Estimation of bearing fault severity in line-connected and inverter-fed three-phase induction motors

Producción CientíficaThis paper addresses a comprehensive evaluation of a bearing fault evolution and its consequent prediction concerning the remaining useful life. The proper prediction of bearing faults in their early stage is a crucial factor for predictive maintenance and mainly for the production management schedule. The detection and estimation of the progressive evolution of a bearing fault are performed by monitoring the amplitude of the current signals at the time domain. Data gathered from line-fed and inverter-fed three-phase induction motors were used to validate the proposed approach. To assess classification accuracy and fault estimation, the models described in this paper are investigated by using Artificial Neural Networks models. The paper also provides process flowcharts and classification tables to present the prognostic models used to estimate the remaining useful life of a defective bearing. Experimental results confirmed the method robustness and provide an accurate diagnosis regardless of the bearing fault stage, motor speed, load level, and type of supply.CAPES (process BEX552269/2011-5)National Council for Scientific and Technological Development (grant #474290/2008-3, #473576/2011-2, #552269/2011-5, #307220/2016-8

Sequential fault detection for sealed deep groove ball bearings of in-wheel motor in variable operating conditions

Sealed deep groove ball bearings (SDGBBs) are employed to perform the relevant duties of in-wheel motor. However, the unique construction and complex operating environment of in-wheel motor may aggravate the occurrence of SDGBB faults. Therefore, this study presents a new intelligent diagnosis method for detecting SDGBB faults of in-wheel motor. The method is constructed on the basis of optimal composition of symptom parameters (SPOC) and support vector machines (SVMs). SPOC, as the objects of a follow-on process, is proposed to obtain from symptom parameters (SPs) of multi-direction. Moreover, the optimal hyper-plane of two states is automatically obtained using soft margin SVM and SPOC, and then using multi-SVMs, the system of intelligent diagnosis is built to detect many faults and identify fault types. The experiment results confirmed that the proposed method can excellently perform fault detection and fault-type identification for the SDGBB of in-wheel motor in variable operating conditions

Sensors Fault Diagnosis Trends and Applications

Fault diagnosis has always been a concern for industry. In general, diagnosis in complex systems requires the acquisition of information from sensors and the processing and extracting of required features for the classification or identification of faults. Therefore, fault diagnosis of sensors is clearly important as faulty information from a sensor may lead to misleading conclusions about the whole system. As engineering systems grow in size and complexity, it becomes more and more important to diagnose faulty behavior before it can lead to total failure. In the light of above issues, this book is dedicated to trends and applications in modern-sensor fault diagnosis

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

Higher-order spectral analysis of stray flux signals for faults detection in induction motors

[EN] This work is a review of current trends in the stray flux signal processing techniques applied to the diagnosis of electrical machines. Initially, a review of the most commonly used standard methods is performed in the diagnosis of failures in induction machines and using stray flux; and then specifically it is treated and performed the algorithms based on statistical analysis using cumulants and polyspectra. In addition, the theoretical foundations of the analyzed algorithms and examples applications are shown from the practical point of view where the benefits that processing can have using HOSA and its relationship with stray flux signal analysis, are illustrated.This work has been supported by Generalitat Valenciana, Conselleria d'Educació, Cultura i Esport in the framework of the "Programa para la promoción de la investigación científica, el desarrollo tecnológico y la innovación en la Comunitat Valenciana", Subvenciones para grupos de investigación consolidables (ref: AICO/2019/224). J. Alberto Conejero is also partially supported by MEC Project MTM2016-75963-P.Iglesias Martínez, ME.; Antonino Daviu, JA.; Fernández De Córdoba, P.; Conejero, JA. (2020). Higher-order spectral analysis of stray flux signals for faults detection in induction motors. Applied Mathematics and Nonlinear Sciences. 5(2):1-14. https://doi.org/10.2478/amns.2020.1.00032S11452H. Akçay and E. Germen. 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Fpga-based broken bars detection on induction motors under different load using motor current signature analysis and mathematical morphology. IEEE Transactions on Instrumentation and Measurement, 63(5):1032–1040, 2013.P. A. Delgado-Arredondo, D. Morinigo-Sotelo, R. A. Osornio-Rios, J. G. Avina-Cervantes, H. Rostro-Gonzalez, and R. de Jesus Romero-Troncoso. Methodology for fault detection in induction motors via sound and vibration signals. Mechanical Systems and Signal Processing, 83:568–589, 2017.M. Drif and A. J. M. Cardoso. Stator fault diagnostics in squirrel cage three-phase induction motor drives using the instantaneous active and reactive power signature analyses. IEEE Transactions on Industrial Informatics, 10(2):1348–1360, 2014.L. Frosini, C. Harlişca, and L. Szabó. Induction machine bearing fault detection by means of statistical processing of the stray flux measurement. IEEE Transactions on Industrial Electronics, 62(3):1846–1854, 2014.Z. Gao, C. Cecati, and S. X. Ding. A survey of fault diagnosis and fault-tolerant techniques—part i: Fault diagnosis with model-based and signal-based approaches. IEEE Transactions on Industrial Electronics, 62(6):3757–3767, 2015.M. Geethanjali and H. Ramadoss. Fault diagnosis of induction motors using motor current signature analysis: A review. In Advanced Condition Monitoring and Fault Diagnosis of Electric Machines, pages 1–37. IGI Global, 2019.T. Ghanbari and A. Farjah. A magnetic leakage flux-based approach for fault diagnosis in electrical machines. IEEE Sensors Journal, 14(9):2981–2988, 2014.A. Glowacz. Acoustic based fault diagnosis of three-phase induction motor. Applied Acoustics, 137:82–89, 2018.A. Glowacz, W. Glowacz, Z. Glowacz, and J. Kozik. Early fault diagnosis of bearing and stator faults of the single-phase induction motor using acoustic signals. Measurement, 113:1–9, 2018.T. Goktas, M. Zafarani, K. W. Lee, B. Akin, and T. Sculley. 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Non invasive sensors for monitoring the efficiency of ac electrical rotating machines. Sensors, 10(8):7874–7895, 2010

