Mecatrónica Módulo 8: Mantenimiento y diagnóstico - PDF

Mecatrónica Módulo 8: Mantenimiento y diagnóstico Libro de Texto (Concepto) Jerzy Jędrzejewski Universidad Técnica de Wroclaw, Polonia Proyecto ampliado de transferencia del concepto europeo para la calificación

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Mecatrónica Módulo 8: Mantenimiento y diagnóstico Libro de Texto (Concepto) Jerzy Jędrzejewski Universidad Técnica de Wroclaw, Polonia Proyecto ampliado de transferencia del concepto europeo para la calificación agregada de la Mecatrónica las fuerzas especializadas en la producción industrial globalizada Proyecto EU Nr MINOS, Plazo: 2005 hasta 2007 Proyecto EU Nr. DE/08/LLP-LdV/TOI/ MINOS**, Plazo: 2008 hasta 2010 El presente proyecto ha sido financiado con el apoyo de la Comisión Europea. Esta publicación (comunicación) es responsabilidad exclusiva de su autor. La Comisión no es responsable del uso que pueda hacerse da la información aquí difundida. Colaboradores en la elaboración y aprobación del concepto conjunto de eseñanza: Technische Universität Chemnitz, Institut für Werkzeugmaschinen und Produktionsprozesse, Deutschland Projektleitung Corvinus Universität Budapest, Institut für Informationstechnologien, Ungarn Universität Stockholm, Institut für Soziologie, Schweden Technische Universität Wroclaw, Institut für Produktionstechnik und Automatisierung, Polen Henschke Consulting Dresden, Deutschland Christian Stöhr Unternehmensberatung, Deutschland Neugebauer und Partner OHG Dresden, Deutschland Korff Isomatic sp.z.o.o. Wroclaw, Polen Euroregionale Industrie- und Handelskammer Jelenia Gora, Polen Dunaferr Metallwerke Dunajvaros, Ungarn Knorr-Bremse Kft. Kecskemet, Ungarn Nationales Institut für berufliche Bildung Budapest, Ungarn IMH, Spanien VUT Brno, Tschechische Republik CICmargune, Spanien University of Naples, Italien Unis, Tschechische Republik Blumenbecker, Tschechische Republik Tower Automotive, Italien Bildungs-Werkstatt ggmbh, Deutschland VEMAS, Deutschland Concepto conjunto de enseñanza: Libro de texto, libro de ejercicios y libro de soluciones Módulo 1-8: Fundamentos / Competencia intercultural y administración de proyectos / Técnica de fluidos / Accionamiento y mandos eléctricos / Componentes mecatrónicos / Sistemas y funciones de la mecatrónica / La puesta en marcha, seguridad y teleservicio / Mantenimiento y diagnóstico Módulo 9-12: Prototipado Rápido/ Robótica/ Migración Europea/ Interfaces Todos los módulos están disponibles en los siguientes idiomas: Alemán, Inglés, español, italiano, polaco, checo, húngaro Más Información Dr.-Ing. Andreas Hirsch Technische Universität Chemnitz Reichenhainer Straße 70, Chemnitz, Deutschland Tel: + 49(0) Fax: + 49(0) Internet: oder Mantenimiento y diagnóstico Índice 1 Objectivos y tareas para el diagnóstico y la asistencia remota 5 2 Sistema de diagnóstico 7 3 Mantenimiento y reparación del sistema de diagnóstico 12 4 Tendencias de desarrollo 16 3 1 Aims and tasks of remote diagnostics and servicing Modern machine systems are highly automate d. The contr ol systems used in the automation perform their tasks on the basis of instructions (control decisions) gene rated in microprocessor s, processors or computers. Control decisions are take n on the basis of signals from sensors located in executin g mechatronic system components, supplying information about the condition of the latter and about the performance of the tasks. The inform ation is use d to infer about system operation and task (process) performance correctness and to evaluate the intensity of distur bances resulting in error s which need to be actively minimized and compensated. The contro l is conducted according to an algorithm whi ch takes int o account a ll the factor s having a bearing on the functioning of the mechatronic system and on the perfo rmance of the processes. In many cases cont rol functions are carried out intelligently using appropriate AI tools. T he diagnosing of a sin gle mechatronic system, whole machines an d processes, the supervising of the operation of mechatronic systems a nd machines and their diagnosing for service purposes can be made intelligent. Malfunctions of and damage to ma chines during their operation result in high costs of production delays, standstills an d repairs for the users. Therefore it has become necessary to continuou sly monitor machines and processes, forecast disturbances, take measures preventing process quality deterioration and take necessary remedial actio ns based on the forecasts. Such monitoring is more and more often remote and decisions are taken remotely. Eve