Design of a labVIEW system applied to predictive maintenance

The implementation of vibration analysis techniques based on virtual instrumentation has spread increasingly in the academic and industrial branch, since the use of any software for this type of analysis brings good results at low cost. Among the existing software for programming and creation of virtual instruments, the LabVIEW was chosen for this project. This software has good interface with the method of graphical programming. In this project, it was developed a system of rotating machine condition monitoring. This monitoring system is applied in a test stand, simulating large scale applications, such as in hydroelectric, nuclear and oil exploration companies. It was initially used a test stand, where an instrumentation for data acquisition was inserted, composed of accelerometers and inductive proximity sensors. The data collection system was structured on the basis of an NI 6008 A/D converter of National Instruments. An electronic circuit command was developed through the A/D converter for a remote firing of the test stand. The equipment monitoring is performed through the data collected from the sensors. The vibration signals collected by accelerometers are processed in the time domain and frequency. Also, proximity probes were used for the axis orbit evaluation and an inductive sensor for the rotation and trigger measurement. © (2013) Trans Tech Publications, Switzerland.

Predictive maintenance of a worm reducer with an unbalanced load

Among all predictive maintenance techniques the oil analysis and vibration analysis are the most important for monitoring some mechanical systems. The integration of these techniques has potential to improve industrial maintenance practices and provide a better economic gain for industries. To study the integration of these two techniques, a test rig was set up to obtain an extreme working condition for the worm reducer used in this paper. The test rig was composed by a motor connected to a reducer through a flexible coupling and with an unbalanced load. The analysis of the results carried out by using a sample of the oil recommended by the manufacturer in extreme conditions, and using liquid contaminant is presented. From the results it was observed that if there is an abnormal instantaneous load in a system, the subsequent vibration analysis may not perceive what occurred if there was no permanent damage, which is not the case with the lubricant analysis.

Maintenance of a reducer by vibration and wear particles analysis

Purpose - The purpose of the paper is to provide information on wear debris on oil and vibration analysis as predictive maintenance techniques in reducer. Design/methodology/approach - The estate of a reducer is verified by analyzing the vibration and oil conditions of a test rig under well-designed conditions utilizing some predictive variables. Findings - According to the vibration and oil analysis it is found out what it was happening into the reducer without disassembling it. Practical implications - This paper demonstrates the use of oil debris analysis and vibration analysis as a technique that enhances preventive maintenance practices. The paper helps practitioners to utilize these techniques more effectively. Originality/value - This paper gives information about two predictive maintenance techniques with a test rig. © Emerald Group Publishing Limited.

Identification of structural damage in flexible structures using system norm and neural networks

Nowadays there is great interest in damage identification using non destructive tests. Predictive maintenance is one of the most important techniques that are based on analysis of vibrations and it consists basically of monitoring the condition of structures or machines. A complete procedure should be able to detect the damage, to foresee the probable time of occurrence and to diagnosis the type of fault in order to plan the maintenance operation in a convenient form and occasion. In practical problems, it is frequent the necessity of getting the solution of non linear equations. These processes have been studied for a long time due to its great utility. Among the methods, there are different approaches, as for instance numerical methods (classic), intelligent methods (artificial neural networks), evolutions methods (genetic algorithms), and others. The characterization of damages, for better agreement, can be classified by levels. A new one uses seven levels of classification: detect the existence of the damage; detect and locate the damage; detect, locate and quantify the damages; predict the equipment's working life; auto-diagnoses; control for auto structural repair; and system of simultaneous control and monitoring. The neural networks are computational models or systems for information processing that, in a general way, can be thought as a device black box that accepts an input and produces an output. Artificial neural nets (ANN) are based on the biological neural nets and possess habilities for identification of functions and classification of standards. In this paper a methodology for structural damages location is presented. This procedure can be divided on two phases. The first one uses norms of systems to localize the damage positions. The second one uses ANN to quantify the severity of the damage. The paper concludes with a numerical application in a beam like structure with five cases of structural damages with different levels of severities. The results show the applicability of the presented methodology. A great advantage is the possibility of to apply this approach for identification of simultaneous damages.

Vibration and wear particles analysis in a test stand

Purpose - This paper aims to provide information on wear debris on oil and vibration analysis as predictive maintenance techniques. Design/methodology/ approach - The estate of a reducer is verified by analyzing the vibration and oil conditions of a test rig under some variables. Findings - According to the vibration and oil analysis it was found what was happening in the reducer without disassembling it. Originality/value - This paper gives information about two predictive maintenance techniques with a test rig. © Emerald Group Publishing Limited.

A mechatronic system applied to wear particle analysis

This paper describes the development of a mechatronic system for a predictive maintenance grounded on wear particle analysis. The reckoning of wear particles containing in lubricating industrial oils brings the image acquisition system into being. The ISO 4406:1999 standard is a guide to establish the counting and evaluation processes of particles. The system applied to the acquisition and analysis of the data consists of a digital camera, a monocular microscope and an oil filtering system. A computational program was developed with the application of Visual Microsoft C++ in a way to detain the oil sample image from the microscope slide to the computer screen. Quantitative analyses of the wear debris particles bulk are exploited applying a graphical interface that was developed to render the image processing of the sample test. The implemented system has a reachable cost thus it can be applied for schooling goals and for bolstering laboratories of minor industries and medium size companies.

Development of an instrumentation system embedded on FPGA for real time measurement of mechanical vibrations in rotating machinery

The real-time monitoring of events in an industrial plant is vital, to monitor the actual conditions of operation of the machinery responsible for the manufacturing process. A predictive maintenance program includes condition monitoring of the rotating machinery, to anticipate possible conditions of failure. To increase the operational reliability it is thus necessary an efficient tool to analyze and monitor the equipments, in real-time, and enabling the detection of e.g. incipient faults in bearings. To fulfill these requirements some innovations have become frequent, namely the inclusion of vibration sensors or stator current sensors. These innovations enable the development of new design methodologies that take into account the ease of future modifications, upgrades, and replacement of the monitored machine, as well as expansion of the monitoring system. This paper presents the development, implementation and testing of an instrument for vibration monitoring, as a possible solution to embed in industrial environment. The digital control system is based on an FPGA, and its configuration with an open hardware design tool is described. Special focus is given to the area of fault detection in rolling bearings. © 2012 IEEE.

Análise da influência da contaminação do lubrificante por biodiesel em um equipamento Pin-on-disk

Pós-graduação em Engenharia Mecânica - FEIS

Manutenção preditiva de motores de combustão interna, à gasolina, através da técnica de análise de lubrificantes

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Manutenção de redutores de velocidade pela integração das técnicas preditivas de análise de vibrações e análise de óleo lubrificante

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Diagnóstico automático de falhas em grupos geradores hidroelétricos utilizando técnicas preditivas de manutenção e redes neurais artificiais

Pós-graduação em Engenharia Mecânica - FEIS

Monitoramento e avaliação da condição de um sistema propulsor aeronaútico através de técnicas de análise de partículas em óleos lubrificantes

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Implementação de técnicas de processamento de sinais para o monitoramento da condição de mancais de rolamento

Pós-graduação em Engenharia Mecânica - FEG

Desenvolvimento de um sistema de instrumentação de medição de vibração mecânica em máquina rotativas, em tempo real, embarcado em FPGA

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Análise e extração de características de imagens termográficas utilizando componentes principais

Pós-graduação em Engenharia Mecânica - FEIS