Time-efficient fault detection and diagnosis system for analog circuits

Time-efficient fault analysis and diagnosis of analog circuits are the most important prerequisites to achieve online health monitoring of electronic equipments, which are involving continuing challenges of ultra-large-scale integration, component tolerance, limited test points but multiple faults. This work reports an FPGA (field programmable gate array)-based analog fault diagnostic system by applying two-dimensional information fusion, two-port network analysis and interval math theory. The proposed system has three advantages over traditional ones. First, it possesses high processing speed and smart circuit size as the embedded algorithms execute parallel on FPGA. Second, the hardware structure has a good compatibility with other diagnostic algorithms. Third, the equipped Ethernet interface enhances its flexibility for remote monitoring and controlling. The experimental results obtained from two realistic example circuits indicate that the proposed methodology had yielded competitive performance in both diagnosis accuracy and time-effectiveness, with about 96% accuracy while within 60 ms computational time.Peer reviewedFinal Published versio

Real-Time Fault Detection and Diagnosis System for Analog and Mixed-Signal Circuits of Acousto-Magnetic EAS Devices

© 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.The paper discusses fault diagnosis of the electronic circuit board, part of acousto-magnetic electronic article surveillance detection devices. The aim is that the end-user can run the fault diagnosis in real time using a portable FPGA-based platform so as to gain insight into the failures that have occurred.Peer reviewe

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

A Review of Fault Diagnosing Methods in Power Transmission Systems

Transient stability is important in power systems. Disturbances like faults need to be segregated to restore transient stability. A comprehensive review of fault diagnosing methods in the power transmission system is presented in this paper. Typically, voltage and current samples are deployed for analysis. Three tasks/topics; fault detection, classification, and location are presented separately to convey a more logical and comprehensive understanding of the concepts. Feature extractions, transformations with dimensionality reduction methods are discussed. Fault classification and location techniques largely use artificial intelligence (AI) and signal processing methods. After the discussion of overall methods and concepts, advancements and future aspects are discussed. Generalized strengths and weaknesses of different AI and machine learning-based algorithms are assessed. A comparison of different fault detection, classification, and location methods is also presented considering features, inputs, complexity, system used and results. This paper may serve as a guideline for the researchers to understand different methods and techniques in this field

"Going back to our roots": second generation biocomputing

Researchers in the field of biocomputing have, for many years, successfully "harvested and exploited" the natural world for inspiration in developing systems that are robust, adaptable and capable of generating novel and even "creative" solutions to human-defined problems. However, in this position paper we argue that the time has now come for a reassessment of how we exploit biology to generate new computational systems. Previous solutions (the "first generation" of biocomputing techniques), whilst reasonably effective, are crude analogues of actual biological systems. We believe that a new, inherently inter-disciplinary approach is needed for the development of the emerging "second generation" of bio-inspired methods. This new modus operandi will require much closer interaction between the engineering and life sciences communities, as well as a bidirectional flow of concepts, applications and expertise. We support our argument by examining, in this new light, three existing areas of biocomputing (genetic programming, artificial immune systems and evolvable hardware), as well as an emerging area (natural genetic engineering) which may provide useful pointers as to the way forward.Comment: Submitted to the International Journal of Unconventional Computin

FPGAs in Industrial Control Applications

The aim of this paper is to review the state-of-the-art of Field Programmable Gate Array (FPGA) technologies and their contribution to industrial control applications. Authors start by addressing various research fields which can exploit the advantages of FPGAs. The features of these devices are then presented, followed by their corresponding design tools. To illustrate the benefits of using FPGAs in the case of complex control applications, a sensorless motor controller has been treated. This controller is based on the Extended Kalman Filter. Its development has been made according to a dedicated design methodology, which is also discussed. The use of FPGAs to implement artificial intelligence-based industrial controllers is then briefly reviewed. The final section presents two short case studies of Neural Network control systems designs targeting FPGAs

Transient fault area location and fault classification for distribution systems based on wavelet transform and Adaptive Neuro-Fuzzy Inference System (ANFIS)

A novel method to locate the zone of transient faults and to classify the fault type in Power Distribution Systems using wavelet transforms and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) has been developed. It draws on advanced techniques of signal processing based on wavelet transforms, using data sampled from the main feeder current to extract important characteristics and dynamic features of the fault signal. In this method, algorithms designed for fault detection and classification based on features extracted from wavelet transforms were implemented. One of four different algorithms based on ANFIS, according to the type of fault, was then used to locate the fault zone. Studies and simulations in an EMTP-RV environment for the 25kV power distribution system of Canada were carried out by considering ten types of faults with different fault inception, fault resistance and fault locations. The simulation results showed high accuracy in classifying the type of fault and determining the fault area, so that the maximum observed error was less than 2%

FPGA Implementation of AI-Based Inverter IGBT Open Circuit Fault Diagnosis of Induction Motor Drives

In modern industrial manufacturing processes, induction motors are broadly utilized as industrial drives. Online condition monitoring and diagnosis of faults that occur inside and/or outside of the Induction Motor Drive (IMD) system makes the motor highly reliable, helping to avoid unsched-uled downtimes, which cause more revenue loss and disruption of production, thus making it as the extensively used industrial drive. This can be achieved only when the irregularities produced out of the fault circumstance are sensed at that instant itself and diagnosed as to what and where happened for suitable action by the protective equipment employed. This requires intelligent control with high performance scheme. Hence, Field Programmable Gate Array (FPGA) based Neuro-Genetic implementation with Back Propagation Neural Network (BPN) is suggested in this article to diagnose the fault more efficiently and almost instantly. It is reported that the classifica-tion of neural network will provide the output within 2 µs although the clone procedure with mi-crocontroller requires 7 ms. This intelligent control with high performance technique is applied to the IMD fed by Voltage Source Inverter (VSI) to diagnose the fault external to the induction motor occurring in the VSI supply system. The proposed approach was simulated and experimentally validated.publishedVersio

A survey on fractional order control techniques for unmanned aerial and ground vehicles

In recent years, numerous applications of science and engineering for modeling and control of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) systems based on fractional calculus have been realized. The extra fractional order derivative terms allow to optimizing the performance of the systems. The review presented in this paper focuses on the control problems of the UAVs and UGVs that have been addressed by the fractional order techniques over the last decade