Carlos Arthur Mattos Teixeira Cavalcante

Pattern Recognition using Multivariate Time Series for Fault Detection in a Thermoeletric Unit

Abstract This paper presents a methodology for recognition of operating patterns of a gas turbine in a thermoelectric power plant (Brazilian Oil Company). Patterns related to the normal starts (without failure) and starts with failure (trip) were recognized. The process data were obtained from the plant information management system (PIMS) and techniques of data mining suitable for multivariable time series were adopted with emphasis on similarity metrics, linear scan and clustering, among others. The recognized patterns represent important and useful results to support the development of dynamic system for the monitoring and predicting the probability of failure in the equipment.

5.14 Patterns Recognition in Energy Management

This chapter aims to discuss issues related to the unsupervised recognition of patterns in the general scope and in particular the problems belonging to the electric sector. In this aspect, an initial discussion was made on the main concepts and procedures of the clustering analysis. For time factor on group formation, some clustering algorithms are required. In engineering, pattern recognition in time series has been used for the detection and diagnosis of faults, optimization of trajectories, and characterization of consumption profiles, mainly electric power. It is worth mentioning that univariate time series (STU) clustering algorithms use standard approaches using point-prototype grouping models. However, pattern recognition in multivariate time series (STM) represents a more complex problem (nonpoint-prototype problem) with intrinsic characteristics. This chapter presents a method for the selection, typification, and clustering of STU capable of recognizing consumption patterns in the electricity sector. For the STM, a special clustering method was proposed based on multivariate statistics. Both the method was successfully implemented and tested in the context of an energy efficiency program carried out by the Energy Company of Maranhao and Alagoas (Brazil), respectively. The results reveal the viability of the method in recognizing patterns consistent with the reality of the electricity sector. The proposed method is also useful to support decision-making at management level.

Using OPC and HL7 Standards to Incorporate an Industrial Big Data Historian in a Health IT Environment

Health Level Seven (HL7) is one of the standards most used to centralize data from different vital sign monitoring systems. This solution significantly limits the data available for historical analysis, because it typically uses databases that are not effective in storing large volumes of data. In industry, a specific Big Data Historian, known as a Process Information Management System (PIMS), solves this problem. This work proposes the same solution to overcome the restriction on storing vital sign data. The PIMS needs a compatible communication standard to allow storing, and the one most commonly used is the OLE for Process Control (OPC). This paper presents a HL7-OPC Server that permits communication between vital sign monitoring systems with PIMS, thus allowing the storage of long historical series of vital signs. In addition, it carries out a review about local and cloud-based Big Medical Data researches, followed by an analysis of the PIMS in a Health IT Environment. Then it shows the architecture of HL7 and OPC Standards. Finally, it shows the HL7-OPC Server and a sequence of tests that proved its full operation and performance.

An optimization model for rates of socially fairer property tax

En este trabajo se presenta un modelo de optimizacion matematica no lineal que determinan las nuevas tarifas del Impuesto Inmobiliario Urbano - Impuesto sobre la propiedad aplicado en apartamentos residenciales en la ciudad de Salvador (BA). Ellos se consideran la progresividad de los tipos impositivos, el mercado de valores de las propiedades, los ingresos medios de los contribuyentes y de los metodos habituales de calculo de la cuantia del impuesto. Los resultados demuestran que la aplicacion de este modelo se puede tratar de manera objetiva y socialmente mas justo fijar los tipos de impuestos a la propiedad para todos los tipos de propiedades en un municipio.

Um modelo de otimização para alíquotas do IPTU socialmente mais justas

En este trabajo se presenta un modelo de optimizacion matematica no lineal que determinan las nuevas tarifas del Impuesto Inmobiliario Urbano - Impuesto sobre la propiedad aplicado en apartamentos residenciales en la ciudad de Salvador (BA). Ellos se consideran la progresividad de los tipos impositivos, el mercado de valores de las propiedades, los ingresos medios de los contribuyentes y de los metodos habituales de calculo de la cuantia del impuesto. Los resultados demuestran que la aplicacion de este modelo se puede tratar de manera objetiva y socialmente mas justo fijar los tipos de impuestos a la propiedad para todos los tipos de propiedades en un municipio.

Un modelo de optimización de las tasas de impuestos a la propiedad socialmente más justo

En este trabajo se presenta un modelo de optimizacion matematica no lineal que determinan las nuevas tarifas del Impuesto Inmobiliario Urbano - Impuesto sobre la propiedad aplicado en apartamentos residenciales en la ciudad de Salvador (BA). Ellos se consideran la progresividad de los tipos impositivos, el mercado de valores de las propiedades, los ingresos medios de los contribuyentes y de los metodos habituales de calculo de la cuantia del impuesto. Los resultados demuestran que la aplicacion de este modelo se puede tratar de manera objetiva y socialmente mas justo fijar los tipos de impuestos a la propiedad para todos los tipos de propiedades en un municipio.