2018
Mikołajczyk, T; Nowicki, K; Bustillo, A; Pimenov, Yu D
Predicting tool life in turning operations using neural networks and image processing Journal Article
In: Mechanical Systems and Signal Processing, vol. 104, pp. 503 - 513, 2018, ISSN: 0888-3270.
Abstract | Links | BibTeX | Tags: Image analysis, Neural networks, Tool life prediction, Tool wear
@article{MIKOLAJCZYK2018503,
title = {Predicting tool life in turning operations using neural networks and image processing},
author = {T Mikołajczyk and K Nowicki and A Bustillo and Yu D Pimenov},
url = {http://www.sciencedirect.com/science/article/pii/S088832701730599X},
doi = {https://doi.org/10.1016/j.ymssp.2017.11.022},
issn = {0888-3270},
year = {2018},
date = {2018-05-01},
journal = {Mechanical Systems and Signal Processing},
volume = {104},
pages = {503 - 513},
abstract = {A two-step method is presented for the automatic prediction of tool life in turning operations. First, experimental data are collected for three cutting edges under the same constant processing conditions. In these experiments, the parameter of tool wear, VB, is measured with conventional methods and the same parameter is estimated using Neural Wear, a customized software package that combines flank wear image recognition and Artificial Neural Networks (ANNs). Second, an ANN model of tool life is trained with the data collected from the first two cutting edges and the subsequent model is evaluated on two different subsets for the third cutting edge: the first subset is obtained from the direct measurement of tool wear and the second is obtained from the Neural Wear software that estimates tool wear using edge images. Although the complete-automated solution, Neural Wear software for tool wear recognition plus the ANN model of tool life prediction, presented a slightly higher error than the direct measurements, it was within the same range and can meet all industrial requirements. These results confirm that the combination of image recognition software and ANN modelling could potentially be developed into a useful industrial tool for low-cost estimation of tool life in turning operations.},
keywords = {Image analysis, Neural networks, Tool life prediction, Tool wear},
pubstate = {published},
tppubtype = {article}
}
2015
Santos, Pedro; Villa, Luisa F; Reñones, Anibal; Bustillo, Andrés; Maudes-Raedo, Jesús
An SVM-Based Solution for Fault Detection in Wind Turbines Journal Article
In: Sensors, vol. 15, no. 3, pp. 5627-5648, 2015, ISSN: 1424-8220.
Abstract | Links | BibTeX | Tags: Fault diagnosis, Neural networks, Support vector machines, wind turbines
@article{Santos2015,
title = {An SVM-Based Solution for Fault Detection in Wind Turbines},
author = {Pedro Santos and Luisa F Villa and Anibal Reñones and Andrés Bustillo and Jesús Maudes-Raedo},
url = {http://www.mdpi.com/1424-8220/15/3/5627},
doi = {10.3390/s150305627},
issn = {1424-8220},
year = {2015},
date = {2015-03-09},
journal = {Sensors},
volume = {15},
number = {3},
pages = {5627-5648},
abstract = {Research into fault diagnosis in machines with a wide range of variable loads and speeds, such as wind turbines, is of great industrial interest. Analysis of the power signals emitted by wind turbines for the diagnosis of mechanical faults in their mechanical transmission chain is insufficient. A successful diagnosis requires the inclusion of accelerometers to evaluate vibrations. This work presents a multi-sensory system for fault diagnosis in wind turbines, combined with a data-mining solution for the classification of the operational state of the turbine. The selected sensors are accelerometers, in which vibration signals are processed using angular resampling techniques and electrical, torque and speed measurements. Support vector machines (SVMs) are selected for the classification task, including two traditional and two promising new kernels. This multi-sensory system has been validated on a test-bed that simulates the real conditions of wind turbines with two fault typologies: misalignment and imbalance. Comparison of SVM performance with the results of artificial neural networks (ANNs) shows that linear kernel SVM outperforms other kernels and ANNs in terms of accuracy, training and tuning times. The suitability and superior performance of linear SVM is also experimentally analyzed, to conclude that this data acquisition technique generates linearly separable datasets.},
keywords = {Fault diagnosis, Neural networks, Support vector machines, wind turbines},
pubstate = {published},
tppubtype = {article}
}
2014
Santos, Pedro; Teixidor, Daniel; Maudes-Raedo, Jesús; Ciurana, Joaquim
Modelling Laser Milling of Microcavities for the Manufacturing of DES with Ensembles Journal Article
In: Journal of Applied Mathematics, vol. 2014, pp. 15, 2014, ISBN: 1110-757X.
