An evaluation system of the setting up of predictive maintenance programmes

Predictive Maintenance can provide an increase in safety, quality and availability in industrial plants. However, the setting up of a Predictive Maintenance Programme is a strategic decision that until now has lacked analysis of questions related to its setting up, management and control. In this paper, an evaluation system is proposed that carries out the decision making in relation to the feasibility of the setting up. The evaluation system uses a combination of tools belonging to operational research such as: Analytic Hierarchy Process, decision rules and Bayesian tools. This system is a help tool available to the managers of Predictive Maintenance Programmes which can both increase the number of Predictive Maintenance Programmes set up and avoid the failure of these programmes. The Evaluation System has been tested in a petrochemical plant and in a food industry.     

Determination of the density of zinc powders for alkaline battery

On May 4th,2006,the international publication of a patentapplicationtitled“alloyed zinc powders for alkaline batteries withhigh Pyknometer density”WO2006/045470 A1[1],appeared·Theintention of this investigation was to see whether it is possible torepro…     

Identification of Oxidative Stress Related Proteins as Biomarkers for Lung Cancer and Chronic Obstructive Pulmonary Disease in Bronchoalveolar Lavage

Lung cancer (LC) and chronic obstructive pulmonary disease (COPD) commonly coexist in smokers, and the presence of COPD increases the risk of developing LC. Cigarette smoke causes oxidative stress and an inflammatory response in lung cells, which in turn may be involved in COPD and lung cancer development. The aim of this study was to identify differential proteomic profiles related to oxidative stress response that were potentially involved in these two pathological entities. Protein content was assessed in the bronchoalveolar lavage (BAL) of 60 patients classified in four groups: COPD, COPD and LC, LC, and control (neither COPD nor LC). Proteins were separated into spots by two dimensional polyacrylamide gel electrophoresis (2D-PAGE) and examined by matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF/TOF). A total of 16 oxidative stress regulatory proteins were differentially expressed in BAL samples from LC and/or COPD patients as compared with the control group. A distinct proteomic reactive oxygen species (ROS) protein signature emerged that characterized lung cancer and COPD. In conclusion, our findings highlight the role of the oxidative stress response proteins in the pathogenic pathways of both diseases, and provide new candidate biomarkers and predictive tools for LC and COPD diagnosis.     

Capacity gain of an uplink-synchronous WCDMA system under channelization code constraints

The performance of an uplink-synchronous wide-band code-division multiple-access (WCDMA) system is evaluated for radio environments with low temporal dispersion. The capacity gain of synchronous WCDMA is evaluated theoretically under certain constraints and by means of extensive dynamic system level simulations for more advanced scenarios. The effect of channelization code shortage, the impact of the dispersive radio channel on the orthogonality of received signals, and soft handover are some of the considered effects. The potential capacity gain is found to equal 35.8% in a multicell scenario, conditioned on an infinite number of channelization codes per cell. For a more realistic scenario with channelization code constraints, the capacity gain is reduced to 9.6%. The absolute number of users per cell, relative to the available number of channelization codes within each scrambling code group, is therefore found to be an important metric. This further suggests that the capacity gain of synchronous WCDMA decreases when other capacity-enhancing techniques are deployed, such as uplink antenna diversity, soft handover, voice activity detection, etc. The presented simulation results in the case where soft handover is not considered accurately match the analytical findings.     

Rho proteins induce metastatic properties in vivo

Rho proteins have been implicated in the regulation of multiple signal transduction processes. Some of the members of this family, including the rho gene from Aplysia californica and the human genes (rhoA, rhoB and rac-1), are proto-oncogenes since when properly mutated they can induce cell transformation, and the generated rho-transformed cells are tumorigenic when inoculated into mice. In addition to their tumorigenic activity, there is evidence suggesting that Rho proteins may contribute to the metastatic phenotype. However, all the experiments implicating Rho proteins or Rho-regulating proteins in the induction of metastatic potential are either indirect or have been performed in vitro. In this study we investigated whether cells transformed by rho oncogenes do have metastatic potential in vivo. We present evidence that cells transformed by the Aplysia californica rho gene, when injected directly into the blood stream are able to efficiently colonize lungs and secondary organs, consistent with the acquisition of the metastatic potential. Moreover, tumors derived from subcutaneous injections of these rho-transformed cells are also able to metastasize in distant organs, a strong support to the hypothesis that Rho proteins play a role in the metastatic phenotype. Finally, cells transformed by the human oncogenes dbl, vav and ost, three well-known guanine exchange factors for members of the Rho family, or cells transformed by the activated human rac-1 or rhoA genes do also have metastatic potential when injected into the blood stream. These results demonstrate that signaling pathways regulated by Rho proteins play an important role in the acquisition of the metastatic phenotype in vivo.     

Effects of outliers on the identification and estimation of GARCH models

Abstract. This paper analyses how outliers affect the identification of conditional heteroscedasticity and the estimation of generalized autoregressive conditionally heteroscedastic (GARCH) models. First, we derive the asymptotic biases of the sample autocorrelations of squared observations generated by stationary processes and show that the properties of some conditional homoscedasticity tests can be distorted. Second, we obtain the asymptotic and finite sample biases of the ordinary least squares (OLS) estimator of ARCH(p) models. The finite sample results are extended to generalized least squares (GLS), maximum likelihood (ML) and quasi‐maximum likelihood (QML) estimators of ARCH(p) and GARCH(1,1) models. Finally, we show that the estimated asymptotic standard deviations are biased estimates of the sample standard deviations.     

