Wavelet-Based Analysis of MCSA for Fault Detection in Electrical Machine

Early detection of irregularity in electrical machines is important because of their diversity of use in different fields. A proper fault detection scheme helps to stop the propagation of failure or limits its escalation to severe degrees, and thus it prevents unscheduled downtimes that cause loss of production and financial income. Among different modes of failures that may occur in the electrical machines, the rotor-related faults are around 20%. Successful detection of any failure in electrical machines is achieved by using a suitable condition monitoring followed by accurate signal processing techniques to extract the fault features. This article aims to present the extraction of features appearing in current signals using wavelet analysis when there is a rotor fault of eccentricity and broken rotor bar. In this respect, a brief explanation on rotor failures and different methods of condition monitoring with the purpose of rotor fault detection is provided. Then, motor current signature analysis, the fault-related features appeared in the current spectrum and wavelet transform analyses of the signal to extract these features are explained. Finally, two case studies involving the wavelet analysis of the current signal for the detection of rotor eccentricity and broken rotor bar are presented

Diagnosis of Induction Motor Faults in Time-Varying Conditions Using the Polynomial-Phase Transform of the Current

© 2011 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] Transient motor current signature analysis is a recently developed technique for motor diagnostics using speed transients. The whole speed range is used to create a unique stamp of each fault harmonic in the time-frequency plane. This greatly increases diagnostic reliability when compared with non-transient analysis, which is based on the detection of fault harmonics at a single speed. But this added functionality comes at a price: well-established signal analysis tools used in the permanent regime, mainly the Fourier transform, cannot be applied to the nonstationary currents of a speed transient. In this paper, a new method is proposed to fill this gap. By applying a polynomial-phase transform to the transient current, a new, stationary signal is generated. This signal contains information regarding the fault components along the different regimes covered by the transient, and can be analyzed using the Fourier transform. The polynomial-phase transform is used in radar, sonar, communications, and power systems fields, but this is the first time, to the best knowledge of the authors, that it has been applied to the diagnosis of induction motor faults. Experimental results obtained with two different commercial motors with broken bars are presented to validate the proposed method.This work was supported by the Spanish "Ministerio de Educacion y Ciencia" in the framework of the "Programa Nacional de Proyectos de Investigacion Fundamental," project reference DPI2008-06583/DPI.Pineda-Sanchez, M.; Riera-Guasp, M.; Roger-Folch, J.; Antonino-Daviu, J.; Pérez-Cruz, J.; Puche-Panadero, R. (2011). Diagnosis of Induction Motor Faults in Time-Varying Conditions Using the Polynomial-Phase Transform of the Current. IEEE Transactions on Industrial Electronics. 58(4):1428-1439. https://doi.org/10.1109/TIE.2010.2050755S1428143958

Feature Extraction for the Prognosis of Electromechanical Faults in Electrical Machines through the DWT

[EN] Recognition of characteristic patterns is proposed in this paper in order to diagnose the presence of electromechanical faults in induction electrical machines. Two common faults are considered; broken rotor bars and mixed eccentricities. The presence of these faults leads to the appearance of frequency components following a very characteristic evolution during the startup transient. The identification and extraction of these characteristic patterns through the Discrete Wavelet Transform (DWT) have been proven to be a reliable methodology for diagnosing the presence of these faults, showing certain advantages in comparison with the classical FFT analysis of the steady-state current. In the paper, a compilation of healthy and faulty cases are presented; they confirm the validity of the approach for the correct diagnosis of a wide range of electromechanical faults.The research leading to these results has received funding from the European Community's Seventh Framework Programme FP7/2007-2013 under Grant Agreement n° 224233 (Research Project PRODI “Power plant Robustification based on fault Detection and Isolation algorithms”). The authors also thank ‘Vicerrectorado de Investigación, Desarrollo e Innovación of Universidad Politécnica de Valencia’ for financing a part of this research through the program ‘Programa de Apoyo a la Investigación y Desarrollo (PAID-06-07).Antonino-Daviu, J.; Riera-Guasp, M.; Pineda-Sanchez, M.; Pons Llinares, J.; Puche-Panadero, R.; Pérez-Cruz, J. (2009). Feature Extraction for the Prognosis of Electromechanical Faults in Electrical Machines through the DWT. International Journal of Computational Intelligence Systems. 2(2):158-167. https://doi.org/10.2991/ijcis.2009.2.2.71581672

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

Enhanced Simulink Induction Motor Model for Education and Maintenance Training

[EN] The training of technicians in maintenance requires the use of signals produced by faulty machines in different operating conditions, which are difficult to obtain either from the industry or through destructive testing. Some tasks in electricity and control courses can also be complemented by an interactive induction machine model having a wider internal parameter configuration. This paper presents a new analytical model of induction machine under fault, which is able to simulate induction machines with rotor asymmetries and eccentricity in different load conditions, both stationary and transient states and yielding magnitudes such as currents, speed and torque. This model is faster computationally than the traditional method of simulating induction machine faults based on the Finite Element Method and also than other analytical models due to the rapid calculation of the inductances. The model is presented in Simulink by Matlab for the comprehension and interactivity with the students or lecturers and also to allow the easy combination of the effect of the fault with external influences, studying their consequences on a determined load or control system. An associated diagnosis tool is also presented.This work was supported by the Spanish Ministerio de Ciencia e Innovación under the framework of the Programa Nacional de Proyectos de Investigación Fundamental, Project Reference DPI2011-23740Pineda-Sanchez, M.; Climente Alarcón, V.; Riera-Guasp, M.; Puche-Panadero, R.; Pons Llinares, J. (2012). Enhanced Simulink Induction Motor Model for Education and Maintenance Training. Journal of Systemics, Cybernetics and Informatics. 10(2):92-97. http://hdl.handle.net/10251/105282S929710

Advanced Analysis of Motor Currents for the Diagnosis of the Rotor Condition in Electric Motors Operating in Mining Facilities

© 1972-2012 IEEE. Predictive maintenance of electric motors is a topic of increasing importance in many industrial applications. The mining industry is not an exception; many electric motors operating in mining facilities are critical machines, and their unexpected failures may imply significant losses and can be hazardous for the users. Due to these facts, an increasing research effort has been dedicated to investigate new techniques that are able to provide a reliable diagnostic of the motor condition. Over recent years, monitoring of electrical quantities (e.g., motor currents) has emerged as a very attractive option for determining the health of several motor parts (rotor, eccentricities, bearings) due to its very interesting advantages: possibility of remote motor monitoring, noninvasive nature, simple application, broad fault coverage, etc. The traditional methods based on the analysis of motor currents during a steady-state operation [motor current signature analysis (MCSA)] are being complemented when not replaced by more reliable approaches. This paper applies an innovative transient-based methodology to several case studies referred to large motors operating in mining facilities. The results prove how this modern methodology enables us to overcome some important drawbacks of the classical MCSA, such as its unsuitability under varying speed conditions, and may provide an earlier indication of rotor electrical asymmetries under such working conditions