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Engineering Applications

Along with colleagues in Engineering at Monash and Deakin Universities, I am investigating applications of data science in process control.

Publications

Tamping Effectiveness Prediction Using Supervised Machine LearningTechniques.
Tan, C. W., Webb, G. I., Petitjean, F., & P., R.
Proceedings of the First International Conference on Rail Transportation (ICRT), in press.
[Bibtex] [Abstract]

@InProceedings{TanEtAl17b,
Title = {Tamping Effectiveness Prediction Using Supervised Machine LearningTechniques},
Author = {Tan, C. W. and Webb, G. I. and Petitjean, F. and Reichl P.},
Booktitle = {Proceedings of the First International Conference on Rail Transportation (ICRT)},
Year = {in press},
Abstract = {Railway maintenance planning is critical in maintaining track assets. Tamping is a common railway maintenance procedure and is often used when geometrical issues are first identified. Tamping repacks ballast particles under sleepers to restore the correct geometrical position of ballasted tracks. However, historical data shows that tamping is not always effective in restoring track to a satisfactory condition. Furthermore, ineffective, or unnecessary tamping tends to reduce the lifetime of existing track. An intuitive way of preventing ineffective tamping is to predict the likely tamping effectiveness. This work aims to predict the likely tamping effectiveness ahead of time using supervised machine learning techniques. Supervised machine learning techniques predict an outcome using labelled training data. In this case, the training database consists of multivariate sensor data from Instrumented Revenue Vehicles (IRVs). The data between the previous and current tamping dates are used. This forms a time series database labelled with the tamping effectiveness of each track location based on the responses recorded from the IRVs before and after tamping. The labelled time series database is then used to train a time series classifier for prediction. This work uses the state of the art time series classification algorithm, k-Nearest Neighbour (k-NN) extended to the case of multivariate time series. k-NN is a non-parametric algorithm that does not make assumptions on the underlying model of the training data. With a sufficiently large training database, non-parametric algorithms can outperform parametric algorithms. Using k-NN, the tamping effectiveness of a potential tamping location that is not in the training database, or locations in the next tamping cycle, is predicted using the expected tamping effectiveness from a location in the training database that is the most similar to the target. This allows the algorithm to effectively to identify locations where tamping is likely to be ineffective. This work achieves high accuracy in the prediction of tamping effectiveness even at 12 weeks before tamping. It is hoped that the methodology will help in assisting decision making for maintenance planning activities.},
Keywords = {Engineering Applications}
}
ABSTRACT Railway maintenance planning is critical in maintaining track assets. Tamping is a common railway maintenance procedure and is often used when geometrical issues are first identified. Tamping repacks ballast particles under sleepers to restore the correct geometrical position of ballasted tracks. However, historical data shows that tamping is not always effective in restoring track to a satisfactory condition. Furthermore, ineffective, or unnecessary tamping tends to reduce the lifetime of existing track. An intuitive way of preventing ineffective tamping is to predict the likely tamping effectiveness. This work aims to predict the likely tamping effectiveness ahead of time using supervised machine learning techniques. Supervised machine learning techniques predict an outcome using labelled training data. In this case, the training database consists of multivariate sensor data from Instrumented Revenue Vehicles (IRVs). The data between the previous and current tamping dates are used. This forms a time series database labelled with the tamping effectiveness of each track location based on the responses recorded from the IRVs before and after tamping. The labelled time series database is then used to train a time series classifier for prediction. This work uses the state of the art time series classification algorithm, k-Nearest Neighbour (k-NN) extended to the case of multivariate time series. k-NN is a non-parametric algorithm that does not make assumptions on the underlying model of the training data. With a sufficiently large training database, non-parametric algorithms can outperform parametric algorithms. Using k-NN, the tamping effectiveness of a potential tamping location that is not in the training database, or locations in the next tamping cycle, is predicted using the expected tamping effectiveness from a location in the training database that is the most similar to the target. This allows the algorithm to effectively to identify locations where tamping is likely to be ineffective. This work achieves high accuracy in the prediction of tamping effectiveness even at 12 weeks before tamping. It is hoped that the methodology will help in assisting decision making for maintenance planning activities.

