Advanced Rotor Fault Diagnosis for Medium-Voltage Induction Motors Via Continuous Transforms

[EN] Anumber of field case studies for rotor fault diagnosis on medium-voltage induction motors operating in a petrochemical plant are presented in this paper. The methodology employed is based on analyzing the induction motor startup current with advanced signal processing tools (continuous transforms) that enable a capture of a complete picture of the rotor condition. Indeed, unlike the classical tools that often rely on the detection of few fault frequencies, these new tools allow extraction of the evolution of a wide range of fault components during the startup transient and steady-state evolutions, which enables improved reliability. This is crucial in medium-high-voltage motors, where a false diagnosis may result in significant expense due to inspection, repair, or forced outage. An additional contribution of the study is its immunity to external voltage supply disturbances, which introduce components that are not related to the failure and which are difficult to detect with classical tools. The results of this study prove how the advanced continuous tools enable an improved visualization of the fault components, distinguishing them from the other components that are not linked to the failure.This work was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science, and Technology under Grant NRF-2013R1A1A2010370, and in part by the Human Resources Development Program of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) funded by the Korea Government Ministry of Trade, Industry, and Energy under Grant 20134030200340Antonino-Daviu, J.; Pons Llinares, J.; Lee, SB. (2016). Advanced Rotor Fault Diagnosis for Medium-Voltage Induction Motors Via Continuous Transforms. IEEE Transactions on Industry Applications. 52(5):4503-4509. https://doi.org/10.1109/TIA.2016.2582720S4503450952

Transient-Based Rotor Cage Assessment in Induction Motors Operating With Soft Starters

[EN] The reliable assessment of the rotor condition in induction motors is a matter of increasing concern in the industry. Although rotor damages are more likely in line-started motors operating under high inertias, some cases of broken rotor bars in motors supplied via soft starters have been also reported in the industry. Motor current signature analysis (MCSA) is the most widely spread approach to diagnose such failures. However, its serious drawbacks in many real industrial applications have encouraged investigation on alternative methods enhancing the reliability of the diagnosis. This paper extends a recently introduced diagnosis methodology relying on the startup current analysis to the case of soft-starter-operated motors. The approach has proven to provide very satisfactory results, even in cases where the classical MCSA does not lead to correct diagnosis conclusions. However, its extension to operation under soft starters was still a pending issue. The experimental results shown in this paper ratify the validity of the proposed diagnosis approach in soft-starteroperated induction motors.This work was supported by the Spanish “Ministerio de Economía y Competitividad” (MINECO) in the framework of the “Proyectos I+D del Subprograma de Generación de Conocimiento, Programa Estatal de Fomento de la Investigación Científica y Técnica de Excelencia” under Grant DPI2014-52842-PCorral Hernández, JÁ.; Antonino-Daviu, J.; Pons Llinares, J.; Climente Alarcón, V.; Francés-Galiana, V. (2015). Transient-Based Rotor Cage Assessment in Induction Motors Operating With Soft Starters. IEEE Transactions on Industry Applications. 51(5):3734-3742. https://doi.org/10.1109/TIA.2015.2427271S3734374251

Toward condition monitoring of damper windings in synchronous motors via EMD analysis

(c) 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising 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] Failures in damper windings of synchronous machines operating in real facilities have been recently reported by several authors and companies. These windings are crucial elements of synchronous motors and generators, playing an important role in the asynchronous startup of these machines as well as in their stability during load transients. However, the diagnosis of failures in such elements has barely been studied in the literature. This paper presents a method to diagnose the condition of damper bars in synchronous motors. It is based on the capture of the stator current of the machine during a direct startup and its further analysis in order to track the characteristic transient evolution of a particular fault-related component in the time-frequency map. The fact that the damper only carries significant current during the startup and little or no current, when the machine operates in steady state, makes this transient-based approach specially suited for the detection of such failure. The Hilbert-Huang transform (based on the empirical mode decomposition method) is proposed as a signal-processing tool. Simulation and experimental results on laboratory synchronous machines prove the validity of the approach for condition monitoring of such windings. © 2012 IEEE.This work was supported by the Spanish Ministerio de Ciencia e Innovacion (MICINN) in the framework of the VI Plan Nacional de Investigacion Cientifica, Desarrollo e Innovacion Tecnologica 2008-2011. (Programa Nacional de proyectos de Investigacion Fundamental, project reference DPI2011-23740). Paper no. TEC-00443-2011.Antonino-Daviu, J.; Riera-Guasp, M.; Pons Llinares, J.; Roger-Folch, J.; Perez, R.; Charlton-Perez, C. (2012). Toward condition monitoring of damper windings in synchronous motors via EMD analysis. IEEE Transactions on Energy Conversion. 27(2):432-439. https://doi.org/10.1109/TEC.2012.2190292S43243927