Abstract | Links | BibTeX | Tags: Ensemble methods, Laser milling, Neural networks, Support vector machines
@article{Santos2014,
title = {Modelling Laser Milling of Microcavities for the Manufacturing of DES with Ensembles},
author = {Pedro Santos and Daniel Teixidor and Jesús Maudes-Raedo and Joaquim Ciurana},
url = {https://www.hindawi.com/journals/jam/2014/439091/},
doi = {10.1155/2014/439091},
isbn = {1110-757X},
year = {2014},
date = {2014-04-17},
journal = {Journal of Applied Mathematics},
volume = {2014},
pages = {15},
abstract = {A set of designed experiments, involving the use of a pulsed Nd:YAG laser system milling 316L Stainless Steel, serve to study the laser-milling process of microcavities in the manufacture of drug-eluting stents (DES). Diameter, depth, and volume error are considered to be optimized as functions of the process parameters, which include laser intensity, pulse frequency, and scanning speed. Two different DES shapes are studied that combine semispheres and cylinders. Process inputs and outputs are defined by considering the process parameters that can be changed under industrial conditions and the industrial requirements of this manufacturing process. In total, 162 different conditions are tested in a process that is modeled with the following state-of-the-art data-mining regression techniques: Support Vector Regression, Ensembles, Artificial Neural Networks, Linear Regression, and Nearest Neighbor Regression. Ensemble regression emerged as the most suitable technique for studying this industrial problem. Specifically, Iterated Bagging ensembles with unpruned model trees outperformed the other methods in the tests. This method can predict the geometrical dimensions of the machined microcavities with relative errors related to the main average value in the range of 3 to 23%, which are considered very accurate predictions, in view of the characteristics of this innovative industrial task.},
keywords = {Ensemble methods, Laser milling, Neural networks, Support vector machines},
pubstate = {published},
tppubtype = {article}
}
2011
Bustillo, Andres; Díez-Pastor, José Francisco; Quintana, Guillem; García-Osorio, César
Avoiding neural network fine tuning by using ensemble learning: application to ball-end milling operations Journal Article
In: The International Journal of Advanced Manufacturing Technology, vol. 57, no. 5–8, pp. 1-12, 2011, ISSN: 0268-3768, (10.1007/s00170-011-3300-z).
Links | BibTeX | Tags: Applied Machine Learning, Business intelligence, Neural networks
@article{Bustillo2011,
title = {Avoiding neural network fine tuning by using ensemble learning: application to ball-end milling operations},
author = {Andres Bustillo and José Francisco Díez-Pastor and Guillem Quintana and César García-Osorio},
url = {http://dx.doi.org/10.1007/s00170-011-3300-z},
doi = {10.1007/s00170-011-3300-z},
issn = {0268-3768},
year = {2011},
date = {2011-01-01},
journal = {The International Journal of Advanced Manufacturing Technology},
volume = {57},
number = {5–8},
pages = {1-12},
publisher = {Springer London},
note = {10.1007/s00170-011-3300-z},
keywords = {Applied Machine Learning, Business intelligence, Neural networks},
pubstate = {published},
tppubtype = {article}
}
2010
Pardo, Carlos; Rodríguez, Juan José; García-Osorio, César; Maudes, Jesús
An Empirical Study of Multilayer Perceptron Ensembles for Regression Tasks Proceedings Article
In: García-Pedrajas, Nicolás; Herrera, Francisco; Fyfe, Colin; Benítez, José Manuel; Ali, Moonis (Ed.): Trends in Applied Intelligent Systems: 23rd International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2010, pp. 106–115, Springer, Córdoba, Spain, 2010, ISBN: 978-3-642-13024-3.
