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

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

Machine learning and deep learning based methods toward Industry 4.0 predictive maintenance in induction motors: Α state of the art survey

Purpose: Developments in Industry 4.0 technologies and Artificial Intelligence (AI) have enabled data-driven manufacturing. Predictive maintenance (PdM) has therefore become the prominent approach for fault detection and diagnosis (FD/D) of induction motors (IMs). The maintenance and early FD/D of IMs are critical processes, considering that they constitute the main power source in the industrial production environment. Machine learning (ML) methods have enhanced the performance and reliability of PdM. Various deep learning (DL) based FD/D methods have emerged in recent years, providing automatic feature engineering and learning and thereby alleviating drawbacks of traditional ML based methods. This paper presents a comprehensive survey of ML and DL based FD/D methods of IMs that have emerged since 2015. An overview of the main DL architectures used for this purpose is also presented. A discussion of the recent trends is given as well as future directions for research. Design/methodology/approach: A comprehensive survey has been carried out through all available publication databases using related keywords. Classification of the reviewed works has been done according to the main ML and DL techniques and algorithms Findings: DL based PdM methods have been mainly introduced and implemented for IM fault diagnosis in recent years. Novel DL FD/D methods are based on single DL techniques as well as hybrid techniques. DL methods have also been used for signal preprocessing and moreover, have been combined with traditional ML algorithms to enhance the FD/D performance in feature engineering. Publicly available datasets have been mostly used to test the performance of the developed methods, however industrial datasets should become available as well. Multi-agent system (MAS) based PdM employing ML classifiers has been explored. Several methods have investigated multiple IM faults, however, the presence of multiple faults occurring simultaneously has rarely been investigated. Originality/value: The paper presents a comprehensive review of the recent advances in PdM of IMs based on ML and DL methods that have emerged since 2015Peer Reviewe

Offline diagnostika rotoru asynchronního motoru

Asynchronous machines are widely used in various industrial applications, and their reliable operation is essential to maintain the production process. Rotor faults are one of the most common types of faults in asynchronous machines and can lead to a significant decrease in the machine's efficiency and lifespan. Offline diagnostic methods have been developed to detect rotor faults in asynchronous machines, including vibration analysis, current signature analysis, and motor current signature analysis. This research paper presents an overview of offline diagnostic methods for detecting rotor faults in asynchronous machines, their principles, advantages, and limitations. The paper also includes case studies of rotor fault diagnosis in asynchronous machines using Finite Element Method (FEM) and FFT analysis for Maxwell 2D electromagnetic FEM model.Asynchronní stroje jsou široce používány v různých průmyslových aplikacích a jejich spolehlivý provoz je nezbytný pro zachování výrobního procesu. Poruchy rotoru jsou jedním z nejčastějších typů poruch asynchronních strojů a mohou vést k výraznému snížení účinnosti a životnosti stroje. Pro detekci poruch rotoru asynchronních strojů byly vyvinuty offline diagnostické metody, včetně analýzy vibrací, analýzy proudové signatury a analýzy proudové signatury motoru. Tento výzkumný článek uvádí přehled offline diagnostických metod pro detekci poruch rotoru asynchronních strojů, jejich principy, výhody a omezení. Článek rovněž obsahuje případové studie diagnostiky poruch rotoru asynchronních strojů pomocí metody konečných prvků (MKP) a analýzy FFT pro Maxwellův 2D elektromagnetický model MKP.410 - Katedra elektroenergetikyvelmi dobř

Advanced model of squirrel cage induction machine for broken rotor bars fault using multi indicators

Squirrel cage induction machine are the most commonly used electrical drives, but like any other machine, they are vulnerable to faults. Among the widespread failures of the induction machine there are rotor faults. This paper focuses on the detection of broken rotor bars fault using multi-indicator. However, diagnostics of asynchronous machine rotor faults can be accomplished by analysing the anomalies of machine local variable such as torque, magnetic flux, stator current and neutral voltage signature analysis. The aim of this research is to summarize the existing models and to develop new models of squirrel cage induction motors with consideration of the neutral voltage and to study the effect of broken rotor bars on the different electrical quantities such as the park currents, torque, stator currents and neutral voltage. The performance of the model was assessed by comparing the simulation and experimental results. The obtained results show the effectiveness of the model, and allow detection and diagnosis of these defects

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

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

Harmonic current sideband indicators (HCSBIs) for broken bar detection and diagnostics in cage induction motors

Induction motor bar breakages have been increasingly studied in the last decades because of economic interests in developing techniques that permit on-line, non-invasive, early detection of motor faults in power plants. This work is specifically focused on broken bar detection and fault severity assessment in three phase power cage motors fed by non-sinusoidal voltage sources. In this work some new fault indicators for rotor bar breakages detection in squirrel cage induction motors are proposed, mathematically developed and experimentally proved. They are based on the sidebands of phase current upper harmonics, and they are well suited especially for converter-fed induction motors. The ratios I(7-2s)f/I5f and I(5+2s)f/I7f , I(13-2s)f/I11f and I(11+2s)f/I13f are examples of such new indicators, and they are not dependent on load torque and drive inertia, as classical indicators do. Their frequency-dependence has been also examined both theoretically and experimentally, and it was found less remarkable with respect to other indicators. Moreover, their values increase linearly with the quantity of consecutive broken bars, almost for not too much advanced faults; on 4-poles motors they were found quietly like the per-unit number of broken bars (ratio on total bar number). An original formulation is presented for motor mathematical modeling, based on the Generalized Symmetrical Components Theory, for sidebands amplitude computation. A complete motor model (involving all the elementary machine electrical circuits, as stator belts and rotor mesh loops) has been used for computer simulations; the same model was then transformed by using some complex Fortescue’s matrices to obtain a steady-state linear solution, solvable for stator and rotor currents, in healthy and faulty conditions. By exploiting the model, the formal definition of a set of new broken bar indicators was finally obtained. Machine simulations carried out by running the complete numerical model confirmed the accuracy of the model, and the theoretical previsions. Experimental work was performed by using a square-wave inverter-fed motor with an appositely prepared cage, for easy testing with increasing number of broken bars and without motor dismounting. Moreover, extensive experimentation was carried out on three industrial motors with different power and poles number, with increasing load, frequency and fault gravity for methodology validation. Finally, the ideas exposed in this work led to a patent application, owned by the University of Rome “Sapienza”

A Study of the Impact of Squirrel-Cage Rotor Faults on the Stator Current Signature

The present paper considers the impact of the degree of damage to a squirrel-cage rotor of an induction motor on the spectrum of the stator current. The study is based on the motor current signature analysis. Performed, for this purpose, were scientific experiments on eight samples of squirrel-cage rotors, seven of which with pre-inflicted faults using Dynamic Motor Analyser. The obtained results are herein presented in graphical and tabular form and are further compared with the ones acquired from an induction motor with an intact rotor winding. It can be clearly ascertained that the larger the number of damaged rotor bars, the more significant the increase in the current amplitude as correspondent to the side band amplitude