Fuzzy current analysis-based fault diagnostic of induction motor using hardware co-simulation with field programmable gate array

Introduction. Presently, signal analysis of stator current of induction motor has become a popular technique to assess the health state of asynchronous motor in order to avoid failures. The classical implementations of failure detection algorithms for rotating machines, based on microprogrammed sequential systems such as microprocessors and digital signal processing have shown their limitations in terms of speed and real time constraints, which requires the use of new technologies providing more efficient diagnostics such as application specific integrated circuit or field programmable gate array (FPGA). The purpose of this work is to study the contribution of the implementation of fuzzy logic on FPGA programmable logic circuits in the diagnosis of asynchronous machine failures for a phase unbalance and a missing phase faults cases. Methodology. In this work, we propose hardware architecture on FPGA of a failure detection algorithm for asynchronous machine based on fuzzy logic and motor current signal analysis by taking the RMS signal of stator current as a fault indicator signal. Results. The validation of the proposed architecture was carried out by a co-simulation hardware process between the ML402 boards equipped with a Virtex-4 FPGA circuit of the Xilinx type and Xilinx system generator under MATLAB/Simulink. Originality. The present work combined the performance of fuzzy logic techniques, the simplicity of stator current signal analysis algorithms and the execution power of ML402 FPGA board, for the fault diagnosis of induction machine achieving the best ratios speed/performance and simplicity/performance. Practical value. The emergence of this method has improved the performance of fault detection for asynchronous machine, especially in terms of hardware resource consumption, real-time online detection and speed of detection.Вступ. В даний час аналіз сигналу струму статора асинхронного двигуна став популярним методом оцінки стану працездатності асинхронного двигуна, щоб уникнути відмов. Класичні реалізації алгоритмів виявлення несправностей машин, що обертаються, засновані на мікропрограмних послідовних системах, таких як мікропроцесори і цифрова обробка сигналів, показали свої обмеження з точки зору швидкості та обмежень у реальному часі, що вимагає використання нових технологій, що забезпечують більш ефективну діагностику. наприклад, інтегральна схема для конкретної програми або програмована вентильна матриця (FPGA). Метою даної є дослідження внеску реалізації нечіткої логіки на програмованих логічних схемах FPGA в діагностику відмов асинхронних машин при несиметрії фаз і обривах фази. Методологія. У цій роботі ми пропонуємо апаратну архітектуру на FPGA алгоритму виявлення відмов асинхронної машини на основі нечіткої логіки та аналізу сигналів струму двигуна, приймаючи середньоквадратичний сигнал статора струму як сигнал індикатора несправності. Результати. Валідація запропонованої архітектури проводилася шляхом апаратного моделювання між платами ML402, оснащеними схемою Virtex-4 FPGA типу Xilinx та генератором системи Xilinx під керуванням MATLAB/Simulink. Оригінальність. Дана робота поєднала в собі ефективність методів нечіткої логіки, простоту алгоритмів аналізу сигналів струму статора та виконавчу потужність плати ML402 FPGA для діагностики несправностей асинхронних машин, досягаючи найкращих співвідношень швидкість/продуктивність та простота/продуктивність. Практична цінність. Поява цього методу покращила продуктивність виявлення несправностей асинхронної машини, особливо з точки зору споживання апаратних ресурсів, онлайн-виявлення в реальному часі та швидкості виявлення

Health Condition Monitoring of Induction Motors

Induction motors (IMs) are commonly used in various industrial applications. A spectrum synch (SS) technique is proposed in this chapter for early IM defect detection using electric current signals; fault detection in this work will focus on defects in rolling element bearings and rotor bars, which together account for more than half of IM imperfections. In bearing fault detection, the proposed SS technique will highlight the peakedness of the fault frequency components distributed over several fault related local bands. These bands are synchronized to form a fault information spectrum to accentuate fault features. A central kurtosis indicator is proposed to extract representative features from the fault information spectrum and formulate a fault index for incipient IM fault diagnosis. The effectiveness of the developed SS technique is tested on IMs with broken rotor bars and with damaged bearings

