Enhanced Industrial Machinery Condition Monitoring Methodology based on Novelty Detection and Multi-Modal Analysis

This paper presents a condition-based monitoring methodology based on novelty detection applied to industrial machinery. The proposed approach includes both, the classical classification of multiple a priori known scenarios, and the innovative detection capability of new operating modes not previously available. The development of condition-based monitoring methodologies considering the isolation capabilities of unexpected scenarios represents, nowadays, a trending topic able to answer the demanding requirements of the future industrial processes monitoring systems. First, the method is based on the temporal segmentation of the available physical magnitudes, and the estimation of a set of time-based statistical features. Then, a double feature reduction stage based on Principal Component Analysis and Linear Discriminant Analysis is applied in order to optimize the classification and novelty detection performances. The posterior combination of a Feed-forward Neural Network and One-Class Support Vector Machine allows the proper interpretation of known and unknown operating conditions. The effectiveness of this novel condition monitoring scheme has been verified by experimental results obtained from an automotive industry machine.Postprint (published version

Tracing the geographical origin of Argentinean lemon juices based on trace element profiles using advanced chemometric techniques

This study examines the application of chemometric techniques associated with trace element concentrations for origin evaluation of lemon juice samples. Seventy-four lemon juice samples from three different provinces of Argentina were evaluated according to their microelement contents to identify differences in patterns of elements in the three provinces. Inductively coupled plasma mass spectrometry (ICP-MS) was used for the determination of twenty-five elements (Ag, Al, As, Ba, Bi, Co, Cr, Cu, Fe, Ga, In, La, Li, Mn, Mo, Ni, Rb, Sb, Sc, Se, Sn, Sr, Tl, V, and Zn). Once the analytical data were collected, supervised pattern recognition techniques were applied to construct classification/discrimination rules to predict the origin of samples on the basis of their profiles of trace elements. Namely, linear discriminant analysis (LDA), partial least square discriminant analysis (PLS-DA), k-nearest neighbors (k-NN), random forest (RF), and support vector machine with radial basis function Kernel (SVM). The results indicated that it was feasible to attribute unknown lemon juice samples to its geographical origin. SVM had better performance compared to RF, k-NN, LDA and PLS-DA, listed in descending order. Eventually, this study verifies that trace element pattern is a powerful geographical indicator when identifying the origin of lemon juice samples by analyzing trace element data with the help of SVM technique. This level of accuracy provides an interesting foundation to propose the combination of trace element contents with SVM technique as a valuable tool to evaluate the geographical origin of lemon juice samples produced in Argentina.Fil: Gaiad, José Emilio. Universidad Nacional del Nordeste; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Química Básica y Aplicada del Nordeste Argentino. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Química Básica y Aplicada del Nordeste Argentino; ArgentinaFil: Hidalgo, Melisa Jazmin. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Química Básica y Aplicada del Nordeste Argentino. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Química Básica y Aplicada del Nordeste Argentino; Argentina. Universidad Nacional del Nordeste; ArgentinaFil: Villafañe, Roxana Noelia. Universidad Nacional de San Luis; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis. Instituto de Química de San Luis. Universidad Nacional de San Luis. Facultad de Química, Bioquímica y Farmacia. Instituto de Química de San Luis; ArgentinaFil: Marchevsky, Eduardo Jorge. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis. Instituto de Química de San Luis. Universidad Nacional de San Luis. Facultad de Química, Bioquímica y Farmacia. Instituto de Química de San Luis; Argentina. Universidad Nacional de San Luis; ArgentinaFil: Pellerano, Roberto Gerardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Química Básica y Aplicada del Nordeste Argentino. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Química Básica y Aplicada del Nordeste Argentino; Argentina. Universidad Nacional del Nordeste; Argentin