n service functions are performed remotely. In man y cases it is nece ssary to monitor and service remotely since only the manufacturers of mechatronic modules and systems have the require d knowledg e to ident ify nonstandard disturbances and their effects and to take service decisions. The task of remote diagnostics is to wirelessly t ransmit (for a short o r considerable distance) diagnostic signals with the require d informational conte nt from the diagnosed object to a near or far receiver, a monitoring station or a monitoring centre. A proper inference system, an intelligent advisory system or an expert will assess the disturbances and will take appropriate service decision s, remotely gene rating forecasts, evaluating the deviations and identifying the degradation of the operating parameters with a required accuracy and probability. The diagnosing system s response are diagnostic inf erences which are th e basis for taking service decisions. The tasks of a remote servicing system include: - preventing excessive deterioration of mechatronic system (machine and equipment) operating parameters by reducing disturb ances and compensating errors; - predicting excessive errors and defects befor e they occur, whereby remedial action can be taken in a planned and prepared way to keep adverse economic consequences to minimum (intelligent action); - optimum planning of service tasks for operating periods most convenient to the user. 5 A revolution in remote diagnostics was the de velopment of wireless supply of sensors and wireless reception of their diagnostic signa ls, whereby the measuring systems could be miniaturized, measurements could be improved and the structure of objects could be penetrated by means of sensors to satisfy diagnostic needs. The connection of sensors to communication networks has resulted in almost limitless possibilities of co ntrolling the d iagnosis process using not only single sensors but also groups of sensors. As a re sult, information from sensors ca n be used by control, diagnostic and forecasting systems. This is of great significance for th e diagnosis of mechatronic system components and modules. 6 2 Idea, structure and operation of diagnostic system Diagnostics of machines ensures th eir precise and reliable operation. The more complex a machine, its mechatronic system an d the con - ducted technological processes are, the larger the number of various disturbances which nee d to be periodi cally or continuously monitored and the err ors they ca use reduced. The high er the precision require d of machines (diagnosed objects), the greater the precision and reliability of identif ication ( i.e. the greater the precisio n of the se nsors, the processing of diagnostic signals and transmitting them to a monitor, a control system and a diagnostic or service centre) must be. Thus the design or choice of a proper diagnostic system, software and hardware requires extensive knowledge of machine building, the processes involved, the theory and practice o f diagnosis and all d iagnostic system components. Diagnostic complexity a nd precision depends on the effect which the diagnosed parameters of machines have on the latter s work processes. Typical malfunct ion percentages for a selected machining centre are shown in table 1 and typical quantities to be monitored are presented in Fig. 1. Malfunction location Share [%] Conveying and feeding objects 20.1 DNC system 18.2 Retooling mechanism 14.6 Tool length setting 14.1 Machine tool mechanical assemblies 12.1 Tool damage 6.8 Workpiece clamping 2.6 Fine-tuning control 1.7 Feeding coolant 1.7 Clamping palettes 1.1 NC system 0.9 Problems with chips 0.9 Hydraulics 0.9 Other malfunctions 4.3 Table 1: Malfunction percentages for machining centres 7 Full diagnostics of such a complex object as an operating machine tool is very difficult and co stly. Sensors f or continuous or period ic monitoring must be permanently installed within the machine tool structure, which is hig hly expensive. The sensors are co nnected by wires and sometimes wirelessly (using proper communication standard) to signal processing circuits. The signals must be explicit, i.e. they should precisely infor m about any changes in the monitored qua ntities and should not be subject to any interference during their tran smission to processing circuits. Th e processe d signal is then used in inference which, in a simple case, consists in evaluating t he measured quantity against the value proper for the monitored para meter. The result of inference is t he basis for the formulation of diag nostic conclusions. For