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

Detection of Broken Outer-Cage Bars for Double-Cage Induction Motors Under the Startup Transient

(c) 2009 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising 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] Unlike single-cage rotor fault detection, fast Fourier transform (FFT)-based steady-state spectrum analysis techniques can fail to detect outer-cage faults in double-cage induction motors due to the small outer-cage current under running conditions. Double-cage motors are typically employed in applications that require loaded starts. This makes the outer cage vulnerable to fatigue failure since it must withstand the high starting current and long startup time frequently. However, there are only a few publications that investigate detection techniques specifically for double-cage motors. In this paper, considering that the influence of the faulty outer cage is strong at startup due to the large outer-cage current, detection of outer-cage faults under the startup transient is investigated. A discrete-wavelet-transform-based method is proposed as a viable solution to the detection of outer-cage faults for double-cage motors. An experimental study on fabricated copper double-cage induction motors shows that the proposed method provides sensitive and reliable detection of double-cage rotor faults compared to FFT.This work was supported in part by the Spanish “Ministerio de Educación y Ciencia,” in the framework of the “Programa Nacional de Proyectos de Investigación Fundamental,” under Project Reference DPI2008-06583/DPI, and in part by the Human Resources Development of Korea Institute of Energy Technology Evaluation and Planning under Grant 20114010203010 funded by the Korean Government Ministry of Knowledge EconomyAntonino-Daviu, J.; Riera-Guasp, M.; Pons Llinares, J.; Park, J.; Lee, SB.; Yoo, J.; Kral, C. (2012). Detection of Broken Outer-Cage Bars for Double-Cage Induction Motors Under the Startup Transient. IEEE Transactions on Industry Applications. 48(5):1539-1548. https://doi.org/10.1109/TIA.2012.2210173S1539154848

Transient tracking of low and high-order eccentricity-related components in induction motors via TFD tools

[EN] The present work is focused on the diagnosis of mixed eccentricity faults in induction motors via the study of currents demanded by the machine. Unlike traditional methods, based on the analysis of stationary currents (Motor Current Signature Analysis (MCSA)), this work provides new findings regarding the diagnosis approach proposed by the authors in recent years, which is mainly focused on the fault diagnosis based on the analysis of transient quantities, such as startup or plug stopping currents (Transient Motor Current Signature Analysis (TMCSA)), using suitable time-frequency decomposition (TFD) tools. The main novelty of this work is to prove the usefulness of tracking the transient evolution of high-order eccentricity-related harmonics in order to diagnose the condition of the machine, complementing the information obtained with the low-order components, whose transient evolution was well characterised in previous works. Tracking of high-order eccentricity-related harmonics during the transient, through their associated patterns in the time-frequency plane, may significantly increase the reliability of the diagnosis, since the set of fault-related patterns arising after application of the corresponding TFD tool is very unlikely to be caused by other faults or phenomena. Although there are different TFD tools which could be suitable for the transient extraction of these harmonics, this paper makes use of a WignerVille distribution (WVD)-based algorithm in order to carry out the time-frequency decomposition of the startup current signal, since this is a tool showing an excellent trade-off between frequency resolution at both high and low frequencies. Several simulation results obtained with a finite element-based model and experimental results show the validity of this fault diagnosis approach under several faulty and operating conditions. Also, additional signals corresponding to the coexistence of the eccentricity and other non-fault related phenomena making difficult the diagnosis (fluctuating load torque) are included in the paper. Finally, a comparison with an alternative TFD tool the discrete wavelet transform (DWT) applied in previous papers, is also carried out in the contribution. The results are promising regarding the usefulness of the methodology for the reliable diagnosis of eccentricities and for their discrimination against other phenomena. © 2010 Elsevier Ltd.All rights reserved.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 DP12008-06583/DPI.Climente Alarcón, V.; Antonino-Daviu, J.; Riera-Guasp, M.; Pons Llinares, J.; Roger-Folch, J.; Jover-Rodriguez, P.; Arkkio, A. (2011). Transient tracking of low and high-order eccentricity-related components in induction motors via TFD tools. Mechanical Systems and Signal Processing. 25(2):667-679. https://doi.org/10.1016/j.ymssp.2010.08.008S66767925

Calculation of Winding Inductances via Magnetic Vector Potential, Discrete Convolution and Fast Fourier Transform

