Artículos en Revistas

Permanent URI for this collectionhttp://48.217.138.120/handle/20.500.12272/538

Browse

Author: search.filters.author.Godoy, José LuisHas files: Yes

Search Results

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    A fault detection and diagnosis technique for multivariate processes using a PLS-decomposition of the measurement space
    (2013) Vega, Jorge Ruben; Godoy, José Luis; Marchetti, Jacinto
    A newstatisticalmonitoring technique based on partial least squares (PLS) is proposed for fault detection and di- 24 agnosis inmultivariate processes that exhibit collinearmeasurements. A typical PLS regression (PLSR)modeling 25 strategy is first extended by adding the projections of the model outputs to the latent space. Then, a PLS- 26 decomposition of the measurements into four terms that belongs to four different subspaces is derived. In 27 Q2 order to online monitor the PLS-projections in each subspace, new specific statistics with non-overlapped do- 28 mains are combined into a single index able to detect process anomalies. To reach a complete diagnosis, a further 29 decomposition of each statistic was defined as a sum of variable contributions. By adequately processing all this 30 information, the technique is able to: i) detect an anomaly through a single combined index, ii) diagnose the 31 anomaly class from the observed pattern of the four component statistics with respect to their respective confi- 32 dence intervals, and iii) identify the disturbed variables based on the analysis of themain variable contributions 33 to each of the four subspaces. The effectiveness observed in the simulated examples suggests the potential appli- 34 cation of this technique to real production systems.
  • Thumbnail Image
    Item
    New contributions to non linear process monitoring through kernel partial least squares
    (2013) Vega, Jorge Ruben; Godoy, José Luis; Marchetti, Jacinto; Zumoffen, David
    The kernel partial least squares (KPLS) method was originally focused on soft-sensor calibration for predicting online quality attributes. In this work, an analysis of the KPLS-based modeling technique and its application to nonlinear process monitoring are presented. To this effect, the measurement decomposition, the development of new specific statistics acting on non-overlapped domains, and the contribution analysis are addressed for purposes of fault detection, diagnosis, and prediction risk assessment. Some practical insights for synthesizing the models are also given, which are related to an appropriate order selection and the adoption of the kernel function parameter. A proper combination of scaled statistics allows the definition of an efficient detection index for monitoring a nonlinear process. The effectiveness of the proposed methods is confirmed by using simulation examples. Keywords: KPLS Modeling, Fault Detection, Fault Diagnosis, Prediction Risk Assessment, Nonlinear Processes.
  • Thumbnail Image
    Item
    Relationships between PCA and PLS-regression
    (Revista Chem And Intell Lab Syst, 2014) Vega, Jorge Rubén; Godoy, José Luis; Marchetti, Jacinto L.
    This work aims at comparing several features of Principal Component Analysis (PCA) and Partial Least Squares Regression (PLSR), as techniques typically utilized for modeling, output prediction, and monitoring of multivariate processes. First, geometric properties of the decomposition induced by PLSR are described in relation to the PCA of the separated input and output data (X-PCA and Y-PCA, respectively). Then, analogies between the models derived with PLSR and YX-PCA (i.e., PCA of the joint input–output variables) are presented; and regarding to process monitoring applications, the specific PLSR and YX-PCA fault detection indices are compared. Numerical examples are used to illustrate the relationships between latent models, output predictive models, and fault detection indices. The three alternative approaches (PLSR, YX-PCA and Y-PCA plus X-PCA) are compared with regard to their use for statistical modeling. In particular, a case study is simulated and the results are used for enhancing the comprehension of the PLSR properties and for evaluating the discriminatory capacity of the fault detection indices based on the PLSR and YX-PCA modeling alternatives. Some recommendations are given in order to choose the more appropriate approach for a specific application: 1) PLSR and YX-PCA have similar capacity for fault detection, but PLSR is recommended for process monitoring because it presents a better diagnosing capability; 2) PLSR is more reliable for output prediction purposes (e.g., for soft sensor development); and 3) YX-PCA is recommended for the analysis of latent patterns imbedded in datasets.