Learning crew scheduling constraints from historical schedules.
Suraweera, P., Webb, G. I., Evans, I., & Wallace, M.
Transportation Research Part C: Emerging Technologies, 26, 214-232, 2013.
[DOI] [Bibtex]

ABSTRACT 

Analysis of Stamping Production Data with View Towards Quality Management.
Rolfe, B., Frayman, Y., Webb, G. I., & Hodgson, P.
Proceedings of the 9th International Conference on Manufacturing Excellence (ICME 03), 2003.
[PDF] [Bibtex]

@InProceedings{RolfeFraymanWebbHodgson03,
Title = {Analysis of Stamping Production Data with View Towards Quality Management},
Author = {B. Rolfe and Y. Frayman and G.I. Webb and P. Hodgson},
Booktitle = {Proceedings of the 9th International Conference on Manufacturing Excellence (ICME 03)},
Year = {2003},
Keywords = {Engineering Applications},
Location = {Melbourne, Australia},
Related = {engineering-applications}
}
ABSTRACT 

Improving the Prediction of the Roll Separating Force in a Hot Steel Finishing Mill.
Rolfe, B., Hodgson, P., & Webb, G. I.
Intelligence in a Small World - Nanomaterials for the 21st Century. Selected Papers from IPMM-2003, Boca Raton, Florida, 2003.
[PDF] [Bibtex]

@InProceedings{RolfeHodgsonWebb03,
Title = {Improving the Prediction of the Roll Separating Force in a Hot Steel Finishing Mill},
Author = {B Rolfe and P Hodgson and G. I. Webb},
Booktitle = {Intelligence in a Small World - Nanomaterials for the 21st Century. Selected Papers from IPMM-2003},
Year = {2003},
Address = {Boca Raton, Florida},
Editor = {J.A. Meech},
Publisher = {CRC-Press },
Audit-trail = {*},
Keywords = {Engineering Applications},
Location = {Sendai, Japan},
Related = {engineering-applications}
}
ABSTRACT 

Predicting The Rolling Force in Hot Steel Rolling Mill using an Ensemble Model.
Frayman, Y., Rolfe, B., Hodgson, P., & Webb, G. I.
Proceedings of the Second IASTED International Conference on Artificial Intelligence and Applications (AIA '02), Calgary, Canada, pp. 143-148, 2002.
[PDF] [Bibtex] [Abstract]

ABSTRACT Accurate prediction of the roll separating force is critical to assuring the quality of the final product in steel manufacturing. This paper presents an ensemble model that addresses these concerns. A stacked generalisation approach to ensemble modeling is used with two sets of the ensemble model members, the first set being learnt from the current input-output data of the hot rolling finishing mill, while another uses the available information on the previous coil in addition to the current information. Both sets of ensemble members include linear regression, multilayer perceptron, and k-nearest neighbor algorithms. A competitive selection model (multilayer perceptron) is then used to select the output from one of the ensemble members to be the final output of the ensemble model. The ensemble model created by such a stacked generalization is able to achieve extremely high accuracy in predicting the roll separation force with the average relative accuracy being within 1% of the actual measured roll force.

Solving Regression Problems using Competitive Ensemble Models.
Frayman, Y., Rolfe, B., & Webb, G. I.
Lecture Notes in Computer Science Vol. 2557: Proceedings of the 15th Australian Joint Conference on Artificial Intelligence (AI 02), Berlin/Heidelberg, pp. 511-522, 2002.
[PDF] [Bibtex] [Abstract]

@InProceedings{FraymanRolfeWebb02,
Title = {Solving Regression Problems using Competitive Ensemble Models},
Author = {Y Frayman and B. Rolfe and G. I. Webb},
Booktitle = {Lecture Notes in Computer Science Vol. 2557: Proceedings of the 15th Australian Joint Conference on Artificial Intelligence (AI 02)},
Year = {2002},
Address = {Berlin/Heidelberg},
Editor = {B. McKay and J.K. Slaney },
Pages = {511-522},
Publisher = {Springer},
Abstract = {The use of ensemble models in many problem domains has increased significantly in the last few years. The ensemble modelling, in particularly boosting, has shown a great promise in improving predictive performance of a model. Combining the ensemble members is normally done in a co-operative fashion where each of the ensemble members performs the same task and their predictions are aggregated to obtain the improved performance. However, it is also possible to combine the ensemble members in a competitive fashion where the best prediction of a relevant ensemble member is selected for a particular input. This option has been previously somewhat overlooked. The aim of this article is to investigate and compare the competitive and co-operative approaches to combining the models in the ensemble. A comparison is made between a competitive ensemble model and that of MARS with bagging, mixture of experts, hierarchical mixture of experts and a neural network ensemble over several public domain regression problems that have a high degree of nonlinearity and noise. The empirical results show a substantial advantage of competitive learning versus the co-operative learning for all the regression problems investigated. The requirements for creating the efficient ensembles and the available guidelines are also discussed.},
Audit-trail = {*},
Keywords = {Engineering Applications},
Location = {Canberra, Australia},
Related = {engineering-applications}
}
ABSTRACT The use of ensemble models in many problem domains has increased significantly in the last few years. The ensemble modelling, in particularly boosting, has shown a great promise in improving predictive performance of a model. Combining the ensemble members is normally done in a co-operative fashion where each of the ensemble members performs the same task and their predictions are aggregated to obtain the improved performance. However, it is also possible to combine the ensemble members in a competitive fashion where the best prediction of a relevant ensemble member is selected for a particular input. This option has been previously somewhat overlooked. The aim of this article is to investigate and compare the competitive and co-operative approaches to combining the models in the ensemble. A comparison is made between a competitive ensemble model and that of MARS with bagging, mixture of experts, hierarchical mixture of experts and a neural network ensemble over several public domain regression problems that have a high degree of nonlinearity and noise. The empirical results show a substantial advantage of competitive learning versus the co-operative learning for all the regression problems investigated. The requirements for creating the efficient ensembles and the available guidelines are also discussed.