Predictive maintenance of induction motors using ultra-low power wireless sensors and compressed recurrent neural networks

In real-world applications - to minimize the impact of failures - machinery is often monitored by various sensors. Their role comes down to acquiring data and sending it to a more powerful entity, such as an embedded computer or cloud server. There have been attempts to reduce the computational effort related to data processing in order to use edge computing for predictive maintenance. The aim of this paper is to push the boundaries even further by proposing a novel architecture, in which processing is moved to the sensors themselves thanks to decrease of computational complexity given by the usage of compressed recurrent neural networks. A sensor processes data locally, and then wirelessly sends only a single packet with the probability that the machine is working incorrectly. We show that local processing of the data on ultra-low power wireless sensors gives comparable outcomes in terms of accuracy but much better results in terms of energy consumption that transferring of the raw data. The proposed ultra-low power hardware and firmware architecture makes it possible to use sensors powered by harvested energy while maintaining high confidentiality levels of the failure prediction previously offered by more powerful mains-powered computational platforms

An Assessment on the Non-Invasive Methods for Condition Monitoring of Induction Motors

The ability to forecast motor mechanical faults at incipient stages is vital to reducing maintenance costs, operation downtime and safety hazards. This paper synthesized the progress in the research and development in condition monitoring and fault diagnosis of induction motors. The motor condition monitoring techniques are mainly classified into two categories that are invasive and non-invasive techniques. The invasive techniques are very basic, but they have some implementation difficulties and high cost. The non-invasive methods, namely MCSA, PVA and IPA, overcome the disadvantages associated to invasive methods. This book chapter reviews the various non-invasive condition monitoring methods for diagnosis of mechanical faults in induction motor and concludes that the instantaneous power analysis (IPA) and Park vector analysis (PVA) methods are best suitable for the diagnosis of small fault signatures associated to mechanical faults. Recommendations for the future research in these areas are also presented

Linear feature selection and classification using PNN and SFAM neural networks for a nearly online diagnosis of bearing naturally progressing degradations.

International audienceIn this work, an effort is made to characterize seven bearing states depending on the energy entropy of Intrinsic Mode Functions (IMFs) resulted from the Empirical Modes Decomposition (EMD).Three run-to-failure bearing vibration signals representing different defects either degraded or different failing components (roller, inner race and outer race) with healthy state lead to seven bearing states under study. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are used for feature reduction. Then, six classification scenarios are processed via a Probabilistic Neural Network (PNN) and a Simplified Fuzzy Adaptive resonance theory Map (SFAM) neural network. In other words, the three extracted feature data bases (EMD, PCA and LDA features) are processed firstly with SFAM and secondly with a combination of PNN-SFAM. The computation of classification accuracy and scattering criterion for each scenario shows that the EMD-LDA-PNN-SFAM combination is the suitable strategy for online bearing fault diagnosis. The proposed methodology reveals better generalization capability compared to previous works and it’s validated by an online bearing fault diagnosis. The proposed strategy can be applied for the decision making of several assets

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

Diagnosis of broken bars in wind turbine squirrel cage induction generator: Approach based on current signal and generative adversarial networks

Producción CientíficaTo ensure the profitability of the wind industry, one of the most important objectives is to minimize maintenance costs. For this reason, the components of wind turbines are continuously monitored to detect any type of failure by analyzing the signals measured by the sensors included in the condition monitoring system. Most of the proposals for the detection and diagnosis of faults based on signal processing and artificial intelligence models use a fault-free signal and a signal acquired on a system in which a fault has been provoked; however, when the failures are incipient, the frequency components associated with the failures are very close to the fundamental component and there are incomplete data, the detection and diagnosis of failures is difficult. Therefore, the purpose of this research is to detect and diagnose failures of the electric generator of wind turbines in operation, using the current signal and applying generative adversarial networks to obtain synthetic data that allow for counteracting the problem of an unbalanced dataset. The proposal is useful for the detection of broken bars in squirrel cage induction generators, which, according to the control system, were in a healthy state