P.J. García Nieto

Hybrid PSO–SVM-based method for forecasting of the remaining useful life for aircraft engines and evaluation of its reliability

Abstract The present paper describes a hybrid PSO–SVM-based model for the prediction of the remaining useful life of aircraft engines. The proposed hybrid model combines support vector machines (SVMs), which have been successfully adopted for regression problems, with the particle swarm optimization (PSO) technique. This optimization technique involves kernel parameter setting in the SVM training procedure, which significantly influences the regression accuracy. However, its use in reliability applications has not been yet widely explored. Bearing this in mind, remaining useful life values have been predicted here by using the hybrid PSO–SVM-based model from the remaining measured parameters (input variables) for aircraft engines with success. A coefficient of determination equal to 0.9034 was obtained when this hybrid PSO–RBF–SVM-based model was applied to experimental data. The agreement of this model with experimental data confirmed its good performance. One of the main advantages of this predictive model is that it does not require information about the previous operation states of the engine. Finally, the main conclusions of this study are exposed.

Parametric study of selective removal of atmospheric aerosol by below-cloud scavenging

Abstract This work studies the scavenging efficiencies of aerosol particles after a given rain regime as a function of time by below-cloud scavenging. We also analyse the health impact of aerosol before and after rain by comparing the respirable dust fractions. The well-known equations of below-cloud scavenging are applied to three atmospheric conditions (clear, hazy and urban) in two precipitation regimes (drizzle and heavy rain) with four different drop size distributions (DSDs) in each case. From this study it is inferred that respirable dust is hardly scavenged by rain, and DSDs have minor importance in each rain regime in the results. As compared with the volume of respirable aerosol before rain, it remains roughly 60% after 12 h of drizzle and after 1 h of heavy rain.

Hybrid modelling based on support vector regression with genetic algorithms in forecasting the cyanotoxins presence in the Trasona reservoir (Northern Spain).

Cyanotoxins, a kind of poisonous substances produced by cyanobacteria, are responsible for health risks in drinking and recreational waters. As a result, anticipate its presence is a matter of importance to prevent risks. The aim of this study is to use a hybrid approach based on support vector regression (SVR) in combination with genetic algorithms (GAs), known as a genetic algorithm support vector regression (GA-SVR) model, in forecasting the cyanotoxins presence in the Trasona reservoir (Northern Spain). The GA-SVR approach is aimed at highly nonlinear biological problems with sharp peaks and the tests carried out proved its high performance. Some physical-chemical parameters have been considered along with the biological ones. The results obtained are two-fold. In the first place, the significance of each biological and physical-chemical variable on the cyanotoxins presence in the reservoir is determined with success. Finally, a predictive model able to forecast the possible presence of cyanotoxins in a short term was obtained.

Analysis of lead times of metallic components in the aerospace industry through a supported vector machine model

The aim of the present paper is the analysis of the factors that have influence over the lead time of batches of metallic components of aerospace engines. The approach used in this article employs support vector machines (SVMs). They are a set of related supervised learning methods used for classification and regression. In this research a model that estimates whether a batch is going to be finished on the forecasted time or not was developed using some sample batches. The validity of this model was checked using a different sample of similar components. This model allows predicting the manufacturing time before the start of the manufacturing. Therefore a buffer time can be taken into account in order to avoid delays with respect to the customers delivery. Further, some other researches have been performed over the data in order to determine which factors have more influence in manufacturing delays. Finally, conclusions of this study are exposed.

Support Vector Machines and Multilayer Perceptron Networks Used to Evaluate the Cyanotoxins Presence from Experimental Cyanobacteria Concentrations in the Trasona Reservoir (Northern Spain)

Cyanobacteria also known as blue-green algae can be found in almost every conceivable environment. Cyanobacteria blooms occur frequently and globally in water bodies and they are a major concern in terms of their effects on other species such as plants, fish and other microorganisms, but especially by the possible acute and chronic effects on human health due to the potential danger from cyanobacterial toxins produced by some of them in recreational or drinking waters. Consequently, anticipation of cyanotoxins presence is a matter of importance to prevent risks. The aim of this study is to build a cyanotoxin diagnostic model by using support vector machines and multilayer perceptron networks from cyanobacterial concentrations determined experimentally in the Trasona reservoir (recreational reservoir used as a high performance training centre of canoeing in the Northern Spain). The results of the present study are two-fold. In the first place, the significance of each biological and physical-chemical variables on the cyanotoxins presence in the reservoir is presented through the model. Secondly, a predictive model able to forecast the possible presence of cyanotoxins is obtained. The agreement of the model with experimental data confirmed its good performance. Finally, conclusions of this innovative research work are exposed.