Advanced signal processing methods for condition monitoring

Condition monitoring of induction motors (IM) among with the predictive maintenance concept are currently among the most promising research topics of manufacturing industry. Production efficiency is an important parameter of every manufacturing plant since it directly influences the final price of products. This research article presents a comprehensive overview of conditional monitoring techniques, along with classification techniques and advanced signal processing techniques. Compared methods are either based on measurement of electrical quantities or nonelectrical quantities that are processed by advanced signal processing techniques. This article briefly compares individual techniques and summarize results achieved by different research teams. Our own testbed is briefly introduced in the discussion section along with plans for future dataset creation. According to the comparison, Wavelet Transform (WT) along with Empirical Mode Decomposition (EMD), Principal Component Analysis (PCA) and Park's Vector Approach (PVA) provides the most interesting results for real deployment and could be used for future experiments.Web of Scienc

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

On the application of intelligent systems for fault diagnosis in induction motors

Muitas pesquisas estão sendo desenvolvidas buscando nos sistemas inteligentes soluções para diagnosticar falhas em máquinas elétricas. Estas falhas envolvem desde problemas elétricos, como curto-circuito numa das fases do estator, ate problemas mecânicos, como danos nos rolamentos. Dentre os sistemas inteligentes aplicados nesta área, destacam-se as redes neurais artificiais, os sistemas fuzzy, os algoritmos genéticos e os sistemas híbridos, como o neuro-fuzzy. Assim, o objetivo deste artigo é traçar um panorama geral sobre os trabalhos mais relevantes que se beneficiaram dos sistemas inteligentes nas diferentes etapas de análise e diagnóstico de falhas em motores elétricos, cuja principal contribuição está em disponibilizar diversos aspectos técnicos a fim de direcionar futuros trabalhos nesta área de aplicação

Artificial Intelligence-based Technique for Fault Detection and Diagnosis of EV Motors: A Review

The motor drive system plays a significant role in the safety of electric vehicles as a bridge for power transmission. Meanwhile, to enhance the efficiency and stability of the drive system, more and more studies based on AI technology are devoted to the fault detection and diagnosis of the motor drive system. This paper reviews the application of AI techniques in motor fault detection and diagnosis in recent years. AI-based FDD is divided into two main steps: feature extraction and fault classification. The application of different signal processing methods in feature extraction is discussed. In particular, the application of traditional machine learning and deep learning algorithms for fault classification is presented in detail. In addition, the characteristics of all techniques reviewed are summarized. Finally, the latest developments, research gaps and future challenges in fault monitoring and diagnosis of motor faults are discussed

Mutual information and meta-heuristic classifiers applied to bearing fault diagnosis in three-phase induction motors

Producción CientíficaThree-phase induction motors are extensively used in industrial processes due to their robustness, adaptability to different operating conditions, and low operation and maintenance costs. Induction motor fault diagnosis has received special attention from industry since it can reduce process losses and ensure the reliable operation of industrial systems. Therefore, this paper presents a study on the use of meta-heuristic tools in the diagnosis of bearing failures in induction motors. The extraction of the fault characteristics is performed based on mutual information measurements between the stator current signals in the time domain. Then, the Artificial Bee Colony algorithm is used to select the relevant mutual information values and optimize the pattern classifier input data. To evaluate the classification accuracy under various levels of failure severity, the performance of two different pattern classifiers was compared: The C4.5 decision tree and the multi-layer artificial perceptron neural networks. The experimental results confirm the effectiveness of the proposed approach.Consejo Nacional de Desarrollo Científico y Tecnológico - (processes 474290/2008-5, 473576/2011-2, 552269/2011-5, 201902/2015-0 and 405228/2016-3

