Application of a model updating technique for health condition monitoring and damage detection of flexible structures

This paper discusses the application of a damage detection methodology to monitor the location and extent of partial structural damage. The methodology combines, in an iterative way, the model updating technique based on frequency response functions (FRF) with monitoring data aiming at identifying the damage area of the structure. After the updating procedure reaches a good correlation between the models, it compares the parameters of the damage structure with those of the undamaged one to find the deteriorated area. The influence of the FEM mesh size on the evaluation of the extent of the damage has also been discussed. The methodology is applied using real experimental data from a spatial frame structure.

Tool Condition Monitoring of Single-Point Dresser Using Acoustic Emission and Neural Networks Models

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Tool condition monitoring of aluminum oxide grinding wheel in dressing operation using acoustic emission and neural networks

The grinding operation gives workpieces their final finish, minimizing surface roughness through the interaction between the abrasive grains of a tool (grinding wheel) and the workpiece. However, excessive grinding wheel wear due to friction renders the tool unsuitable for further use, thus requiring the dressing operation to remove and/or sharpen the cutting edges of the worn grains to render them reusable. The purpose of this study was to monitor the dressing operation using the acoustic emission (AE) signal and statistics derived from this signal, classifying the grinding wheel as sharp or dull by means of artificial neural networks. An aluminum oxide wheel installed on a surface grinding machine, a signal acquisition system, and a single-point dresser were used in the experiments. Tests were performed varying overlap ratios and dressing depths. The root mean square values and two additional statistics were calculated based on the raw AE data. A multilayer perceptron neural network was used with the Levenberg-Marquardt learning algorithm, whose inputs were the aforementioned statistics. The results indicate that this method was successful in classifying the conditions of the grinding wheel in the dressing process, identifying the tool as "sharp''(with cutting capacity) or "dull''(with loss of cutting capacity), thus reducing the time and cost of the operation and minimizing excessive removal of abrasive material from the grinding wheel.

A sewage sludge co-composting process using respirometric monitoring method in hermetic rotary reactor

This paper has the objective of monitoring the biological activity of composting process of sewage sludge, sugarcane bagasse and ground coffee in a hermetic rotary reactor using the respirometric method in laboratory scale, in order to obtain parameters and system design for large scale projects. Another particularity of this study is the use of a hermetic reactor with gas purging cycles. Purging was performed when the percentage of oxygen reached less than 5%, thus eliminating the gaseous mixture (with elevated CO2 ratio) and the introduction of environmental air with around 21% of O2, successively until the compost was stabilized. The average purge intervals obtained were 29 h and 2 min with reactor rotation frequency of 15 min. The time of the compost stabilization was optimized in 60% if compared to the 90 days in the traditional method. The results obtained can be used to design the process in industrial scale using a simple O2 gas analyzer.

Speed estimation for induction motor using neural networks method

This work presents an alternative approach based on neural network method in order to estimate speed of induction motors, using the measurement of primary variables such as voltage and current. Induction motors are very common in many sectors of the industry and assume an important role in the national energy policy. The nowadays methodologies, which are used in diagnosis, condition monitoring and dimensioning of these motors, are based on measure of the speed variable. However, the direct measure of this variable compromises the system control and starting circuit of an electric machinery, reducing its robustness and increasing the implementation costs. Simulation results and experimental data are presented to validate the proposed approach. © 2003-2012 IEEE.

A synthetic control chart for monitoring the process mean and variance

Purpose - The aim of this paper is to present a synthetic chart based on the non-central chi-square statistic that is operationally simpler and more effective than the joint X̄ and R chart in detecting assignable cause(s). This chart will assist in identifying which (mean or variance) changed due to the occurrence of the assignable causes. Design/methodology/approach - The approach used is based on the non-central chi-square statistic and the steady-state average run length (ARL) of the developed chart is evaluated using a Markov chain model. Findings - The proposed chart always detects process disturbances faster than the joint X̄ and R charts. The developed chart can monitor the process instead of looking at two charts separately. Originality/value - The most important advantage of using the proposed chart is that practitioners can monitor the process by looking at only one chart instead of looking at two charts separately. © Emerald Group Publishing Limted.

