A wavelet-based multivariable approach for fault detection in dynamic systems

This paper presents a multivariable extension to a recently proposed wavelet-based technique for fault detection. In the original formulation, the Discrete Wavelet Transform is used to carry out dynamic consistency checks between pairs of signals within frequency subbands. For this purpose, moving average models with an integrative term are employed to reproduce the dynamics of the system in each subband under consideration. The present work introduces a new architecture allowing the use of subband models with more general multivariable structures. More specifically, a multivariable ARX (autoregressive with exogenous input) structure is adopted for each subband model. The proposed technique is illustrated in a case study involving a nonlinear simulation model for an aircraft with a sensor fault. The results show that the multivariable approach outperforms the original formulation in terms of residue amplification following the fault onset

Development of a Rapid Soil Water Content Detection Technique Using Active Infrared Thermal Methods for In-Field Applications

The aim of this study was to investigate the suitability of active infrared thermography and thermometry in combination with multivariate statistical partial least squares analysis as rapid soil water content detection techniques both in the laboratory and the field. Such techniques allow fast soil water content measurements helpful in both agricultural and environmental fields. These techniques, based on the theory of heat dissipation, were tested by directly measuring temperature dynamic variation of samples after heating. For the assessment of temperature dynamic variations data were collected during three intervals (3, 6 and 10 s). To account for the presence of specific heats differences between water and soil, the analyses were regulated using slopes to linearly describe their trends. For all analyses, the best model was achieved for a 10 s slope. Three different approaches were considered, two in the laboratory and one in the field. The first laboratory-based one was centred on active infrared thermography, considered measurement of temperature variation as independent variable and reported r = 0.74. The second laboratory–based one was focused on active infrared thermometry, added irradiation as independent variable and reported r = 0.76. The in-field experiment was performed by active infrared thermometry, heating bare soil by solar irradiance after exposure due to primary tillage. Some meteorological parameters were inserted as independent variables in the prediction model, which presented r = 0.61. In order to obtain more general and wide estimations in-field a Partial Least Squares Discriminant Analysis on three classes of percentage of soil water content was performed obtaining a high correct classification in the test (88.89%). The prediction error values were lower in the field with respect to laboratory analyses. Both techniques could be used in conjunction with a Geographic Information System for obtaining detailed information on soil heterogeneity

Nitrogen Concentration Estimation in Tomato Leaves by VIS-NIR Non-Destructive Spectroscopy

Nitrogen concentration in plants is normally determined by expensive and time consuming chemical analyses. As an alternative, chlorophyll meter readings and N-NO3 concentration determination in petiole sap were proposed, but these assays are not always satisfactory. Spectral reflectance values of tomato leaves obtained by visible-near infrared spectrophotometry are reported to be a powerful tool for the diagnosis of plant nutritional status. The aim of the study was to evaluate the possibility and the accuracy of the estimation of tomato leaf nitrogen concentration performed through a rapid, portable and non-destructive system, in comparison with chemical standard analyses, chlorophyll meter readings and N-NO3 concentration in petiole sap. Mean reflectance leaf values were compared to each reference chemical value by partial least squares chemometric multivariate methods. The correlation between predicted values from spectral reflectance analysis and the observed chemical values showed in the independent test highly significant correlation coefficient (r = 0.94). The utilization of the proposed system, increasing efficiency, allows better knowledge of nutritional status of tomato plants, with more detailed and sharp information and on wider areas. More detailed information both in space and time is an essential tool to increase and stabilize crop quality levels and to optimize the nutrient use efficiency

Optimization of Wavelet Filters for Parity Relation-Based Fault Detection

This paper revisits a wavelet approach for parity relation-based fault detection and proposes an improvement through the adaptation of the wavelet filters employed in the decomposition of the residue signal. In the parity space approach under consideration, the parity vector is obtained by minimizing a cost that expresses a compromise between sensitivity to faults and robustness against external disturbances. The proposed improvement consists of optimizing the wavelet filter parameters in order to further reduce the resulting cost value. An example involving the model of a two-mass-spring system is presented for illustration. The results show that the proposed filter optimization procedure results in a larger increase of the residue following the onset of a fault, without introducing additional time delays in the detection process.FAPESPCNPqUniv Fed Sao Paulo, Inst Sci & Technol, BR-12231280 Sao Jose Dos Campos, SP, BrazilInst Tecnol Aeronaut, Dept Elect Engn, BR-12228900 Sao Jose Dos Campos, SP, BrazilUniv Estadual Mato Grosso Sul, Dept Math, BR-79540000 Cassilandia, MS, BrazilUniv Fed Sao Paulo, Inst Sci & Technol, BR-12231280 Sao Jose Dos Campos, SP, BrazilFAPESP: 2011/17610-0CNPq: 303714/2014-0Web of Scienc

A two-dimensional RC network topology for fault-tolerant design of analog circuits