BibTeX | Tags: Data Mining, Ensemble methods, Neural networks, Regression
@inproceedings{PRGM10,
title = {An Empirical Study of Multilayer Perceptron Ensembles for Regression Tasks},
author = {Carlos Pardo and Juan José Rodríguez and César García-Osorio and Jesús Maudes},
editor = {Nicolás García-Pedrajas and Francisco Herrera and Colin Fyfe and José Manuel Benítez and Moonis Ali},
isbn = {978-3-642-13024-3},
year = {2010},
date = {2010-01-01},
booktitle = {Trends in Applied Intelligent Systems: 23rd International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2010},
volume = {6097},
pages = {106–115},
publisher = {Springer},
address = {Córdoba, Spain},
series = {Lecture Notes in Computer Science},
keywords = {Data Mining, Ensemble methods, Neural networks, Regression},
pubstate = {published},
tppubtype = {inproceedings}
}
2007
García-Pedrajas, Nicolás; García-Osorio, César; Fyfe, Colin
Nonlinear ``boosting'' projections for ensemble construction Journal Article
In: Journal of Machine Learning Research, vol. 8, pp. 1–33, 2007, ISSN: 1532-4435.
Abstract | Links | BibTeX | Tags: Boosting, Classifier ensembles, Data Mining, Ensemble methods, Neural networks, Nonlinear projections
@article{cgosorio07boosting,
title = {Nonlinear ``boosting'' projections for ensemble construction},
author = {Nicolás García-Pedrajas and César García-Osorio and Colin Fyfe},
url = {http://jmlr.csail.mit.edu/papers/volume8/garcia-pedrajas07a/garcia-pedrajas07a.pdf},
issn = {1532-4435},
year = {2007},
date = {2007-01-01},
journal = {Journal of Machine Learning Research},
volume = {8},
pages = {1–33},
abstract = {In this paper we propose a novel approach for ensemble construction based on the use of nonlinear
projections to achieve both accuracy and diversity of individual classifiers. The proposed approach
combines the philosophy of boosting, putting more effort on difficult instances, with the basis of
the random subspace method. Our main contribution is that instead of using a random subspace,
we construct a projection taking into account the instances which have posed most difficulties to
previous classifiers. In this way, consecutive nonlinear projections are created by a neural network
trained using only incorrectly classified instances. The feature subspace induced by the hidden layer
of this network is used as the input space to a new classifier. The method is compared with bagging
and boosting techniques, showing an improved performance on a large set of 44 problems from the
UCI Machine Learning Repository. An additional study showed that the proposed approach is less
sensitive to noise in the data than boosting methods.},
keywords = {Boosting, Classifier ensembles, Data Mining, Ensemble methods, Neural networks, Nonlinear projections},
pubstate = {published},
tppubtype = {article}
}
projections to achieve both accuracy and diversity of individual classifiers. The proposed approach
combines the philosophy of boosting, putting more effort on difficult instances, with the basis of
the random subspace method. Our main contribution is that instead of using a random subspace,
we construct a projection taking into account the instances which have posed most difficulties to
previous classifiers. In this way, consecutive nonlinear projections are created by a neural network
trained using only incorrectly classified instances. The feature subspace induced by the hidden layer
of this network is used as the input space to a new classifier. The method is compared with bagging
and boosting techniques, showing an improved performance on a large set of 44 problems from the
UCI Machine Learning Repository. An additional study showed that the proposed approach is less
sensitive to noise in the data than boosting methods.
2005
García-Osorio, César
Data Mining and Visualization PhD Thesis
School of Computing, Paisley University, 2005.
BibTeX | Tags: Andrews curves, Data Mining, Data visualization, Neural networks, Self organizing maps
@phdthesis{cgosorio05thesis,
title = {Data Mining and Visualization},
author = {César García-Osorio},
year = {2005},
date = {2005-05-01},
address = {High Street, Paisley, Strathclyde, PA1 2BE, United Kingdon},
school = {School of Computing, Paisley University},
keywords = {Andrews curves, Data Mining, Data visualization, Neural networks, Self organizing maps},
pubstate = {published},
tppubtype = {phdthesis}
}
García-Osorio, César; Fyfe, Colin
The Combined Use of Self-organizing Maps and Andrews' Curves Journal Article
In: International Journal of Neural Systems, vol. 15, no. 3, pp. 1-10, 2005, ISSN: 0129-0657.
