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On-line tool condition monitoring using artificial neural networks
N DT Abstracts This paper examines the application of artificial neural networks to the estimation of geometrical parameters of an adhered aluminium T- joint using ultrasonic Lamb waves (s + a). Modulus FFTs of received signals were applied as inputs to conventional feed-forward networks, which were trained using the delta rule with momentum. The success rate of various network structures in recognising bond categories was studied as a function of the density of information applied to the network inputs and the number of hidden nodes in the network. An optimum network structure appears to exist that will solve a number of problems of this type.
Yam W.; Upadyaya, B.R.; An integrated signal processing and neural networks system for steam generator tubing diagnostics using eddy current inspection 61317
Annals o f Nuclear Energy, Vol. 23, No. l 0, pp. 813-825 (Jul. 1996) The primary purpose of this research was to develop an integrated approach by combining information compression methods and artificial neural networks for the monitoring of plant components using nondestructive evaluation (NDE) data. Data from eddy current inspection of steam generator tubing were utilized to evaluate this technology. A PC-based data pre-processing and display program was also developed at part of an expert system for data management and decision making. The results of the analysis showed that for effective (low-error) defect classification and estimation of parameters, it is necessary to identify proper feature vectors using different data representation methods.
61471 Javed, M.A.; Hope, A.D.; Littlefair, G.; Adradi, D.; Smith, G.T.; Rao, B.K.N. O n - l i n e tool condition monitoring using artificial neural networks Insight, Vol. 38, No. 5, pp. 351-354 (May 1996) Multiple sensors are used in this work to provide complementary information about the process and this helps to improve the confidence factor of the resulting diagnostics. The problem of on-line tool wear monitoring in turning operations is approached by applying a three- layered, error-back-propagation-based network for fusion of three machinery performance-indicating features. A demonstrator system has been developed from this research and is capable of classifying previously unseen data into five discrete levels (three levels of flank wear and two levels of chipping).
61267 Dobmann, G.; Kroening, M.; Theiner, W.; Willens, H.; Fiedler, U. Nondestructive characterization of materials (ultrasonic and micromagnetic techniques) for strength and toughness prediction and the detection of early creep damage Nuclear Engineering and Design, Vol. 160 No. 1-2, pp. 137-158 (Feb. 1996) In order to characterize microstructural states superimposed by residual stresses in an unambiguous way, numerical modelling was applied using advanced tools of mathematical approximation theory, i.e. multiregression algorithms and neural networks. For the detection of early creep damage in fossil power plant applications, i.e. micropores and their subsequent development to linked pores and microcracks, besides the micromagnetic techniques an ultrasonic technique was also applied and optimized for in situ applications on components such as pipe bends. Whereas the ultrasonic technique is sensitive to pore concentrations as small as about 0.2%, the parameters of the micromagnetic techniques are mainly influenced by temperature-and load- induced microstructural changes occurring in service, dependent on the steel quality. The techniques are applied at two pipe bends (steel grades 14MoV63 and X20CrMoVI21) loaded under near practical conditions during seven inspection intervals between 2048h and 21000h to evaluate the progress of damage.
61439 Carullo, A.; Ferraris, F.; Graziani, S.; Grimaldi, U.; Parvis, M. Ultrasonic distance sensor improvement using a two-level neural network IEEE Transactions on Instrumentation and Measurement, Vol. 45, No. 2, pp. 677-682 (1996) This paper discusses the performance improvement that a neural network can provide to a contactless distance sensor based on the measurement of the time of flight (TOF) of an ultrasonic (US) pulse. The sensor, which embeds a correction system for the temperature effect, achieves a distance uncertainty (rms) of less than 0.5 mm over 0.5 m by using a two-level neural network to process the US echo and determine the TOF in the presence of environmental acoustic noise. The network embeds a "guard" neuron that guards against gross measurement errors, which would be possible in the presence of high environmental noise.
Chukwujekwu OkaJbr, A.; Chandrashekhara, K.; Jiang, Y.P. Delamination prediction in composite beams w i t h built-in piezoelectric devices using modal analysis and neural network 61223
Lowes, S.; Shippen, J. An investigation into the transferability of neural networks for condition monitoring 61429
Insight, Vol. 38, No. 8, pp. 566-569 (Aug. 1996) An investigation into the feasibility of transferring a neural network from one machine to another for fault detection is detailed within this paper: The procedure incorporates using frequency domain trends and indices determined from frequency spectra. The results indicate that the predictive accuracy of the neural network is high, therefore a neural network can be transferred with careful pre-processing of data.
Smart Materials and Structures, Vol. 5, No. 3, pp. 338-347 (Jun. 1996) The effect of prescribed delamination on natural frequencies of laminated composite beam specimens is examined both experimentally and theoretically. Modal testing of a perfect beam and beams with different delamination size is conducted using polyvinylidene fluoride film (PVDF) sensors and piezoceramic (PZT) patch with sine sweep actuation. Modal testing of beams is also conducted using PVDF sensors and instrumented hammer excitation. The experimental modal frequencies are compared with the results obtained using a simplified beam theory. Also, backpropagation neural network models are developed using the results from the beam theory and used to predict delamination size. Modal frequencies can be easily and accurately obtained with PZT patch excitation and PVDF sensing.
