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

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 review of current signature analysis for condition monitoring of wound rotor induction generator and fault diagnosis techniques

Abstract: The wound-rotor induction generator (WRIG) is commonly used for wind energy application. WRIGs have simple construction, are robust with high starting torque and low starting current. Additionally, WRIGs allow rotor resistance control and can be driven at variable speeds. Despite the relative robustness of WRIGs, these machines still experience a variety of faults in practice. This paper presents an in-depth review of condition monitoring techniques for three-phase wound-rotor induction generators. Various recent research applying current signature analysis as a method of detecting and diagnosing different types of faults on both the stator and rotor of this machine is reviewed. The application of probabilistic and artificial intelligence methods such as Bayesian classification, artificial neural networks and fuzzy logic used for fault diagnosis are also investigated

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

Condition Monitoring System of Wind Turbine Generators

The development and implementation of the condition monitoring systems (CMS) play a significant role in overcoming the number of failures in the wind turbine generators that result from the harsh operation conditions, such as over temperature, particularly when turbines are deployed offshore. In order to increase the reliability of the wind energy industry, monitoring the operation conditions of wind generators is essential to detect the immediate faults rapidly and perform appropriate preventative maintenance. CMS helps to avoid failures, decrease the potential shutdowns while running, reduce the maintenance and operation costs and maintain wind turbines protected. The knowledge of wind turbine generators\u27 faults, such as stator and rotor inter-turn faults, is indispensable to perform the condition monitoring accurately, and assist with maintenance decision making. Many techniques are utilized to avoid the occurrence of failures in wind turbine generators. The majority of the previous techniques that are applied to monitor the wind generator conditions are based on electrical and mechanical concepts and theories. An advanced CMS can be implemented by using a variety of different techniques and methods to confirm the validity of the obtained electrical and mechanical condition monitoring algorithms. This thesis is focused on applying CMS on wind generators due to high temperature by contributing the statistical, thermal, mathematical, and reliability analyses, and mechanical concepts with the electrical methodology, instead of analyzing the electrical signal and frequencies trends only. The newly developed algorithms can be compared with previous condition monitoring methods, which use the electrical approach in order to establish their advantages and limitations. For example, the hazard reliability techniques of wind generators based on CMS are applied to develop a proper maintenance strategy, which aims to extend the system life-time and reduce the potential failures during operation due to high generator temperatures. In addition, the use of some advanced statistical techniques, such as regression models, is proposed to perform a CMS on wind generators. Further, the mechanical and thermal characteristics are employed to diagnose the faults that can occur in wind generators. The rate of change in the generator temperature with respect to the induced electrical torque; for instance is considered as an indicator to the occurrence of faults in the generators. The behavior of the driving torque of the rotating permanent magnet with respect to the permanent magnet temperature can also utilize to indicate the operation condition. The permanent magnet model describes the rotating permanent magnet condition during operation in the normal and abnormal situations. In this context, a set of partial differential equations is devolved for the characterization of the rotations of the permanent. Finally, heat transfer analysis and fluid mechanics methods are employed to develop a suitable CMS on the wind generators by analyzing the operation conditions of the generator\u27s heat exchanger. The proposed methods applied based on real data of different wind turbines, and the obtained results were very convincing

State of the art and trends in the monitoring, detection and diagnosis of failures in electric induction motors

Producción CientíficaDespite the complex mathematical models and physical phenomena on which it is based, the simplicity of its construction, its affordability, the versatility of its applications and the relative ease of its control have made the electric induction motor an essential element in a considerable number of processes at the industrial and domestic levels, in which it converts electrical energy into mechanical energy. The importance of this type of machine for the continuity of operation, mainly in industry, is such that, in addition to being an important part of the study programs of careers related to this branch of electrical engineering, a large number of investigations into monitoring, detecting and quickly diagnosing its incipient faults due to a variety of factors have been conducted. This bibliographic research aims to analyze the conceptual aspects of the first discoveries that served as the basis for the invention of the induction motor, ranging from the development of the Fourier series, the Fourier transform mathematical formula in its different forms and the measurement, treatment and analysis of signals to techniques based on artificial intelligence and soft computing. This research also includes topics of interest such as fault types and their classification according to the engine, software and hardware parts used and modern approaches or maintenance strategies

Experimental diagnosis of inter-turns stator fault and unbalanced voltage supply in induction motor using MCSA and DWER

