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9th International Renewable Energy Congress (IREC)

作者： Sarikh, S. Raoufi, M. Bennouna, A. Benlarabi, A. Ikken, B. Cadi Ayyad Univ Marrakesh Lab Nanomat Energy & Environm Fac Sci Semlalia Marrakech Morocco IRESEN Green Energy Pk Res Platform Souissi Rabat Morocco

ISBN: (纸本)9781538609989

In the photovoltaic field, regarding the importance of sustainability, monitoring systems are a paramount component for yield assessment. Yet in the industrial production, fault detection remains a manually handled issue. However, faults are responsible for significant power loss and sometimes, even dramatically damages are reported such fire hazard or material deterioration. This paper presents a methodology for fault detection in the photovoltaic system regarding the different impacts of faults on the I-V curve. Indeed, fault classification is a crucial step for the failure diagnosis. The proposed method mainly covers uniform dust faults, partial shading faults, short circuit faults, and aging. The proposed algorithm is assessed through a simulation on Matlab Simulink and finally, it is validated using experimental I-V curves measurements in faulty cases.

关键词： experimental photovoltaic characterization I-V curve analysis photovoltaic model fault detection algorithm

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Prognostics of Onboard Electrohydraulic Servomechanisms: Proposal of a Novel Model-Based fault detection Neural Technique 2

Prognostics of Onboard Electrohydraulic Servomechanisms: Pro...

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2nd International Conference on System Reliability and Safety (ICSRS)

作者： Dalla Vedova, Matteo D. L. Cerqua, Gianluca Maggiore, Paolo Politecn Torino Dept Mech & Aerosp Engn Corso Duca Abruzzi 24 Turin Italy

ISBN: (纸本)9781538633229

The paper aims to propose a new prognostic procedure centered on the characterization of the state of health of an electrohydraulic servomechanism typically used in aircraft primary flight controls. This approach is based on the innovative use of a model based fault detection and identification method (FDI) that applies artificial neural network in order to identify the actual state of wear of the actuator. The considered case study concerns an electrohydraulic actuator with a flapper-nozzle servo-valve whose progressive faults taken into consideration are: the clogging of the first stage of the flapper-nozzle valve, and the progressive gain loss of the torque motor. By means of the extraction of data from the system responses, under different extent of damage, multiple neural networks have been trained. Such networks have been integrated to create the prognostic method that has finally been tested under different conditions.

关键词： prognostics electrohydraulic actuator servo-valve artificial neural network model-based fault detection algorithm

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A Framework of fault detection algorithm for Intelligent Vehicle by Using Real Experimental Data

A Framework of Fault Detection Algorithm for Intelligent Veh...

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14th International IEEE Conference on Intelligent Transportation Systems (ITSC)

作者： Hashimoto, Naohisa Ozguner, Umit Sawant, Neil Yokozuka, Masashi Kato, Shin Matsumoto, Osamu Tsugawa, Sadayuki Natl Inst Adv Ind Sci & Technol Tsukuba Japan Ohio State Univ Dept Elect & Comp Engn Columbus OH 43210 USA Natl Inat Adv Ind Sci & Technol Ibaraki Japan Ohio State Univ Columbus OH 43210 USA

ISBN: (纸本)9781457721977

Automated vehicles have a possibility to contribute more capacity, safety, low emission and high efficiency to transportation. However, unstable conditions of the automated system can cause serious problem, thus the automated vehicle requires high reliability. The objective of this research is to develop algorithms of fault (unstable condition) detection for automated vehicles, and to improve the overall reliability of the system. In this study, we initially solved and updated identification of some pattern of data constellations under normal and unstable conditions through the experiments in a real world. The multiple experiments were done in the public area (course distance is about 1.1[km]) with some times, where some pedestrian, bicycles and other robots coexisted. The method of detecting faults utilizes mahalanobis distance, correlation coefficient and linearization in order to improve the reliability of detecting the faults correctly, because real experimental conditions include some random noises, and the method must be robust for the changing conditions. The feature of this study is to utilize the experiment results in real world, construct the algorithms and evaluate it. The simulations were done with the real experimental data, in order to evaluate it. The simulation result shows that the proposed system detects faults correctly, and it proves the validity of the proposed method proved.

关键词： Equations fault detection Mathematical model Mobile robots Sensors Vehicles automated highways automated vehicles correlation coefficient correlation methods data constellations fault detection algorithm fault diagnosis intelligent vehicle mahalanobis dis

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Simple and Robust fault Tolerant Control for Open-Circuit in High Speed PM Machines

Simple and Robust Fault Tolerant Control for Open-Circuit in...