Links | BibTeX | Tags: Andrews curves, Data Mining, Data visualization, Neural networks, Self organizing maps
@article{cgosorio05AndrewsSOM,
title = {The Combined Use of Self-organizing Maps and Andrews' Curves},
author = {César García-Osorio and Colin Fyfe},
doi = {10.1142/S0129065705000207},
issn = {0129-0657},
year = {2005},
date = {2005-01-01},
journal = {International Journal of Neural Systems},
volume = {15},
number = {3},
pages = {1-10},
keywords = {Andrews curves, Data Mining, Data visualization, Neural networks, Self organizing maps},
pubstate = {published},
tppubtype = {article}
}
García-Osorio, César
Data Mining and Visualization Technical Report
Paisley University High Street, Paisley, Strathclyde, PA1 2BE, United Kingdon, no. 30, 2005.
Links | BibTeX | Tags: Andrews curves, Data Mining, Data visualization, Neural networks, Self organizing maps
@techreport{cgosorio05TR,
title = {Data Mining and Visualization},
author = {César García-Osorio},
url = {http://cis.paisley.ac.uk/research/reports/tr30.zip},
year = {2005},
date = {2005-01-01},
number = {30},
address = {High Street, Paisley, Strathclyde, PA1 2BE, United Kingdon},
institution = {Paisley University},
keywords = {Andrews curves, Data Mining, Data visualization, Neural networks, Self organizing maps},
pubstate = {published},
tppubtype = {techreport}
}
2004
García-Osorio, César; Raedo, Jesús Maudes; Fyfe, Colin
Using Andrews' Curves for Clustering and Sub-Clustering Self-Organizing Maps Proceedings Article
In: Verleysen, Michel (Ed.): European Symposium on Artificial Neural Networks (ESANN 2004), pp. 477–482, d-side publications, Bruges, Belgium, 2004, ISBN: 2-930307-04-8.
BibTeX | Tags: Andrews curves, Clustering, Data Mining, Data visualization, Neural networks, Self organizing maps
@inproceedings{cgosorio04withSOM,
title = {Using Andrews' Curves for Clustering and Sub-Clustering Self-Organizing Maps},
author = {César García-Osorio and Jesús Maudes Raedo and Colin Fyfe},
editor = {Michel Verleysen},
isbn = {2-930307-04-8},
year = {2004},
date = {2004-04-01},
booktitle = {European Symposium on Artificial Neural Networks (ESANN 2004)},
pages = {477–482},
publisher = {d-side publications},
address = {Bruges, Belgium},
keywords = {Andrews curves, Clustering, Data Mining, Data visualization, Neural networks, Self organizing maps},
pubstate = {published},
tppubtype = {inproceedings}
}
García-Osorio, César; Fyfe, Colin
Comparing Exploratory Projection Pursuit Artificial Neural Networks Proceedings Article
In: Botty, Vicente; Corchado, Emilio (Ed.): 3rd International Workshop on Practical Applications of Agents and Multiagent Systems, IWPAAMS 2004, pp. 201–210, Universidad de Burgos, 2004, ISBN: 84–96394–07–7.
BibTeX | Tags: Data Mining, Exploratory projection pursuit, Neural networks
@inproceedings{cgosorio04comparing,
title = {Comparing Exploratory Projection Pursuit Artificial Neural Networks},
author = {César García-Osorio and Colin Fyfe},
editor = {Vicente Botty and Emilio Corchado},
isbn = {84–96394–07–7},
year = {2004},
date = {2004-01-01},
booktitle = {3rd International Workshop on Practical Applications of Agents and Multiagent Systems, IWPAAMS 2004},
pages = {201–210},
publisher = {Universidad de Burgos},
keywords = {Data Mining, Exploratory projection pursuit, Neural networks},
pubstate = {published},
tppubtype = {inproceedings}
}
2003
García-Osorio, César; Fyfe, Colin
Three Neural Exploratory Projection Pursuit Algorithms Proceedings Article
In: European Symposium on Intelligent Technologies, Hybrid Systems and their implementation on Smart Adaptive Systems (EUNITE 2003), pp. 409–420, 2003, ISBN: 3-86130-194-6.
