Vibration Analysis for Engine fault Detection

In the Vibration analysis for engine fault detection, we use different visualization graph. Today‘s world growing fast and machinery part getting complex so it’s difficult to find out fault in the machine so here means in this paper we explain how we find out the fault of the machine with help of visualization it’s easy to find out a fault here we use angular.js, D3.js for visualization and use MQTT protocol for publishing and subscribe sensor data. In the automobile industries machines are the main part of how we find out fault yes we find out fault with help of sensors using sensors here we analyze the machine

Guest Editorial Special Section on Advanced Signal and Image Processing Techniques for Electric Machines and Drives Fault Diagnosis and Prognosis

© 2017 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] With the expansion of the use of electrical drive sys- tems to more critical applications, the issue of reliability and fault mitigation and condition-based maintenance have consequently taken an increasing importance: it has become a crucial one that cannot be neglected or dealt with in an ad-hoc way. As a result research activity has increased in this area, and new methods are used, some based on a continuation and improvement of previous accomplishments, while others are applying theory and techniques in related areas. This Special Section of the IEEE Transactions on Industrial Informatics attracted a number of papers dealing with Advanced Signal and Image Processing Techniques for Electric Machine and Drives Fault Diagnosis and Prognosis. This editorial aims to put these contributions in context, and highlight the new ideas and directions therein.Antonino-Daviu, J.; Lee, SB.; Strangas, E. (2017). Guest Editorial Special Section on Advanced Signal and Image Processing Techniques for Electric Machines and Drives Fault Diagnosis and Prognosis. IEEE Transactions on Industrial Informatics. 13(3):1257-1260. doi:10.1109/TII.2017.2690464S1257126013

Stray Flux Monitoring for Reliable Detection of Rotor Faults under the Influence of Rotor Axial Air Ducts

International audienc

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

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

A novel fault-detection methodology of proposed reduced switch MLI fed induction motor drive using discrete wavelet transforms

Induction motors are typically promoted in industrial applications by adopting energy-efficient power-electronic drive technology. Multilevel inverters (MLI) have been widely recognized in recent days for high-power, medium-voltage-efficient drives. There has been vital interest in forming novel multilevel inverters with reduced switching elements. The newly proposed reduced-switch five-level inverter topology extends with fewer switches, low dv/dt stress, high efficiency, and so on, over the formal multilevel inverter topologies. The multilevel inverter's reputation is greatly affected due to several faults on switching elements and complex switching sequences. In this paper, a novel fault identification process is evaluated in both healthy and faulty conditions using discrete-wavelet transform analysis. The discrete wavelet transform utilizes the multi-resolution analysis with a feature extraction methodology acquired for fault identification over the classical methods. A novel fault identification scheme is implemented on reduced-switch five-level MLI topology using the Matlab/Simulink platform to increase the drive system's reliability. The effectiveness of simulation outcomes is illustrated with proper comparisons. The pro posed topology's hardware model is implemented using a dSPACE DS1103 real-time digital controller and the results of the experiment are presented

Magnetic Flux Analysis for the Condition Monitoring of Electric Machines: A Review

[EN] Magnetic flux analysis is a condition monitoring technique that is drawing the interest of many researchers and motor manufacturers. The great enhancements and reduction in the costs and dimensions of the required sensors, the development of advanced signal processing techniques that are suitable for flux data analysis, along with other inherent advantages provided by this technology are relevant aspects that have allowed the proliferation of flux-based techniques. This paper reviews the most recent scientific contributions related to the development and application of flux-based methods for the monitoring of rotating electric machines. Particularly, aspects related to the main sensors used to acquire magnetic flux signals as well as the leading signal processing and classification techniques are commented. The discussion is focused on the diagnosis of different types of faults in the most common rotating electric machines used in industry, namely: squirrel cage induction machines (SCIM), wound rotor induction machines (WRIM), permanent magnet machines (PMM) and wound field synchronous machines (WFSM). A critical insight of the techniques developed in the area is provided and several open challenges are also discussed.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 Tecnologico del Sistema de I+D+i, Subprograma Estatal de Generacion de Conocimiento" reference PGC2018-095747-B-I00 and by the Consejo Nacional de Ciencia y Tecnología under CONACyT Scholarship with key code 2019-000037-02NACF. Paper no. TII-20-5308.Zamudio-Ramírez, I.; Osornio-Rios, RA.; Antonino-Daviu, J.; Razik, H.; Romero-Troncoso, RDJ. (2022). Magnetic Flux Analysis for the Condition Monitoring of Electric Machines: A Review. IEEE Transactions on Industrial Informatics. 18(5):2895-2908. https://doi.org/10.1109/TII.2021.30705812895290818