complex ph enomena a nd object b ehaviours many diagn ostic signals must be simultaneously evaluated. Such an inf erence process can be highly comp lex and req uire very complex proc edures and algorithms and sometimes artificia l intelligence tools: fu zzy logic, artificial neural networks and expert systems. Also the efficiency of the communic ation system, especially when the diagnosed quantities are critical for system operation reliability (require a quick response), is important. T he further away fro m the signal source the sensor is, the greater the danger that the monitoring system sensitivity may be not high enough and the response time t oo long. In such cases it may become necessary to e mploy measurement amplifiers integrat ed with the sensors, d igital filter s a nd proper signal processing. In this way one can greatly increase the measurement resolution. Tool wear Tool chipping Machine force Air temperature and humidity Machine tool temperature Workpiece temperature and geometry Palette clamping force Presence of unmachined workpiece Geometry of unmachined workpiece Tool temperature Geometry deformation Guard closing Vibrations Main drive load Feed drive load Supply voltage Rotation speed Ball screw forces Spindle torgue Oil feed Oil pressure Oil temperature Air pressure Acoustic emission Clamping pressure Feed force Positioning accuracy Axies and connectors Palette clamping force Fig.1: Typical machining centre quantities requiring monitoring 8 The input data for object diagnostics are: - diagnostic signal properties and a cquisition points (sensor locations, the rate of changes and availability for service), - the boundary values of controlled quantities, - dependencies between the generated signal and the disturbances in the performance of an object or a process, - sensors a nd measuring instruments (sensit ivity, complexity, adaptability, numerousness, cost, the degree of automation), - the form of acquired information, - the methods of processing signals, - verification methods, - the method of communicating with receivers, - the strategy of diagnosis, - inference methods. In order to reduce the number of sensors an d the complexity of the signal processing system one should use such sensor s which ca n supply much information about the behaviour of an object. Measurement paths can be much simplified and diagnostic information more easily acquired if intelligent converters are used. The structure of an intelligen t force converter is shown in Fig. 2. These are usually small-sized units made as MEMS (Micro-Electro-Mechanical Systems) microstructures, which include a sensor with a matching digital amplifier and a microprocessor with sto red knowledge for intelligent signal processing. DIN Force sensor 1 Force sensor 2 Pressure sensor Sampling-storing system a/d converter RS 485 RS 232 Microprocessor Inputs Outputs, Alarm PC Temp. sensor a/c Fig. 2: Structure of intelligent force converter 9 The criteria for designing diagnostics are: - diagnostic signal sensitivity to changes in machine/process performance and information capacity, - the degree of machine/process degradation, - the level of service personnel qualifications, - reliability, - operating costs. A typical unit for diagno sing mechanical objects consists of the following assemblies and components: 1. A measuring system (sensors, mat ching systems respo nsible for energy a nd information matching of signals, diagnostic sockets for retrieving information from the object). 2. Instrumentation amplifiers, a/d conve rters, channel selectors, I/O ports and other. 3. A digital sig nal processor (used for calculating diagnostic symptoms). 4. A decision system (incorporating logic converters, voltage level translators, digital comparators and other). 5. An informat ion display system whi ch decodes information and presents it in the form most convenient for t he user (monitor, printer, analogue indicators, digital indicators and other). 6. An informa tion storag e system (memory: RAM, RAM-DISK, VDISK). 7. Software (operating system, signal processing and analysis, state diagnosis a nd predictio n, functions performed by the diag nostic unit, communication b etween system layers, system ope ration management). 10 A block diagram of the diagnostic unit is shown in Fig. 3. DIAGNOSTIC UNIT Components Diagnostic signal sensors Diagnostic system Diagnostic sockets Processor Multichannel diagnostic signal converter System bus RAM, RAM-DISK, VDISK Keyboard Monitor Printer Software Fig. 3: Block diagram of microprocessor diagnostic unit 11
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