Jovanovic, A.S.; Lucia, A.C.; Fukuda, S. Knowledge-based (expert) systems applications in power plant and structural engineering 61415
C o m m i s s i o n o f the European Communities, EUR-15408-EN (1993) This book contains a selection of papers presented at or prepared for the SMiRT 12 (Structural Mechanics in Reactor Technology) post- conference seminar. Most of the papers are linked to very practical problems and reflect a shift from conventional expert systems to the systems that tend to be more or less integrated with other tools. Tutorial-like papers given an introduction and describe the state of the art in some of the most important enabling technologies. Review papers given an illustration of what is going on in the field with particular emphasis on European Commission projects. These papers are followed by contributions presenting single systems or projects and their result in design, operation, maintenance, analysis, inspection and control.
Reintjes, J.; Mahon, R.; Duncan, M.D.; Tankersley, L.L.; Schultz, A.; Chen, V.C.; Kover, DJ.; Howard, P.L. O p t i c a l oil debris monitor 61170
Advanced Materials and Process T e c h n o l o g y for Mechanical Failure Prevention. Proceedings o f the 48th Meeting o f the Mechanical Failures Prevention Group, Wakefield, Massachusetts (United States), 19-21 Apr. 1994, pp. 57-66. Vibration Institute (1994) We describe a real time, on-line, optical oil-debris monitor that is expected to provide a cumulative record of the health of engines and gear boxes as well as advanced warning of catastrophic failure. The monitor is based on illumination of the oil lubrication column with a diode laser, followed by imaging in transmission of suspended particles and identification of the particles by analysis of their shape and size using an on-board computerized particle classifier. The optical monitor is capable of recognizing metallic and ferrous particles, such as those from metal gears and bearings, as well as non ferrous particles, such as ingested sand and debris from ceramic or composite bearings. We will describe initial tests of the concept using video detection of images of various types of particles in flowing oil systems and will discuss computational requirements and alternatives for real time particle classification.
Hakulinen, A.; Hakkarainen, J. A neural network approach to quality control of padlock manufacturing
61368
Pattern Recognition Letters, Vol. 17, No. 4, pp. 357-362 (1996) We describe a neural network based visual quality control system especially suited for the needs of padlock manufacturing. In this system the image produced by the CCD camera is converted into a binary image by using a custom filter. The image is divided into subimages which are aligned via a set of reference images. The horizontal and vertical projections of the subimages are used as input features to a MLP neural network classifier. We also present some preliminary empirical results.
326
Yam W.; Upadyaya, B.R.; An integrated signal processing and neural networks system for steam generator tubing diagnostics using eddy current inspection 61317
Annals o f Nuclear Energy, Vol. 23, No. l 0, pp. 813-825 (Jul. 1996) The primary purpose of this research was to develop an integrated approach by combining information compression methods and artificial neural networks for the monitoring of plant components using nondestructive evaluation (NDE) data. Data from eddy current inspection of steam generator tubing were utilized to evaluate this technology. A PC-based data pre-processing and display program was also developed at part of an expert system for data management and decision making. The results of the analysis showed that for effective (low-error) defect classification and estimation of parameters, it is necessary to identify proper feature vectors using different data representation methods.
61471 Javed, M.A.; Hope, A.D.; Littlefair, G.; Adradi, D.; Smith, G.T.; Rao, B.K.N. O n - l i n e tool condition monitoring using artificial neural networks Insight, Vol. 38, No. 5, pp. 351-354 (May 1996) Multiple sensors are used in this work to provide complementary information about the process and this helps to improve the confidence factor of the resulting diagnostics. The problem of on-line tool wear monitoring in turning operations is approached by applying a three- layered, error-back-propagation-based network for fusion of three machinery performance-indicating features. A demonstrator system has been developed from this research and is capable of classifying previously unseen data into five discrete levels (three levels of flank wear and two levels of chipping).
61267 Dobmann, G.; Kroening, M.; Theiner, W.; Willens, H.; Fiedler, U. Nondestructive characterization of materials (ultrasonic and micromagnetic techniques) for strength and toughness prediction and the detection of early creep damage Nuclear Engineering and Design, Vol. 160 No. 1-2, pp. 137-158 (Feb. 1996) In order to characterize microstructural states superimposed by residual stresses in an unambiguous way, numerical modelling was applied using advanced tools of mathematical approximation theory, i.e. multiregression algorithms and neural networks. For the detection of early creep damage in fossil power plant applications, i.e. micropores and their subsequent development to linked pores and microcracks, besides the micromagnetic techniques an ultrasonic technique was also applied and optimized for in situ applications on components such as pipe bends. Whereas the ultrasonic technique is sensitive to pore concentrations as small as about 0.2%, the parameters of the micromagnetic techniques are mainly influenced by temperature-and load- induced microstructural changes occurring in service, dependent on the steel quality. The techniques are applied at two pipe bends (steel grades 14MoV63 and X20CrMoVI21) loaded under near practical conditions during seven inspection intervals between 2048h and 21000h to evaluate the progress of damage.
