage when diagnosing a broad range of systems with different power, coupling, inertia, load, etc. In this paper, an original method called spectral kurtosis with reference is presented in order to designa system’s healthy reference. Its principle is first explained on asynthetic signal. This approach is then evaluated for mechanicalunbalance detection in an induction machine using the stator currents instantaneous frequency. The normalized behaviour ofthe proposed indicator is then confirmed for different operatingconditions and its robustness with respect to load variationsis demonstrated. Finally, the advantages of using a statisticalindicator based on a healthy reference compared to a raw faultsignature are discussed

Novelty detection based condition monitoring scheme applied to electromechanical systems

This study is focused on the current challenges dealing with electromechanical system monitoring applied in industrial frameworks, that is, the presence of unknown events and the limitation to the nominal healthy condition as starting knowledge. Thus, an industrial machinery condition monitoring methodology based on novelty detection and classification is proposed in this study. The methodology is divided in three main stages. First, a dedicated feature calculation and reduction over each available physical magnitude. Second, an ensemble structure of novelty detection models based on one-class support vector machines to identify not previously considered events. Third, a diagnosis model supported by a feature fusion scheme in order to reach high fault classification capabilities. The effectiveness of the fault detection and identification methodology has been compared with classical single model approach, and verified by experimental results obtained from an electromechanical machine. © 2018 IEEE.Postprint (author's final draft

Speed sensorless with modified rotor flux field oriented control of faculty three-phase induction motor

This thesis proposes a high performance speed sensorless vector control of star-connected three-phase Induction Motor (TPIM) under open-phase fault. The proposed drive system consists of two parts: Indirect Rotor flux Field-Oriented Control (Indirect RFOC) and speed estimation based on Model Reference Adaptive System (MRAS). In RFOC of TPIM, rotor speed estimation is required in order to implement the control algorithm. The rotor speed can either be obtained using a mechanical speed sensor or it can be estimated from the terminal variables of the TPIM using an observer. In this work, rotor speed is estimated using an observer which is based on MRAS. However, unlike other MRAS based speed estimators, the proposed observer is designed to work for both healthy and faulty TPIM. When a fault occurred, minimum changes to the control parameters and special transformation to the variables of the RFOC and MRAS speed estimator are performed. The ability of the drive system to work in both healthy and faulty conditions is important in some critical applications that require continuous operation of the drive systems. To verify the effectiveness and reliability of the proposed method, simulations and experiments are conducted. In this research, MATLAB/Simulink software is used to evaluate the effectiveness of the proposed method. Verification and validation of the proposed drive system are through hardware implementation using dSPACE DS 1104 ACE KIT and 1.5 kW TPIM. The simulation and experiment results show that satisfactory performance of the indirect RFOC and MRAS for a TPIM under open-phase fault is achieved. It is shown that the torque and speed oscillations caused by the unbalanced structure of the faulty TPIM are effectively reduced by more than 50%. Speed sensorlesss RFOC of TPIM under open-phase fault condition is shown to be capable of operating in speed range from zero to 60 rad/s, however with reduced torque capability

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

Self-tuning observer for sensor fault-tolerant control of induction motor drive

This paper introduces a new solution for the speed and current sensor fault-tolerant direct field-oriented control of induction motor drives. Two self-adjusting observers derived from a modified current-based model reference adaptive system (CB-MRAS) are presented. Finally, the recursive least squares method was used to estimate the parameters of the used observers. The method, in the proposed solution, provides a very fast and accurate finding of the observer parameters while maintaining relative simplicity and ease of implementation. The presented algorithm eliminates the CB-MRAS observer dependence on the induction motor parameters and also compensates for the inaccuracies in the evaluation of the stator voltage vector. The proposed fault-tolerant control offers the drive operation while either a speed sensor or one/two current sensors fault occurs. The drive still works with the direct field-oriented control even when no current sensors are healthy. The proposed scheme was simulated in the MATLAB/Simulink software environment. Then the algorithm was implemented in a floating-point digital signal controller (DSC) TMS320F28335 and tested on an induction motor drive prototype of rated power of 2.2 kW to validate the proposed schemes.Web of Science149art. no. 256

Wound Rotor Induction Generator Inter-Turn Short-Circuits Diagnosis Using a New Digital Neural Network