BibTeX | Tags: Data Mining, Exploratory projection pursuit, Neural networks
@inproceedings{cgosorio03threeEPP,
title = {Three Neural Exploratory Projection Pursuit Algorithms},
author = {César García-Osorio and Colin Fyfe},
isbn = {3-86130-194-6},
year = {2003},
date = {2003-01-01},
booktitle = {European Symposium on Intelligent Technologies, Hybrid Systems and their implementation on Smart Adaptive Systems (EUNITE 2003)},
pages = {409–420},
keywords = {Data Mining, Exploratory projection pursuit, Neural networks},
pubstate = {published},
tppubtype = {inproceedings}
}
García-Osorio, César; Fyfe, Colin
Initialising Exploratory Projection Pursuit Networks Proceedings Article
In: Hamza, M H (Ed.): 3rd Annual IASTED International Conference on Visualization, Imaging, and Image Processing (VIIP 2003), pp. 124–128, The International Association of Science and Technology for Development, 2003, ISBN: 0-88986-382-2.
BibTeX | Tags: Data Mining, Exploratory projection pursuit, Neural networks
@inproceedings{cgosorio03initialisingEPP,
title = {Initialising Exploratory Projection Pursuit Networks},
author = {César García-Osorio and Colin Fyfe},
editor = {M H Hamza},
isbn = {0-88986-382-2},
year = {2003},
date = {2003-01-01},
booktitle = {3rd Annual IASTED International Conference on Visualization, Imaging, and Image Processing (VIIP 2003)},
pages = {124–128},
publisher = {The International Association of Science and Technology for Development},
keywords = {Data Mining, Exploratory projection pursuit, Neural networks},
pubstate = {published},
tppubtype = {inproceedings}
}
2002
Corchado, Emilio; Fyfe, Colin; Marticorena-Sánchez, Raúl; López-Nozal, Carlos; García-Osorio, César; Maudes-Raedo, Jesús
Unsupervised Learning applied to the Identification of Languages Proceedings Article
In: Corchado, J M; others, (Ed.): The International Workshop on the Practical Applications of Agents and Multi-agent Systems, pp. 261–264, Universidad de Salamanca, 2002, ISBN: 84-607-5827-3.
BibTeX | Tags: Data Mining, Neural networks, Unsupervised learning
@inproceedings{corchado02aprendizaje,
title = {Unsupervised Learning applied to the Identification of Languages},
author = {Emilio Corchado and Colin Fyfe and Raúl Marticorena-Sánchez and Carlos López-Nozal and César García-Osorio and Jesús Maudes-Raedo},
editor = {J M Corchado and others},
isbn = {84-607-5827-3},
year = {2002},
date = {2002-01-01},
booktitle = {The International Workshop on the Practical Applications of Agents and Multi-agent Systems},
pages = {261–264},
publisher = {Universidad de Salamanca},
keywords = {Data Mining, Neural networks, Unsupervised learning},
pubstate = {published},
tppubtype = {inproceedings}
}
García-Osorio, César; Corchado, Emilio; Fyfe, Colin
Three Neural Exploratory Projection Pursuit Algorithms as Agent Information Providers Proceedings Article
In: The International Workshop on the Practical Applications of Agents and Multi-agent Systems, pp. 217–226, Universidad de Salamanca, 2002, ISBN: 84-607-5827-3.
BibTeX | Tags: Data Mining, Exploratory projection pursuit, Neural networks
@inproceedings{cgosorio02epp,
title = {Three Neural Exploratory Projection Pursuit Algorithms as Agent Information Providers},
author = {César García-Osorio and Emilio Corchado and Colin Fyfe},
isbn = {84-607-5827-3},
year = {2002},
date = {2002-01-01},
booktitle = {The International Workshop on the Practical Applications of Agents and Multi-agent Systems},
pages = {217–226},
publisher = {Universidad de Salamanca},
keywords = {Data Mining, Exploratory projection pursuit, Neural networks},
pubstate = {published},
tppubtype = {inproceedings}
}