A new data mining methodology applied to the modelling of the influence of diet and lifestyle on the value of bone mineral density in post-menopausal women

In this work, the application of ‘multivariate adaptive regression splines’ (MARS) for modelling osteoporosis is described. This article focuses on the explanation of a new technique that combines the use of the principal components analysis (PCA) method with MARS. The use of this new technique allows for an easier management of large databases with a lower computational cost as the PCA allows the elimination of those variables that are redundant from the point of view of the phenomena under study. Osteoporosis is characterized by low ‘bone mineral density’ (BMD). This illness has a high-cost impact in all developed countries. The aim of this article is the development of a mathematical method capable of predicting the BMD of post-menopausal women, taking into account only certain nutritional variables. A nutritional habits and lifestyle questionnaire is drawn up. The variables obtained from this, together with the BMD of the patients calculated by densitometry, are processed using the ‘principal component analysis’ (PCA) algorithm in order to reduce the size of the database. Finally, the ‘MARS method’ is applied. It has been proved to be possible to build a MARS model in order to forecast the BMD of the post-menopausal women in function of their responses to the questionnaire. This model can be used to determine which women should take a densitometry.

The use of design of experiments to improve a neural network model in order to predict the thickness of the chromium layer in a hard chromium plating process

The hard chromium plating process aims at creating a coating of hard and wear-resistant chromium with a thickness of some micrometres directly on the metal part without the insertion of copper or nickel layers. Chromium plating features high levels of hardness and resistance to wear and it is due to these properties that they can be applied in a huge range of sectors. Resistance to corrosion of a hard chromium plate depends on the thickness of its coating, and its adherence and micro-fissures. This micro-fissured structure is what provides the optimal hardness of the layers. The hard chromium plating process is one of the most effective ways of protecting the base material against a hostile environment or improving the surface properties of the base material. However, in the electroplating industry, electroplaters are faced with many problems and undesirable results with chromium plated materials. Common problems faced in the electroplating industry include matt deposition, milky white chromium deposition, rough or sandy chromium deposition and insufficient thickness and hardness. This article presents an artificial neural network (ANN) model to predict the thickness of the layer in a hard chromium plating process. The optimization of the ANN was performed by means of the design of experiments theory (DOE). In the present work the purpose of using DOE is twofold: to define the optimal experiments which maximize the ratio of the model accuracy, and to minimize the number of necessary experiments (ANN models trained and validated).

Design and finite element analysis of a wet cycle cement rotary kiln

The finite element method (FEM) is applied to the nonlinear analysis of a cement rotary kiln for the Rais Hamidou factory (Algeria). The nonlinearity is due to contact conditions between the kiln body, tyres and foundations. The FEM is first used in a reduced model of the kiln in order to obtain the meshing criterion for the global model. Then, an overall FEM analysis is performed for the different operating and live loads at different positions of the rotary kiln. Stress and displacement components are evaluated based on the ASME rules (Asme Boiler and Pressure Vessel Code. VIII. Division 2--Alternative Rules. The American Society of Mechanical Engineers, 1995). Finally, in this work, the main design criterion is the out-of-roundness values at the kiln shell so that the thickness of the kiln shell is smaller in the central bend span than the values at the tyres.

Turbidity Prediction in a River Basin by Using Artificial Neural Networks: A Case Study in Northern Spain

Chemical and physical-chemical parameters define water quality and are involved in water body type and habitat determination. They support a biological community of a certain ecological status. Water quality controls involve a large number of measurements of variables and observations according to the European Water Framework Directive (Directive 2000/60/EC). In some cases, such as areas with especially critical uses or points in which potential pollution episodes are expected, the automatic monitoring is recommended. However, the chemical and physical-chemical measurements are costly and time consuming. Turbidity is shown as a key variable for the water quality control and it is also an integrative parameter. For this reason, the aim of this work is focused on this main parameter through the study of the influence of several water quality parameters on it. The artificial neural networks (ANNs) have been used in a wide range of biological problems with promising results. Bearing this in mind, turbidity values have been predicted here by using artificial neural networks (ANNs) from the remaining measured water quality parameters with success taking into account the synergistic interactions between the input variables in the Nalón river basin (Northern Spain). Finally, the main conclusions of this study are exposed.

Study of cyanotoxins presence from experimental cyanobacteria concentrations using a new data mining methodology based on multivariate adaptive regression splines in Trasona reservoir (Northern Spain).

There is an increasing need to describe cyanobacteria blooms since some cyanobacteria produce toxins, termed cyanotoxins. These latter can be toxic and dangerous to humans as well as other animals and life in general. It must be remarked that the cyanobacteria are reproduced explosively under certain conditions. This results in algae blooms, which can become harmful to other species if the cyanobacteria involved produce cyanotoxins. In this research work, the evolution of cyanotoxins in Trasona reservoir (Principality of Asturias, Northern Spain) was studied with success using the data mining methodology based on multivariate adaptive regression splines (MARS) technique. The results of the present study are two-fold. On one hand, the importance of the different kind of cyanobacteria over the presence of cyanotoxins in the reservoir is presented through the MARS model and on the other hand a predictive model able to forecast the possible presence of cyanotoxins in a short term was obtained. The agreement of the MARS model with experimental data confirmed the good performance of the same one. Finally, conclusions of this innovative research are exposed.