This paper presents a comparative study between two techniques of signal processing to diagnose both faults the inter-turn short circuit (ITSC) in stator windings and the unbalanced voltage supply (UVS) in induction motors. The first is considered a classical technique called Motor Current Signature Analysis (MCSA) which is based on the processing of the stator current by the Fast Fourier Transform (FFT). The second is anadvanced technique based on a Discrete Wavelet Energy Ratio (DWER) of three stator currents. The aim objective of this paper is to compare the ability and effectiveness of both techniques to detect the ITSC fault and the UVS in induction motors, and distinguishing between them. An experimental implementation tests the two diagnosis techniques.The results obtained show that the MCAS technique by the FFT analysis has a difficult to discriminate between the current harmonics due to the provide voltage unbalance and those originated by ITSC faults. Unlike the DWERtechnique, which has high sensitivity and exceptional ability to detect and distinguish between the two faults that lead to the reliability of the diagnosis system. To demonstrate that the DWER is an accurate and robust diagnosis approach are used the neural network (NN) as a tool to classify the faults (ITSC and USV) where using DWER indicators as NN input. The results obtained of combination between the DWER and NN are effective and proved its ability to detect both faults under different load conditions and distinguish between them accurately with low error (10-5)

Electromagnetic flux monitoring for detecting faults in electrical machines

The ability of the electromagnetic flux measured in various locations of a 35-kW cage induction motor to provide useful information about faults was investigated. The usefulness of this monitoring parameter was assessed in comparison with some other electrical parameters used for fault detection, such as stator phase current and circulating currents between the parallel branches of the stator winding. The following faults were investigated in this thesis: a turn-to-turn short circuit in the stator winding; rotor cage-related faults (breakage of rotor bars); static and dynamic eccentricity, and bearing fault. The relevant fault signatures of the studied electrical parameters were obtained from measurements and/or from numerical electromagnetic field simulations in steady state. These signatures were analysed and compared in order to deduce the most appropriate quantity for the detection of a specific fault. When and where possible, the accuracy of different fault signatures issuing from numerical electromagnetic field simulations was validated by experiments. This investigation is essential since, following a good agreement, it may be assumed that if a monitoring system cannot detect and diagnose an artificial fault from the virtual measurement signals, it is hardly likely to work with real electrical machines, either. In this respect, the numerical methods of analysis limited the present study to such faults that affect the electromagnetic field of a machine. On the exclusive basis of data obtained from simulations, a study of the modifications brought by various stator winding designs to some of the asymmetrical air-gap electromagnetic flux density harmonics responsible for the detection of various faults was carried out. The analysis of a core fault (insulation fault in the stator lamination) artificially implemented in the numerical electromagnetic model of the machine in terms of finding a suitable parameter to sense such a fault was also studied in this work.reviewe

On-line measurement of partial discharges in high voltage rotating machines.

The on-line condition monitoring of rotating machines is given paramount importance, particularly in Oils and Gas industries where the financial implications of machine shutdown is very high. This project work was directed towards the on-line condition monitoring of high voltage rotating machines by detection of partial discharges (PD) which are indicative of stator insulation degradation. Partial discharge manifests itself in various forms which can be detected using various electrical and non-electrical techniques. The electrical method of detecting small current pulses generated by PD using a Rogowski coil as a sensor has been investigated in this work. Dowding & Mills, who are commercially involved in the condition monitoring of rotating machines, currently use a system called StatorMonotor® for PD detection. The research is intended to develop a new partial discharge detection system that will replace the existing system which is getting obsolete. A three phase partial discharge detection unit was specified, designed and developed that is capable of filtering, amplifying and digitising the discharge signals. The associated data acquisition software was developed using LabVIEW software that was capable of acquiring, displaying and storing the discharge signals. Additional software programs were devised to investigate the removal of external noise. A data compression algorithm was developed to store the discharge data in an efficient manner; also ensuring the backward compatibility to the existing analysis software. Tests were performed in laboratory and on machines on-site and the results are presented. Finally, the data acquisition (DAQ) cards that used the PCMCIA bus was replaced with new USB based DAQ cards with the software modified accordingly. The three phase data acquisition unit developed as a result of this project has produced encouraging results and will be implemented in an industrial environment to evaluate and benchmark its performance with the existing system. Most importantly, a hardware data acquisition platform for the detection of PD pulses has been established within the company which is easily maintainable and expandable to suit any future requirements