Improving an Inverse Model of Sheet Metal Forming by Neural Network Based Regression.
Frayman, Y., Rolfe, B., & Webb, G. I.
Proceedings of the Design Engineering Technical Conferences and Computer and Information in Engineering Conference (DETC'02/ASME 2002), New York, pp. 1-8, 2002.
[PDF] [Bibtex] [Abstract]

@InProceedings{FraymanRolfeWebb02b,
Title = {Improving an Inverse Model of Sheet Metal Forming by Neural Network Based Regression},
Author = {Y Frayman and B. Rolfe and G. I. Webb},
Booktitle = {Proceedings of the Design Engineering Technical Conferences and Computer and Information in Engineering Conference (DETC'02/ASME 2002)},
Year = {2002},
Address = {New York},
Pages = {1-8},
Publisher = {ASME Press},
Abstract = {The inverse model for a sheet metal forming process aims to determine the initial parameter levels required to form the final formed shape. This is a difficult problem that is usually approached by traditional methods such a finite element analysis. Formulating the problem as a classification problem makes is possible to use a well established classification algorithms such as decision trees. The classification is, however, generally based on a winner-takes-all approach when associating the output value with the corresponding class. On the other hand when formulating the problem as a regression task, all the output values are combined to produce the corresponding class value. For a multi-class problem, this may result in very different associations between the output of the model and the corresponding class. Such formulation makes it possible to use a well known regression algorithms such as neural networks.In this paper, we develop a neural network based inverse model of a sheet forming process, and compare its performance with that of a linear model. Both models are used in two modes: classification mode and a function estimation mode to investigate the advantage of re-formulating the problem as function estimation. This results in large improvements in the recognition rate of set-up parameters of a sheet metal forming process for both models, with a neural network model achieving much more accurate parameters recognition than a linear model},
Audit-trail = {*},
Keywords = {Engineering Applications},
Related = {engineering-applications}
}
ABSTRACT The inverse model for a sheet metal forming process aims to determine the initial parameter levels required to form the final formed shape. This is a difficult problem that is usually approached by traditional methods such a finite element analysis. Formulating the problem as a classification problem makes is possible to use a well established classification algorithms such as decision trees. The classification is, however, generally based on a winner-takes-all approach when associating the output value with the corresponding class. On the other hand when formulating the problem as a regression task, all the output values are combined to produce the corresponding class value. For a multi-class problem, this may result in very different associations between the output of the model and the corresponding class. Such formulation makes it possible to use a well known regression algorithms such as neural networks.In this paper, we develop a neural network based inverse model of a sheet forming process, and compare its performance with that of a linear model. Both models are used in two modes: classification mode and a function estimation mode to investigate the advantage of re-formulating the problem as function estimation. This results in large improvements in the recognition rate of set-up parameters of a sheet metal forming process for both models, with a neural network model achieving much more accurate parameters recognition than a linear model

Fault Detection in a Cold Forging Process Through Feature Extraction with a Neural Network.
Rolfe, B., Frayman, Y., Hodgson, P., & Webb, G. I.
Proceedings of the IASTED International Conference on Artificial Intelligence and Applications (AIA 2002), Calgary, Canada, pp. 155-159, 2002.
[PDF] [Bibtex] [Abstract]

ABSTRACT This paper investigates the application of neural networks to the recognition of lubrication defects typical to an industrial cold forging process employed by fastener manufacturers. The accurate recognition of lubrication errors, such as coating not being applied properly or damaged during material handling, is very important to the quality of the final product in fastener manufacture. Lubrication errors lead to increased forging loads and premature tool failure, as well as to increased defect sorting and the re-processing of the coated rod. The lubrication coating provides a barrier between the work material and the die during the drawing operation; moreover it needs be sufficiently robust to remain on the wire during the transfer to the cold forging operation. In the cold forging operation the wire undergoes multi-stage deformation without the application of any additional lubrication. Four types of lubrication errors, typical to production of fasteners, were introduced to a set of sample rods, which were subsequently drawn under laboratory conditions. The drawing force was measured, from which a limited set of features was extracted. The neural network based model learned from these features is able to recognize all types of lubrication errors to a high accuracy. The overall accuracy of the neural network model is around 98% with almost uniform distribution of errors between all four errors and the normal condition.