Detection of Permanent Magnet DC Motor Failure Due to Brush Wear Using Parameter Estimation and Statistical Analysis

Failure detection of DC motors is a common study, and could be extremely useful in real world applications. Undiagnosed eminent motor failure could cause a range of effects, and without maintenance will inevitably occur. Motor faults can be classified as electrical or mechanical, both with wide ranges of causes. Electrical failure includes stator or rotor winding faults, inverter faults, position of sensor faults in brushless motors, bearing faults, and brush faults. Mechanical faults include bearing faults, broken rotor bar, rotor eccentricity faults, end ring faults, and load faults. The aim of this study was to observe the effect of brush fault within a permanent magnet DC (PMDC) motor. Carbon contact brushes are used in PMDC motors to transmit electrical current from the stator of the motor to the rotor of the motor, ensuring the rotation of the commutators. Over time, the carbon contact becomes worn down from commutators continually moving across them. As the contacts length is decreased, the spring holding it in place becomes more stretched out, putting in more effort to hold the brush in place. This introduces a resistance, referred to as a contact resistance, that can affect the motor speed and performance. Changes in speed and resistance can be measured and observed, and curves can be fitted to their relationship with statistical significance. We can also create a simulation method using basic differential equations that describe the motor and introducing random noise to the simulation with generation of random numbers for the motor parameters. Finally, a confidence interval is generated, and eminent motor failure can be predicted when values measured values stray from the simulated path. Erratic motor behavior can also be observed at the point of eminent motor failure

Condition monitoring of induction motors in the nuclear power station environment

The induction motor is a highly utilised electrical machine in industry, with the nuclear industry being no exception. A typical nuclear power station usually contains more than 1000 motors, where they are used in safety and non-safety application. The efficient and fault-free operation of this machine is critical to the safe and economical operation of any plant, including nuclear power stations. A comprehensive literature review was conducted that covered the functioning of the induction machine, its common faults and methods of detecting these faults. The Condition Based Maintenance framework was introduced in which condition monitoring of induction machines is an essential component. The main condition monitoring methods were explained with the main focus being on Motor Current Signature Analysis (MCSA) and the various methods associated with it. Three analysis methods were selected for further study, namely, Current Signature Analysis, Instantaneous Power Signature Analysis (IPSA) and Motor Square Current Signature Analysis (MSCSA). Essentially, the methodology used in this dissertation was to study the three common motor faults (bearings, stator and rotor cage) in isolation and compare the results to that of the healthy motor of the same type. The test loads as well as fault severity were varied where possible to investigate its effect on the fault detection scheme. The data was processed using an FFT based algorithm programed in MATLAB. The results of the study of the three spectral analysis techniques showed that no single technique is able to detect motor faults under all tested circumstances. The MCSA technique proved the most capable of the three techniques as it was able to detect faults under most conditions, but generally suffered poor results in inverter driven motor applications. The IPSA and MSCSA techniques performed selectively when compared to MCSA and were relatively successful when detecting the mechanical faults. The fact that the former techniques produce results at unique points in the spectrum would suggest that they are more suitable for verifying results. As part of a comprehensive condition monitoring scheme, as required by a large population of the motors on a nuclear power station, the three techniques presented in this study could readily be incorporated into the Condition Based Maintenance framework where the strengths of each could be exploited