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International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM)

作者： Catuogno, Guillermo Di Noia, Luigi Pio Del Pizzo, Andrea Garcia, Guillermo Leidhold, Roberto Natl Univ San Luis Automat Control Lab LCA Villa Mercedes Argentina Univ Naples Federico II Dept Elect Engn & Informat Technol Naples Italy Natl Univ Rio Cuarto Appl Elect Grp GEA Rio Cuarto Argentina Univ Otto Von Guericke Inst Elect Power Syst IESY Magdeburg Germany

ISBN: (纸本)9781538649411

A fault tolerant strategy is proposed to control the power flow in High-Speed Permanent Magnet Machines (HS-PMM) with arbitrary Electromotive Force (EMF) waveform. The strategy aims to minimize ripple torque and reduce reactive power that flow on the machine. The control algorithm is based on the instantaneous reactive power theory and is implemented with a conventional three-phase four-wire converter. The focus of the work emphasizes the fault detection algorithm (FDA), which is key to the correct performance of the fault tolerant system. Simulation results are presented to the system when an open circuit fault occurs and the correct performance of the FDA and the entire system is observed.

关键词： High-Speed PMM fault detection algorithm fault Tolerance

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fault detection with CP-Decomposed Qualitative Models

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IFAC-PapersOnLine 2016年第5期49卷 309-314页

作者： Müller, Thorsten Lichtenberg, Gerwald Fraunhofer Institute for Solar Energy Systems - ISE Freiburg79110 Germany Hamburg University of Applied Sciences Hamburg21033 Germany

The paper shows how a fault detection algorithm based on stochastic automata as qualitative model can be improved by non-negative CP tensor decomposition to make it applicable to large discrete-time systems. Because exponential growth of the number of transitions of the automaton with a rising number of states, inputs and outputs of the system can usually not be avoided, tensor decomposition methods enable the reduction of the amount of data to be stored by an order of magnitude. In order to exploit the full potential of the decomposition, a fault detection algorithm that is applicable to the decomposed tensor structure is defined. An example based on real measurement data shows the functionality of the algorithm. © 2016

关键词： fault detection Automata theory Digital control systems Discrete time control systems Signal detection Stochastic models Stochastic systems Tensors Discrete time systems Exponential growth fault detection algorithm Qualitative model Real measurements Stochastic Automata Tensor decomposition Tensor structures

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detection of Inter-Turn and Dynamic Eccentricity faults Using Stator Current Frequency Pattern in IPM-Type BLDC Motors

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2016年第3期63卷 1771-1780页

作者： Park, Jun-Kyu Hur, Jin Univ Ulsan Sch Elect Engn Ulsan 44610 South Korea Incheon Natl Univ Dept Elect Engn Inchon 22012 South Korea

This paper propose a fault detection algorithm for stator inter-turn faults (SITFs), dynamic eccentricity faults (DEFs), and hybrid faults (a combination of SITF and DEF) in a permanent magnet (PM) brushless dc motor. We analyzed specific frequency patterns of the stator input current to diagnosis and distinguish the fault types. First, we developed a two-dimensional (2-D) finite element method (FEM) program using a system matrix, and then we confirmed additional specific frequency patterns of the input current by a fast Fourier transform. Finally, specific frequency patterns for each fault type were verified by experimental results.

关键词： Dynamic eccentricity fault detection algorithm frequency pattern hybrid fault inter-turn-fault interior permanent magnet (IPM)-type brushless dc (BLDC) motor stator current signature analysis stator input current

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Mixed approach for fault diagnosis and fault location of hybrid systems

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IFAC-PapersOnLine 2016年第12期49卷 1002-1007页

作者： Maaref, B. Abazi, Z. Simeu Dhouibi, H. Messaoud, H. Gascard, E. LARATSI National Engineering School of Monastir Tunisia G-SCOP CNRS Grenoble- INP/UJF- Grenoble 1 France

Today, the fault diagnosis remains a very important task due to its essential role in improving the dependability and also in minimizing the harmful consequences which can be catastrophic for equipment and human safety. This article proposes a diagnostic approach for hybrid systems through the use of Bond Graph and observers well known in continuous field, coupled to timed automata well used in the field of discrete events systems. Indeed, for a complex system, the Bond Graph allows modeling of multi-energy physical processes. The observer-based approach is used to generate dynamic models (in normal and in failing mode). The fault detection algorithm consists mainly in comparing the real measures of the system with measures estimated by an observer. Thus, the fault isolation procedure consists in analyzing the fault signature matrix. Timed automata allow to take into account the dynamic evolution of the system and the failures propagation. To refine the failure location, a diagnoser based on timed automaton is built. The proposed approach is validated by simulation tests in a hydraulic system with two tanks. © 2016

