Detection of Broken Rotor Bars in Nonlinear Startups of Inverter-Fed Induction Motors

[EN] Fault detection in induction motors powered by inverters operating in nonstationary regimes remains a challenge. The trajectory in the time-frequency plane of harmonics related to broken rotor bar develops very in proximity to the path described by the fundamental component. In addition, their energy is much lower than the amplitude of the first harmonic. These two characteristics make it challenging to observe them. The Dragon Transform (DT), here presented, is developed to overcome the described problem. In this article, the DT is assessed with nonlinear inverter-fed startups, where its high time and frequency resolutions facilitate the monitoring of fault harmonics even with highly adjacent trajectories to the first harmonic path.Fernández-Cavero, V.; Pons Llinares, J.; Duque-Perez, O.; Morinigo-Sotelo, D. (2021). Detection of Broken Rotor Bars in Nonlinear Startups of Inverter-Fed Induction Motors. IEEE Transactions on Industry Applications. 57(3):2559-2568. https://doi.org/10.1109/TIA.2021.30663172559256857

A comparison of techniques for fault detection in inverter-fed induction motors in transient regime

"(c) 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, 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 components of this work in other works."[EN] Fault detection in induction motors operating in non-stationary regimes has become a need in today's industry. Most of the works published deal with line-fed motors. Nevertheless, the number of inverterfed induction motors has significantly increased in recent years. Therefore, several fault detection techniques have been proposed lately for this type of motors, based mainly on an adequate input signal processing to obtain fault signatures in the time-frequency domain. In this paper, a comparison of time-frequency techniques applied to fault detection in inverter-fed induction motors in a transient state is presented. For that purpose, the techniques are applied to two current signals acquired from two induction motors with two types of faults: bar breakage and mixed eccentricity. The paper shows the particularities and special difficulties of diagnosing under this type of feeding, reviewing the works related to each technique. The strengths and weaknesses of these techniques are discussed with the goal of providing a criterion for its application in an industrial environment and guidance for future developments in this field.This work was supported in part by the Spanish Ministerio de Economia y Competitividad 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.Fernandez-Cavero, V.; Morinigo-Sotelo, D.; Duque-Perez, O.; Pons Llinares, J. (2017). A comparison of techniques for fault detection in inverter-fed induction motors in transient regime. IEEE Access. 5:8048-8063. https://doi.org/10.1109/ACCESS.2017.2702643S80488063

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

Diagnosis of Broken Rotor Bars during the Startup of Inverter-Fed Induction Motors Using the Dragon Transform and Functional ANOVA

[EN] A proper diagnosis of the state of an induction motor is of great interest to industry given the great importance of the extended use of this motor. Presently, the use of this motor driven by a frequency converter is very widespread. However, operation by means of an inverter introduces certain difficulties for a correct diagnosis, which results in a signal with higher harmonic content and noise level, which makes it difficult to perform a correct diagnosis. To solve these problems, this article proposes the use of a time-frequency technique known as Dragon Transform together with the functional ANOVA statistical technique to carry out a proper diagnosis of the state of the motor by working directly with the curves obtained from the application of the transform. A case study is presented showing the good results obtained by applying the methodology in which the state of the rotor bars of an inverter-fed motor is diagnosed considering three failure states and operating at different load levels.This research has been partially funded by the University of Valladolid.Fernández-Cavero, V.; García-Escudero, LA.; Pons Llinares, J.; Fernández-Temprano, MA.; Duque-Perez, O.; Morinigo-Sotelo, D. (2021). Diagnosis of Broken Rotor Bars during the Startup of Inverter-Fed Induction Motors Using the Dragon Transform and Functional ANOVA. Applied Sciences. 11(9):1-12. https://doi.org/10.3390/app1109376911211

Multi-Rate Vibration Signal Analysis for Bearing Fault Detection in Induction Machines Using Supervised Learning Classifiers

Vibration signals carry important information about the health state of a ball bearing and have proven their efficiency in training machine learning models for fault diagnosis. However, the sampling rate and frequency resolution of these acquired signals play a key role in the detection analysis. Industrial organizations often seek cost-effective and qualitative measurements, while reducing sensor resolution to optimize their resource allocation. This paper compares the performance of supervised learning classifiers for the fault detection of bearing faults in induction machines using vibration signals sampled at various frequencies. Three classes of algorithms are tested: linear models, tree-based models, and neural networks. These algorithms are trained and evaluated on vibration data collected experimentally and then downsampled to various intermediate levels of sampling, from 48 kHz to 1 kHz, using a fractional downsampling method. The study highlights the trade-off between fault detection accuracy and sampling frequency. It shows that, depending on the machine learning algorithm used, better training accuracies are not systematically achieved when training with vibration signals sampled at a relatively high frequency.Peer reviewe

A Time-Frequency Analysis for Broken Rotor Bar Detection in Closed Loop Inverter Fed Induction Motor at Imposed Speed

In this paper, an analysis to detect broken rotor bars while the motor is operating in closed loop control is presented. Due to changes in the supply frequency, which occur to keep the speed constant, a time-frequency technique is required even when the transient is completed. Signals from the phase current of the induction motor are analysed using Short Time Fourier Transform. Finally, the same signals are analysed through the Dragon Transform, that ensures high resolution frequency and time and allows to detect trajectories of the harmonics with different load conditions

Multi-Fault Diagnosis in Three-Phase Induction Motors Using Data Optimization and Machine Learning Techniques

Induction motors are very robust, with low operating and maintenance costs, and are therefore widely used in industry. They are, however, not fault-free, with bearings and rotor bars accounting for about 50% of the total failures. This work presents a two-stage approach for three-phase induction motors diagnosis based on mutual information measures of the current signals, principal component analysis, and intelligent systems. In a first stage, the fault is identified, and, in a second stage, the severity of the defect is diagnosed. A case study is presented where different severities of bearing wear and bar breakage are analyzed. To test the robustness of the proposed method, voltage imbalances and load torque variations are considered. The results reveal the promising performance of the proposal with overall accuracies above 90% in all cases, and in many scenarios 100% of the cases are correctly classified. This work also evaluates different strategies for extracting the signals, showing the possibility of reducing the amount of information needed. Results show a satisfactory relation between efficiency and computational cost, with decreases in accuracy of less than 4% but reducing the amount of data by more than 90%, facilitating the efficient use of this method in embedded systems

Early Detection of Faults in Induction Motors—A Review

There is an increasing interest in improving energy efficiency and reducing operational costs of induction motors in the industry. These costs can be significantly reduced, and the efficiency of the motor can be improved if the condition of the machine is monitored regularly and if monitoring techniques are able to detect failures at an incipient stage. An early fault detection makes the elimination of costly standstills, unscheduled downtime, unplanned breakdowns, and industrial injuries possible. Furthermore, maintaining a proper motor operation by reducing incipient failures can reduce motor losses and extend its operating life. There are many review papers in which analyses of fault detection techniques in induction motors can be found. However, all these reviewed techniques can detect failures only at developed or advanced stages. To our knowledge, no review exists that assesses works able to detect failures at incipient stages. This paper presents a review of techniques and methodologies that can detect faults at early stages. The review presents an analysis of the existing techniques focusing on the following principal motor components: stator, rotor, and rolling bearings. For steady-state and transient operating modes of the motor, the methodologies are discussed and recommendations for future research in this area are also presented