International audienceThis paper deals with a new transformation and fusion of digital input patterns used to train and test feedforward neural network for a wound-rotor three-phase induction machine windings short-circuit diagnosis. The single type of short-circuits tested by the proposed approach is based on turn-to-turn fault which is known as the first stage of insulation degradation. Used input/output data have been binary coded in order to reduce the computation complexity. A new procedure, namely addition and mean of the set of same rank, has been implemented to eliminate the redundancy due to the periodic character of input signals. However, this approach has a great impact on the statistical properties on the processed data in terms of richness and of statistical distribution. The proposed neural network has been trained and tested with experimental signals coming from six current sensors implemented around a setup with a prime mover and a 5.5 kW wound-rotor three-phase induction generator. Both stator and rotor windings have been modified in order to sort out first and last turns in each phase. The experimental results highlight the superiority of using this new procedure in both training and testing modes

Sistema de detecção e diagnóstico de falhas em geradores de indução duplamente alimentados

Dissertação para obtenção do Grau de Mestre em Engenharia Electrotécnica e de ComputadoresO uso de fontes de energia renovável na produção de energia eléctrica tem vindo registar um crescimento, como consequência das políticas ambientais que durante as últimas décadas têm promovido o uso de energias limpas. Entre as diversas fontes de energia renovável, a energia eólica ocupa um lugar de destaque com um crescimento de 28% nos últimos 15 anos. Com a implementação da energia eólica na rede eléctrica, o gerador de indução duplamente alimentado (GIDA) tem sido bastante utilizado devido à sua robustez. Contudo, e tendo em conta as características dos sistemas de energia eólica, estes estão susceptíveis a elementos naturais, tais como ventos fortes e chuvas, que podem provocar falhas no gerador de indução e afectar a performance do sistema. Manter em funcionamento o GIDA durante a ocorrência de uma falha é indesejável e pode ter consequências graves para o próprio equipamento. Desta forma, é importante que os sistemas de energia eólica disponham de sistemas de detecção e diagnóstico de falhas. Com estes sistemas é possível detectar a presença de falhas no seu estado inicial e assim evitar a degradação dos equipamentos e da rede. Em suma, o principal objectivo deste trabalho é desenvolver um sistema de detecção e diagnóstico de falhas baseado na análise das correntes do estator de um GIDA. O sistema proposto usa técnicas como o PCA e FFT, para o processamento das correntes lidas e assim detectar possíveis falhas

Artificial Intelligence Supported EV Electric Powertrain for Safety Improvement

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

On-line Condition Monitoring, Fault Detection and Diagnosis in Electrical Machines and Power Electronic Converters

The objective of this PhD research is to develop robust, and non-intrusive condition monitoring methods for induction motors fed by closed-loop inverters. The flexible energy forms synthesized by these connected power electronic converters greatly enhance the performance and expand the operating region of induction motors. They also significantly alter the fault behavior of these electric machines and complicate the fault detection and protection. The current state of the art in condition monitoring of power-converter-fed electric machines is underdeveloped as compared to the maturing condition monitoring techniques for grid-connected electric machines. This dissertation first investigates the stator turn-to-turn fault modelling for induction motors (IM) fed by a grid directly. A novel and more meaningful model of the motor itself was developed and a comprehensive study of the closed-loop inverter drives was conducted. A direct torque control (DTC) method was selected for controlling IM’s electromagnetic torque and stator flux-linkage amplitude in industrial applications. Additionally, a new driver based on DTC rules, predictive control theory and fuzzy logic inference system for the IM was developed. This novel controller improves the performance of the torque control on the IM as it reduces most of the disadvantages of the classical and predictive DTC drivers. An analytical investigation of the impacts of the stator inter-turn short-circuit of the machine in the controller and its reaction was performed. This research sets a based knowledge and clear foundations of the events happening inside the IM and internally in the DTC when the machine is damaged by a turn fault in the stator. This dissertation also develops a technique for the health monitoring of the induction machine under stator turn failure. The developed technique was based on the monitoring of the off-diagonal term of the sequence component impedance matrix. Its advantages are that it is independent of the IM parameters, it is immune to the sensors’ errors, it requires a small learning stage, compared with NN, and it is not intrusive, robust and online. The research developed in this dissertation represents a significant advance that can be utilized in fault detection and condition monitoring in industrial applications, transportation electrification as well as the utilization of renewable energy microgrids. To conclude, this PhD research focuses on the development of condition monitoring techniques, modelling, and insightful analyses of a specific type of electric machine system. The fundamental ideas behind the proposed condition monitoring technique, model and analysis are quite universal and appeals to a much wider variety of electric machines connected to power electronic converters or drivers. To sum up, this PhD research has a broad beneficial impact on a wide spectrum of power-converter-fed electric machines and is thus of practical importance