Acoustic Condition Monitoring & Fault Diagnostics for Industrial Systems

Condition monitoring and fault diagnostics for industrial systems is required for cost reduction, maintenance scheduling, and reducing system failures. Catastrophic failure usually causes significant damage and may cause injury or fatality, making early and accurate fault diagnostics of paramount importance. Existing diagnostics can be improved by augmenting or replacing with acoustic measurements, which have proven advantages over more traditional vibration measurements including, earlier detection of emerging faults, increased diagnostic accuracy, remote sensors and easier setup and operation. However, industry adoption of acoustics remains in relative infancy due to vested confidence and reliance on existing measurement and, perceived difficulties with noise contamination and diagnostic accuracy. Researched acoustic monitoring examples typically employ specialist surface-mount transducers, signal amplification, and complex feature extraction and machine learning algorithms, focusing on noise rejection and fault classification. Usually, techniques are fine-tuned to maximise diagnostic performance for the given problem. The majority investigate mechanical fault modes, particularly Roller Element Bearings (REBs), owing to the mechanical impacts producing detectable acoustic waves. The first contribution of this project is a suitability study into the use of low-cost consumer-grade acoustic sensors for fault diagnostics of six different REB health conditions, comparing against vibration measurements. Experimental results demonstrate superior acoustic performance throughout but particularly at lower rotational speed and axial load. Additionally, inaccuracies caused by dynamic operational parameters (speed in this case), are minimised by novel multi-Support Vector Machine training. The project then expands on existing work to encompass diagnostics for a previously unreported electrical fault mode present on a Brush-Less Direct Current motor drive system. Commonly studied electrical faults, such as a broken rotor bar or squirrel cage, result from mechanical component damage artificially seeded and not spontaneous. Here, electrical fault modes are differentiated as faults caused by issues with the power supply, control system or software (not requiring mechanical damage or triggering intervention). An example studied here is a transient current instability, generated by non-linear interaction of the motor electrical parameters, parasitic components and digital controller realisation. Experimental trials successfully demonstrate real-time feature extraction and further validate consumer-grade sensors for industrial system diagnostics. Moreover, this marks the first known diagnosis of an electrically-seeded fault mode as defined in this work. Finally, approaching an industry-ready diagnostic system, the newly released PYNQ-Z2 Field Programmable Gate Array is used to implement the first known instance of multiple feature extraction algorithms that operate concurrently in continuous real-time. A proposed deep-learning algorithm can analyse the features to determine the optimum feature extraction combination for ongoing continuous monitoring. The proposed black-box, all-in-one solution, is capable of accurate unsupervised diagnostics on almost any application, maintaining excellent diagnostic performance. This marks a major leap forward from fine-tuned feature extraction performed offline for artificially seeded mechanical defects to multiple real-time feature extraction demonstrated on a spontaneous electrical fault mode with a versatile and adaptable system that is low-cost, readily available, with simple setup and operation. The presented concept represents an industry-ready all-in-one acoustic diagnostic solution, that is hoped to increase adoption of acoustic methods, greatly improving diagnostics and minimising catastrophic failures

Low-Cost Diagnosis of Rotor Asymmetries in Induction Machines Working at a Very Low Slip Using the Reduced Envelope of the Stator Current

(c) 2015 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] Fault diagnosis of rotor asymmetries in induction machines working at a very low slip, through Fourier-based methods,usually requires a long acquisition time to achieve a high spectral resolution and a high sampling frequency to reduce aliasing effects. However, this approach generates a huge amount of data, which makes its implementation difficult using embedded devices with small internal memory, such as digital signal processors and field programmable gate arrays or devices with low computing power. In this paper, a new simplified diagnostic signal designated as the reduced envelope of the stator current is introduced to address this problem. The reduced envelope signal is built using only one sample of the current per cycle without any further processing, and it is demonstrated that it carries the same spectral information about the fault as the full-length current signal. Based on this approach, an embedded device has only to store and process a minimal set of samples compared with the raw current signal for a desired resolution. In this paper, the theoretical basis of the proposed method is presented, as well as its experimental validation using two different motors with broken bars: 1) a high-power induction motor working in a factory; and 2) a low-power induction motor mounted in a laboratory test bed.This work was supported by the Spanish "Ministerio de Economia y Competitividad" in the framework of the "Programa Estatal de Investigacion, Desarrollo e Innovacion Orientada a los Retos de la Sociedad" under Project DPI2014-60881-R. Paper no. TEC-00762-2014.Sapena-Bano, A.; Pineda-Sanchez, M.; Puche-Panadero, R.; Martinez-Roman, J.; Kanovic, Z. (2015). Low-Cost Diagnosis of Rotor Asymmetries in Induction Machines Working at a Very Low Slip Using the Reduced Envelope of the Stator Current. IEEE Transactions on Energy Conversion. 30(4):1409-1419. doi:10.1109/TEC.2015.24452161409141930