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

Stator current demodulation for induction machine rotor faults diagnosis

International audienceSeveral studies have demonstrated that induction machine faults introduce phase and/or amplitude modulation of the stator currents. Hence, demodulation of the stator currents is of high interest for induction machines faults detection and diagnosis. The demodulation techniques can be classified into mono-dimensional and multi-dimensional approaches. The monodimensional techniques include the synchronous demodulator, the Hilbert transform, the Teager energy operator and other approaches. The multi-dimensional approaches include the Concordia transform and the Principal Component Analysis. Once the demodulation has been performed, demodulated signals are further processed in order to measure failure severity. In this paper, we present a comprehensive comparison of these demodulation techniques for eccentricity and broken rotor bars faults detection

FEM approach for diagnosis of induction machines' non-adjacent broken rotor bars by short-time Fourier transform spectrogram

Rotor electrical faults are an issue frequently encountered when applying condition monitoring and fault diagnosis on induction machines. The detection via the analysis of the stator current becomes challenging when the rotor cage suffers from multiple breakages at non-adjacent positions. In that case, electromagnetic asymmetries induced by the broken bars can be masked in such a way, that the diagnostic ability is highly likely to be obscured, thus leading to misinterpretation of the monitored signals’ signatures. A new approach is proposed in this work to overcome this problem while the motor is at steady state. In this paper, an industrial 6.6kV, 1.1MW induction motor is simulated with Finite Element Analysis (FEM) and its electromagnetic variables are analysed and studied under healthy state and several faulty conditions. The analysis of the stator current and stray flux waveforms is executed in both the transient and the steady state and aims to diagnose the challenging cases where the rotor breakages are nonconsecutive with regards to their spatial location. The results show the potential of flux analysis to fault severity regardless of the spatial position of the broken bars

A New Approach for Broken Rotor Bar Detection in Induction Motors Using Frequency Extraction in Stray Flux Signals

This paper offers a reliable solution to the detection of broken rotor bars in induction machines with a novel methodology, which is based on the fact that the fault-related harmonics will have oscillating amplitudes due to the speed ripple effect. The method consists of two main steps: Initially, a time-frequency transformation is used and the focus is given on the steady-state regime; thereupon, the fault-related frequencies are handled as periodical signals over time and the classical fast Fourier transform is used for the evaluation of their own spectral content. This leads to the discrimination of subcomponents related to the fault and to the evaluation of their amplitudes. The versatility of the proposed method relies on the fact that it reveals the aforementioned signatures to detect the fault, regardless of the spatial location of the broken rotor bars. Extensive finite element simulations on a 1.1 MW induction motor and experimental testing on a 1.1 kW induction motor lead to the conclusion that the method can be generalized on any type of induction motor independently from the size, power, number of poles, and rotor slot numbers

On the broken rotor bar diagnosis using time-frequency analysis:'Is one spectral representation enough for the characterisation of monitored signals?'

© 2019 Institution of Engineering and Technology. All rights reserved. This work enhances the knowledge of the diagnostic potential of the broken bar fault in induction motors. Since a series of studies have been published over the years regarding condition monitoring and fault diagnostics of these machines, it is essential to reach a common ground on why - sometimes - different techniques render different results. In this context, an investigation is provided with regards to the optimal window that should be adopted for the implementation of a proper time-frequency analysis of the monitored signals. On this agenda, this study attempts to set lower and upper bound limits for proper windowing from the digital signal processing point of view. This is done by proposing a formula for the lower limit, which is derived according to the specific frequencies one desires to put under inspection and which are the fault-related signatures. Finally, a discussion on the upper bound is put onwards; results from finite-element simulations are examined with the discussed approach in both the transient regime and the steady state, while experimental results verify the simulations with satisfying accuracy

Gradual Wear Diagnosis of Outer-race Rolling Bearing Faults through Artificial Intelligence Methods and Stray Flux Signals