[Otro] Przedstawiono analiz¿ uszkodze¿ silnika indukcyjnego spowodowanych z¿amaniem pr¿tu, lub zwarciem wewn¿trznym. Oszacowano amplitud¿ wynikaj¿c¿ z uszkodzenia. Induktancja uzwoje¿ jest kluczowa w tym modelu. Zaproponowana metoda jest bardzo prosta i szybka. Zastosowano wektorowy potencja¿ magnetyczny oraz szybk¿ transformat¿ Fouriera.[EN] Fault analysis of induction motors with broken bars or inter-turn short circuits needs accurate machine models to correctly identify and quantify the magnitude of the fault. Winding inductances and their derivatives are a key component of these models. In this paper a new, very easy and extremely fast method to compute them, based on the Magnetic Vector Potential (MVP), the Discrete Circular Convolution and the Fast Fourier Transform (FFT), is presented.This work was supported by the European Community's Seventh Framework Program FP7/2007-2013 under Grant Agreement 224233 (Research Project PRODI "Power Plant Robustification based on Fault Detection and Isolation Algorithms")Pineda-Sánchez, M.; Roger-Folch, J.; Pérez-Cruz, J.; Riera-Guasp, M.; Puche-Panadero, R.; Antonino-Daviu, J.; Pons Llinares, J. (2010). Calculation of Winding Inductances via Magnetic Vector Potential, Discrete Convolution and Fast Fourier Transform. Przegląd Elektrotechniczny. 86(5):109-113. http://hdl.handle.net/10251/103509S10911386

Very Accurate Time-Frequency Representation of Induction Motors Harmonics for Fault Diagnosis Under Load Variations

Induction motors work under steady-state in many applications. Nevertheless, in some cases they experience periodic load fluctuations, which generate constant frequency harmonics close to variable frequency bar breakage harmonics. In these cases, time-frequency (t-f) transforms are better suited than steady-state analysis since the fault harmonic frequencies change in time. Even if the healthy and faulty frequencies do not overlap in the spectrum, if the speed is unknown, it is difficult to distinguish the constant frequency healthy harmonic from the variable frequency bar breakage harmonic. On the other hand, transient techniques present in technical literature are not precise enough to deal with both the changing frequency of the bar breakage harmonic and a close constant frequency (as the one generated by most of the periodic load fluctuations). To achieve reliable results under these challenging situations, a very precise time-frequency transform must be used, enabling to simultaneously draw the constant and variable frequencies, even if they are very close in the t-f plane. The Dragon-Transform is here proposed to address the problem. It is shown through simulation and experimental results, how it enables to very accurately plot up to five faulty harmonics evolutions, distinguishing at the same time the constant frequency of the load oscillation, traced as a very thin horizontal line. Precision is so high that even the oscillations caused by ripple effect can be observed for the first time in technical literature, enhancing the reliability of the diagnosis performed, and opening the path for a true solution of the problem

Role of age and comorbidities in mortality of patients with infective endocarditis

[Purpose]: The aim of this study was to analyse the characteristics of patients with IE in three groups of age and to assess the ability of age and the Charlson Comorbidity Index (CCI) to predict mortality. [Methods]: Prospective cohort study of all patients with IE included in the GAMES Spanish database between 2008 and 2015.Patients were stratified into three age groups:<65 years,65 to 80 years,and ≥ 80 years.The area under the receiver-operating characteristic (AUROC) curve was calculated to quantify the diagnostic accuracy of the CCI to predict mortality risk. [Results]: A total of 3120 patients with IE (1327 < 65 years;1291 65-80 years;502 ≥ 80 years) were enrolled.Fever and heart failure were the most common presentations of IE, with no differences among age groups.Patients ≥80 years who underwent surgery were significantly lower compared with other age groups (14.3%,65 years; 20.5%,65-79 years; 31.3%,≥80 years). In-hospital mortality was lower in the <65-year group (20.3%,<65 years;30.1%,65-79 years;34.7%,≥80 years;p < 0.001) as well as 1-year mortality (3.2%, <65 years; 5.5%, 65-80 years;7.6%,≥80 years; p = 0.003).Independent predictors of mortality were age ≥ 80 years (hazard ratio [HR]:2.78;95% confidence interval [CI]:2.32–3.34), CCI ≥ 3 (HR:1.62; 95% CI:1.39–1.88),and non-performed surgery (HR:1.64;95% CI:11.16–1.58).When the three age groups were compared,the AUROC curve for CCI was significantly larger for patients aged <65 years(p < 0.001) for both in-hospital and 1-year mortality. [Conclusion]: There were no differences in the clinical presentation of IE between the groups. Age ≥ 80 years, high comorbidity (measured by CCI),and non-performance of surgery were independent predictors of mortality in patients with IE.CCI could help to identify those patients with IE and surgical indication who present a lower risk of in-hospital and 1-year mortality after surgery, especially in the <65-year group

Evaluation of startup-based rotor fault severity indicators under different starting methods

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