61439 Carullo, A.; Ferraris, F.; Graziani, S.; Grimaldi, U.; Parvis, M. Ultrasonic distance sensor improvement using a two-level neural network IEEE Transactions on Instrumentation and Measurement, Vol. 45, No. 2, pp. 677-682 (1996) This paper discusses the performance improvement that a neural network can provide to a contactless distance sensor based on the measurement of the time of flight (TOF) of an ultrasonic (US) pulse. The sensor, which embeds a correction system for the temperature effect, achieves a distance uncertainty (rms) of less than 0.5 mm over 0.5 m by using a two-level neural network to process the US echo and determine the TOF in the presence of environmental acoustic noise. The network embeds a "guard" neuron that guards against gross measurement errors, which would be possible in the presence of high environmental noise.
Chukwujekwu OkaJbr, A.; Chandrashekhara, K.; Jiang, Y.P. Delamination prediction in composite beams w i t h built-in piezoelectric devices using modal analysis and neural network 61223
Lowes, S.; Shippen, J. An investigation into the transferability of neural networks for condition monitoring 61429
Insight, Vol. 38, No. 8, pp. 566-569 (Aug. 1996) An investigation into the feasibility of transferring a neural network from one machine to another for fault detection is detailed within this paper: The procedure incorporates using frequency domain trends and indices determined from frequency spectra. The results indicate that the predictive accuracy of the neural network is high, therefore a neural network can be transferred with careful pre-processing of data.
Smart Materials and Structures, Vol. 5, No. 3, pp. 338-347 (Jun. 1996) The effect of prescribed delamination on natural frequencies of laminated composite beam specimens is examined both experimentally and theoretically. Modal testing of a perfect beam and beams with different delamination size is conducted using polyvinylidene fluoride film (PVDF) sensors and piezoceramic (PZT) patch with sine sweep actuation. Modal testing of beams is also conducted using PVDF sensors and instrumented hammer excitation. The experimental modal frequencies are compared with the results obtained using a simplified beam theory. Also, backpropagation neural network models are developed using the results from the beam theory and used to predict delamination size. Modal frequencies can be easily and accurately obtained with PZT patch excitation and PVDF sensing.
Jovanovic, A.S.; Lucia, A.C.; Fukuda, S. Knowledge-based (expert) systems applications in power plant and structural engineering 61415
C o m m i s s i o n o f the European Communities, EUR-15408-EN (1993) This book contains a selection of papers presented at or prepared for the SMiRT 12 (Structural Mechanics in Reactor Technology) post- conference seminar. Most of the papers are linked to very practical problems and reflect a shift from conventional expert systems to the systems that tend to be more or less integrated with other tools. Tutorial-like papers given an introduction and describe the state of the art in some of the most important enabling technologies. Review papers given an illustration of what is going on in the field with particular emphasis on European Commission projects. These papers are followed by contributions presenting single systems or projects and their result in design, operation, maintenance, analysis, inspection and control.
Reintjes, J.; Mahon, R.; Duncan, M.D.; Tankersley, L.L.; Schultz, A.; Chen, V.C.; Kover, DJ.; Howard, P.L. O p t i c a l oil debris monitor 61170
Advanced Materials and Process T e c h n o l o g y for Mechanical Failure Prevention. Proceedings o f the 48th Meeting o f the Mechanical Failures Prevention Group, Wakefield, Massachusetts (United States), 19-21 Apr. 1994, pp. 57-66. Vibration Institute (1994) We describe a real time, on-line, optical oil-debris monitor that is expected to provide a cumulative record of the health of engines and gear boxes as well as advanced warning of catastrophic failure. The monitor is based on illumination of the oil lubrication column with a diode laser, followed by imaging in transmission of suspended particles and identification of the particles by analysis of their shape and size using an on-board computerized particle classifier. The optical monitor is capable of recognizing metallic and ferrous particles, such as those from metal gears and bearings, as well as non ferrous particles, such as ingested sand and debris from ceramic or composite bearings. We will describe initial tests of the concept using video detection of images of various types of particles in flowing oil systems and will discuss computational requirements and alternatives for real time particle classification.
Hakulinen, A.; Hakkarainen, J. A neural network approach to quality control of padlock manufacturing
61368
Pattern Recognition Letters, Vol. 17, No. 4, pp. 357-362 (1996) We describe a neural network based visual quality control system especially suited for the needs of padlock manufacturing. In this system the image produced by the CCD camera is converted into a binary image by using a custom filter. The image is divided into subimages which are aligned via a set of reference images. The horizontal and vertical projections of the subimages are used as input features to a MLP neural network classifier. We also present some preliminary empirical results.
326