[EN] Electric motors have been widely used as fundamental elements for driving kinematic chains on mechatronic systems, which are very important components for the proper operation of several industrial applications. Although electric motors are very robust and efficient machines, they are prone to suffer from different faults. One of the most frequent causes of failure is due to a degradation on the bearings. This fault has commonly been diagnosed at advanced stages by means of vibration and current signals. Since low-amplitude fault-related signals are typically obtained, the diagnosis of faults at incipient stages turns out to be a challenging task. In this context, it is desired to develop non-invasive techniques able to diagnose bearing faults at early stages, enabling to achieve adequate maintenance actions. This paper presents a non-invasive gradual wear diagnosis method for bearing outer-race faults. The proposal relies on the application of a linear discriminant analysis (LDA) to statistical and Katz¿s fractal dimension features obtained from stray flux signals, and then an automatic classification is performed by means of a feed-forward neural network (FFNN). The results obtained demonstrates the effectiveness of the proposed method, which is validated on a kinematic chain (composed by a 0.746 KW induction motor, a belt and pulleys transmission system and an alternator as a load) under several operation conditions: healthy condition, 1 mm, 2 mm, 3 mm, 4 mm, and 5 mm hole diameter on the bearing outer race, and 60 Hz, 50 Hz, 15 Hz and 5 Hz power supply frequencies.This work was supported by the Spanish Ministerio de Ciencia Innovación y Universidades and FEDER program in the framework of the `Proyectos de I+D de Generación de Conocimiento del Programa Estatal de Generación de Conocimiento y Fortalecimiento Científico y Tecnológico del Sistema de I+D+i, Subprograma Estatal de Generación de Conocimiento¿ (ref: PGC2018-095747-B-I00), and Consejo Nacional de Ciencia y Tecnología (CONACyT) under scholarship 652815.Zamudio-Ramirez, I.; Osornio-Rios, RA.; Antonino-Daviu, JA.; Cureño-Osornio, J.; Saucedo-Dorantes, J. (2021). Gradual Wear Diagnosis of Outer-race Rolling Bearing Faults through Artificial Intelligence Methods and Stray Flux Signals. Electronics. 10(12):1-22. https://doi.org/10.3390/electronics10121486122101

Data Mining in Electrical Machine Maintenance Reports

Industrial electrical machine maintenance logs pertinent information, such as fault causality and earlier indications, in the form of a semi-standardized report, previously written and now in digital form. New practices in predictive maintenance, state-of-the-art condition monitoring, include increasing applications of machine learning. Reports contain a large volume of natural text in various languages and semantics, proving costly for feature extraction. This chapter aims to present novel techniques in information extraction to enable literature access to this untapped information reserve. A high level of correlation between text features and fault causality is noted, encouraging research for extended application in the scope of electrical machine maintenance, especially in artificial intelligence indication detection training. Furthermore, these innovative models can be used for decision-making during the repair. Information from well-trained classifiers can be extrapolated to advance fault causality understanding

Reliable Detection of Rotor Bars Breakage in Induction Motors via MUSIC and ZSC Methods

"(c) 2018 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] Induction motors are used in a variety of industrial applications where frequent startup cycles are required. In those cases, it is necessary to apply sophisticated signal processing analysis methods in order to reliably follow the time evolution of fault-related harmonics in the signal. In this paper, the zero-sequence current (ZSC) is analyzed using the high-resolution spectral method of multiple signal classification. The analysis of the ZSC signal has proved to have several advantages over the analysis of a single-phase current waveform. The method is validated through simulation and experimental results. The simulations are carried out for a 1.1-MW and a 4-kW induction motors under finite element analysis. Experimentation is performed on a healthy motor, a motor with one broken rotor bar, and a motor with two broken rotor bars. The analysis results are satisfactory since the proposed methodology reliably detects the broken rotor bar fault and its severity, both during transient and steady-state operation of the induction motor.This work was supported in part by the Spanish Ministerio de Economia y Competitividad (MINECO) and in part by the 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 under Grant DPI2014-52842-P.Morinigo-Sotelo, D.; Romero-Troncoso, R.; Panagiotou, P.; Antonino-Daviu, J.; Gyftakis, KN. (2018). Reliable Detection of Rotor Bars Breakage in Induction Motors via MUSIC and ZSC Methods. IEEE Transactions on Industry Applications. 54(2):1224-1234. https://doi.org/10.1109/TIA.2017.2764846S1224123454