Perceptual speech coding and enhancement using frame-synchronizedfast wavelet packet transform algorithms

This paper presents new wideband speech coding and integrated speech coding-enhancement systems based on frame-synchronized fast wavelet packet transform algorithms. It also formulates temporal and spectral psychoacoustic models of masking adapted to wavelet packet analysis. The algorithm of the proposed FFT-like overlapped block orthogonal wavelet packet transform permits us to efficiently approximate the auditory critical band decomposition in the time and frequency domains. This allows us to make use of the temporal and spectral masking properties of the human auditory system to decrease the average bit rate of the encoder while perceptually hiding the quantization error. The same wavelet packet representation is used to merge speech enhancement and coding in the context of auditory modeling. The advantage of the method presented in this paper over previous approaches is that perceptual enhancement and coding, which is usually implemented as a cascade of two separate systems, are combined. This leads to a decreased computational load. Experiments show that the proposed wideband coding procedure by itself can achieve transparent coding of speech signals sampled at 16 kHz at an average bit rate of 39.4 kbit/s. The combined speech coding-enhancement procedure achieves higher bit rate values that depend on the residual noise characteristics at the output of the enhancement process     

Self-Assembly,Surface Activity and Structure of n-Octyl-β-D-thioglucopyranoside in Ethylene Glycol-Water Mixtures

The effect of the addition of ethylene glycol (EG) on the interfacial adsorption and micellar properties of the alkylglucoside surfactant n-octyl-β-d-thioglucopyranoside (OTG) has been investigated. Critical micelle concentrations (cmc) upon EG addition were obtained by both surface tension measurements and the pyrene 1:3 ratio method. A systematic increase in the cmc induced by the presence of the co-solvent was observed. This behavior was attributed to a reduction in the cohesive energy of the mixed solvent with respect to pure water, which favors an increase in the solubility of the surfactant with EG content. Static light scattering measurements revealed a decrease in the mean aggregation number of the OTG micelles with EG addition. Moreover, dynamic light scattering data showed that the effect of the surfactant concentration on micellar size is also controlled by the content of the co-solvent in the system. Finally, the effect of EG addition on the microstructure of OTG micelles was investigated using the hydrophobic probe Coumarin 153 (C153). Time-resolved fluorescence anisotropy decay curves of the probe solubilized in micelles were analyzed using the two-step model. The results indicate a slight reduction of the average reorientation time of the probe molecule with increasing EG in the mixed solvent system, thereby suggesting a lesser compactness induced by the presence of the co-solvent.     

Statistical quality control through overall vibration analysis

The present study introduces the concept of statistical quality control in automotive wheel bearings manufacturing processes. Defects on products under analysis can have a direct influence on passengers’ safety and comfort. At present, the use of vibration analysis on machine tools for quality control purposes is not very extensive in manufacturing facilities.Noise and vibration are common quality problems in bearings. These failure modes likely occur under certain operating conditions and do not require high vibration amplitudes but relate to certain vibration frequencies. The vibration frequencies are affected by the type of surface problems (chattering) of ball races that are generated through grinding processes.The purpose of this paper is to identify grinding process variables that affect the quality of bearings by using statistical principles in the field of machine tools. In addition, an evaluation of the quality results of the finished parts under different combinations of process variables is assessed. This paper intends to establish the foundations to predict the quality of the products through the analysis of self-induced vibrations during the contact between the grinding wheel and the parts. To achieve this goal, the overall self-induced vibration readings under different combinations of process variables are analysed using statistical tools. The analysis of data and design of experiments follows a classical approach, considering all potential interactions between variables. The analysis of data is conducted through analysis of variance (ANOVA) for data sets that meet normality and homoscedasticity criteria. This paper utilizes different statistical tools to support the conclusions such as chi squared, Shapiro–Wilks, symmetry, Kurtosis, Cochran, Hartlett, and Hartley and Krushal–Wallis.The analysis presented is the starting point to extend the use of predictive techniques (vibration analysis) for quality control. This paper demonstrates the existence of predictive variables (high-frequency vibration displacements) that are sensible to the processes setup and the quality of the products obtained. Based on the result of this overall vibration analysis, a second paper will analyse self-induced vibration spectrums in order to define limit vibration bands, controllable every cycle or connected to permanent vibration-monitoring systems able to adjust sensible process variables identified by ANOVA, once the vibration readings exceed established quality limits.     

State space models for condition monitoring: a case study

A Condition Monitoring system can increase safety, quality and availability in industrial plants. Safety requirements are especially important in critical machineries, like a turbine driving a centrifugal compressor located at a petrochemical plant in the case study presented in this paper. A Condition Monitoring system is set up for vibration data coming from the turbine. Four years of monthly data observed at two different locations of the equipment are analysed. The core of the system is a model to forecast the state of the machine using data provided by the Condition Monitoring system at each moment in time. The model is based on the State Space framework whose associated recursive algorithms (Kalman Filter and Fixed Interval Smoothing) provide the basis for a number of different operations, from which the most important in the present context is the extrapolation of the distribution of forecasts on which the probability of failure is estimated. The cost model on which the decision of making a preventive replacement is taken is based on the ‘expected cost per unit time’ for a pre-determined critical value of the vibration measure. The system is thoroughly tested on the data.