This paper proposes a novel one-port passive circuit topology consisting of a two-dimensional network of resistors and capacitors, which can be used as a fault-tolerant building block for analog circuit design. Through an analytical procedure, the network is shown to follow simple first-order admittance dynamics. A Monte Carlo method is employed to describe the effect of simultaneous faults (short or open circuit) in random network elements in terms of confidence bounds in the frequency-domain admittance profile. Faults in 10% of the elements resulted in only minor changes of the frequency response (up to 3.9 dB in magnitude and 12.5 ∘ in phase in 95% of the cases). An example is presented to illustrate the use of the proposed RC network in the faulttolerant design of a low-pass filter

Bond strengths, degree of conversion of the cement and molecular structure of the adhesive-dentine joint in fibre post restorations

Objectives: Because fibre post restorations are influenced by multiple factors such as the types of bonding materials, the dentine region and the time under moist exposure, this study sought to determine the bond strength of endodontic restorations and its relation to the degree of conversion of the cement layer and the molecular structure of the dentine-bonded joints.Methods: The performance of 2 etch-and-rinse (All-Bond 2 and One-Step Plus) and 2 self-etch (Clearfil SE Bond and Xeno III) adhesives at post spaces regions, after 7 d or 4 m, was evaluated. FRC Postec Plus posts were cemented to the root canal with a dual-cure resin cement (Duo-Link). Transverse sections of the tooth were subjected to push-out testing, to degree-of-conversion measurements and to hybrid layer evaluation through mu-Raman spectroscopy.Results: Coronal bonding was higher than cervical and middle bonding. The hybrid layer was thicker for the etch-and-rinse systems, with thicknesses decreasing towards the middle region. The degree of conversion measured for the 3-step etch-and-rinse group after 4 m was significantly higher than that for the self-etching groups.Conclusions: Although not totally stable at the adhesive-dentine interface, the 3-step etch-and-rinse adhesive in the coronal dentine provided the best bond strength, degree of conversion of the cement and hybrid layer thickness in post restorations, in both short- and long-term analyses. (C) 2012 Elsevier Ltd. All rights reserved.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq

Binary classification of chalcone derivatives with LDA or KNN based on their antileishmanial activity and molecular descriptors selected using the Successive Projections Algorithm feature-selection technique

Chalcones are naturally occurring aromatic ketones, which consist of an α-, β-unsaturated carbonyl system joining two aryl rings. These compounds are reported to exhibit several pharmacological activities, including antiparasitic, antibacterial, antifungal, anticancer, immunomodulatory, nitric oxide inhibition and anti-inflammatory effects. In the present work, a Quantitative Structure–Activity Relationship (QSAR) study is carried out to classify chalcone derivatives with respect to their antileishmanial activity (active/inactive) on the basis of molecular descriptors. For this purpose, two techniques to select descriptors are employed, the Successive Projections Algorithm (SPA) and the Genetic Algorithm (GA). The selected descriptors are initially employed to build Linear Discriminant Analysis (LDA) models. An additional investigation is then carried out to determine whether the results can be improved by using a non-parametric classification technique (One Nearest Neighbour, 1NN). In a case study involving 100 chalcone derivatives, the 1NN models were found to provide better rates of correct classification than LDA, both in the training and test sets. The best result was achieved by a SPA–1NN model with six molecular descriptors, which provided correct classification rates of 97% and 84% for the training and test sets, respectively.publisher: Elsevier articletitle: Binary classification of chalcone derivatives with LDA or KNN based on their antileishmanial activity and molecular descriptors selected using the Successive Projections Algorithm feature-selection technique journaltitle: European Journal of Pharmaceutical Sciences articlelink: http://dx.doi.org/10.1016/j.ejps.2013.09.019 content_type: article copyright: Copyright © 2013 Elsevier B.V. All rights reserved.status: publishe

Cross-validation for the selection of spectral variables using the successive projections algorithm

This work compares the use of a separate validation set and leave-one-out cross-validation to guide the selection of variables in the Successive Projections Algorithm (SPA) for multivariate calibration. Two case studies involving diesel and corn analysis by NIR spectrometry are presented. A graphical interface for SPA is available at www.ele.ita.br/similar to kawakami/spa/

A variable elimination method to improve the parsimony of MLR models using the successive projections algorithm

The successive projections algorithm (SPA) is a variable selection technique designed to minimize collinearity problems in multiple linear regression (MLR). This paper proposes a modification to the basic SPA formulation aimed at further improving the parsimony of the resulting MLR model. For this purpose, an elimination procedure is incorporated to the algorithm in order to remove variables that do not effectively contribute towards the prediction ability of the model as indicated by an F-test. The utility of the proposed modification is illustrated in a simulation study, as well as in two application examples involving the analysis of diesel and com samples by near-infrared (NIR) spectroscopy. The results demonstrate that the number of variables selected by SPA can be reduced without significantly compromising prediction performance. In addition, SPA is favourably compared with classic Stepwise Regression and full-spectrum PLS. A graphical user interface for SPA is available at www.ele.ita.br/similar to kawakami/spa/. (C) 2008 Elsevier B.V. All rights reserved