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.

Monitoring in precision metal drilling process using multi-sensors and neural network

This paper presents a new method to estimate hole diameters and surface roughness in precision drilling processes, using coupons taken from a sandwich plate composed of a titanium alloy plate (Ti6Al4V) glued onto an aluminum alloy plate (AA 2024T3). The proposed method uses signals acquired during the cutting process by a multisensor system installed on the machine tool. These signals are mathematically treated and then used as input for an artificial neural network. After training, the neural network system is qualified to estimate the surface roughness and hole diameter based on the signals and cutting process parameters. To evaluate the system, the estimated data were compared with experimental measurements and the errors were calculated. The results proved the efficiency of the proposed method, which yielded very low or even negligible errors of the tolerances used in most industrial drilling processes. This pioneering method opens up a new field of research, showing a promising potential for development and application as an alternative monitoring method for drilling processes. © 2012 Springer-Verlag London Limited.

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

Pós-graduação em Engenharia Mecânica - FEG

A New Approach for Real Time Fault Diagnosis in Induction Motors Based on Vibration Measurement

Condition monitoring is used to increase machinery availability and machinery performance, reducing consequential damage, increasing machine life, reducing spare parts inventories, and reducing breakdown maintenance. An efficient real time vibration measurement and analysis instruments is capable of providing warning and predicting faults at early stages. In this paper, a new methodology for the implementation of vibration measurement and analysis instruments in real time based on circuit architecture mapped from a MATLAB/Simulink model is presented. In this study, signal processing applications such as FIR filters and fast Fourier transform are treated as systems, which are implemented in hardware using a system generator toolbox, which translates a Simulink model in a hardware description language - HDL for FPGA implementations.

Identification of lubricant contamination by biodiesel using vibration analysis and neural network

Purpose - The purpose of this paper is to provide information on lubricant contamination by biodiesel using vibration and neural network.Design/methodology/approach - The possible contamination of lubricants is verified by analyzing the vibration and neural network of a bench test under determinated conditions.Findings - Results have shown that classical signal analysis methods could not reveal any correlation between the signal and the presence of contamination, or contamination grade. on other hand, the use of probabilistic neural network (PNN) was very successful in the identification and classification of contamination and its grade.Research limitations/implications - This study was done for some specific kinds of biodiesel. Other types of biodiesel could be analyzed.Practical implications Contamination information is presented in the vibration signal, even if it is not evident by classical vibration analysis. In addition, the use of PNN gives a relatively simple and easy-to-use detection tool with good confidence. The training process is fast, and allows implementation of an adaptive training algorithm.Originality/value - This research could be extended to an internal combustion engine in order to verify a possible contamination by biodiesel.

Application of rotor response analysis to fault detection in hydro powerplants.

In this article some considerations obtained during the utilization of rotor response analysis techniques in hydraulic powerplants are discussed. An applied research work was carried out in two hydraulic turbines analysing the rotor response both theoretically and experimentally. A developed mathematical model was used to simulate the rotordynamic behaviour of Francis and Kaplan turbines. The main dynamical effects that appear during the operation of the machines are discussed too. A series of measurements were carried out in the turbines using impact hammers to determine the modal behaviour of the units. The tests were carried out with the machine still and in operation. Some results and the comparison with the theory is presented in this article. The improved theoretical model was used for a sensitivity analysis of the different bearings to the main excitations that fake place during the machine operation. From this analysis, the best measuring points for condition monitoring were determined.

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.

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 da vibração para o controle do funcionamento de um ventilador de um sistema de exaustão

This work has as objective to show the vibrational analysis as a method of predictive maintenance as an auxiliar procedure in the fail detection in equipments, most specifically in the rotative ones, and with that help the maintenance team to have conditions to foresee the right time to do the swap of the components of the systems, what would lead to failures. Knowing the exact moment the resources of people and money can be focused in the critic operations to the plant. This technic has been already studied for more then 30 years and was widely used in this work, not only as an equipment condition verification method, but also, after the equipment replacement, was used to prove that the new fan was operating under the best work conditions and with that maintenance could return to contol the vibration level of the equipment, not being necessary any kind of intervention