关键词： fault detection Automata theory Electric fault currents Failure analysis Graph theory Hybrid systems Hydraulic equipment Hydraulic machinery Bond graph Continuous fields Diagnostic approach Discrete events systems fault detection algorithm fault isolation Observer Timed Automata

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fault detection Analysis of Boolean Control Networks

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2015年第10期60卷 2734-2739页

作者： Fornasini, Ettore Valcher, Maria Elena Univ Padua Dipartimento Ingn Informaz I-35131 Padua Italy

In this technical note, we address the fault detection problem for Boolean control networks (BCNs). We first investigate completeness and T-completeness of the set of input/output trajectories. Next, we introduce the concept of meaningful fault, and prove necessary and sufficient conditions under which meaningful faults can be detected from the input/output trajectories of the BCN. Two fault detection algorithms are provided.

关键词： Boolean control networks completeness fault detection algorithm fault detection T-completeness

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Decoupled current control and sensor fault detection with second-order sliding mode for induction motor

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IET CONTROL THEORY AND APPLICATIONS 2015年第4期9卷 608-617页

作者： Kommuri, Suneel Kumar Rath, Jagat Jyoti Veluvolu, Kalyana Chakravarthy Defoort, Michael Soh, Yeng Chai Kyungpook Natl Univ Coll IT Engn Sch Elect Engn Daegu 702701 South Korea Univ Lille Nord France CNRS UMR 8201 LAMIH UVHC F-59313 Valenciennes France Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

This study proposes a new decoupled control method for induction motor (IM) based on higher order sliding mode (HOSM) controller. The proposed controller serves dual purpose by offering decoupled control and sensor fault detection. In this scheme, the decoupled control of d-q currents does not require the knowledge of the speed. The HOSM controllers play the same role as the compensation voltages produced by a decoupling compensator. From the compensation voltages, the speed can be estimated accurately through algebraic calculations. The estimated and actual speeds are then employed for fault detection algorithm to detect the fault. Simulations on a 1/4-hp three-phase IM in the presence of random measurement noise highlights the performance of the proposed approach for decoupling current control and robust sensor fault detection.

关键词： induction motors electric current control fault diagnosis variable structure systems machine control decoupled current control sensor fault detection second-order sliding mode induction motor higher order sliding mode controller HOSM controller fault detection algorithm robust sensor fault detection

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Wavelet based fault detection and rls parameter estimation of conductive fibers with a simultaneous estimation of time-varying disturbance

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IFAC-PapersOnLine 2015年第3期28卷 1773-1778页

作者： Schimmack, Manuel McGaw, David Mercorelli, Paolo Institute of Product and Process Innovation Leuphana University of Lueneburg Volgershall 1 LueneburgD-21339 Germany

This paper presents a method for a variable fault detection algorithm, based on wavelets, and offers the possibility to realize soft, hard and very hard fault detection. The proposed algorithm is based on the estimation of the variance of the local Lipschitz constant of the signal over a receding time horizon. The fault (outlier) is recognized if the local Lipschitz constant lies outside the computed boundary. Currently, to estimate parameters in the nano range. Thus the input signal requires a very high frequency, which subsequently requires a very high sampling rate. A modified Recursive Least Squares (RLS) method was used to estimate parameters of conductive multifilament fibers using on-line identification model during the manufacturing process, including inductance, within the nano range, using input-output scaling factors. In contrast, this technique uses a broader sampling rate and an input signal with low frequency to identify the parameters characterizing the linear model. This method was used to provide a scaled identification bandwidth together with a reduced sampling rate. Through the scaling of the input-output data, a general model of the identification technique was obtained to estimate time-varying sinusoidal disturbance signal during the manufacturing process. In our contribution a time-varying sinusoidal disturbance is considered in terms of magnitude and frequency. The identification is obtained using modified Least Squares Methods (LSM). In this kind of system, the inductance represents the most critical parameter to be estimated. The examined results indicated that the proposed RLS algorithm method, using a forgetting factor, is a useful method for estimating time-varying sinusoidal disturbances as well as the inductance. © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Parameter estimation fault detection Inductance Least squares approximations Manufacture Signal sampling Disturbance signals fault detection algorithm Identification techniques Parameters characterizing Recursive least square (RLS) Sinusoidal disturbance signals SISO Wavelets

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