The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses data-driven algorithms, which are usually associated with the collection of data using sensors. Data are subsequently processed and analyzed. The second approach uses models to analyze information about the structure. In the latter case, the overall performance of the approach is associated with the accuracy of the model and the information that is used to define it. Although both approaches are widely used, data-driven algorithms are preferred in most cases because they afford the ability to analyze data acquired from sensors and to provide a real-time solution for decision making; however, these approaches involve high-performance processors due to the high computational cost. As a contribution to the researchers working with data-driven algorithms and applications, this work presents a brief review of data-driven algorithms for damage identification in structural health-monitoring applications. This review covers damage detection, localization, classification, extension, and prognosis, as well as the development of smart structures. The literature is systematically reviewed according to the natural steps of a structural health-monitoring system. This review also includes information on the types of sensors used as well as on the development of data-driven algorithms for damage identification.

Cerro Matoso SA (CMSA) is located in Montelibano, Colombia. It is one of the biggest producers of ferronickel in the world. The structural health monitoring process performed in the electric arc furnaces at CMSA is of great importance in the maintenance and control of ferronickel production. The control of thermal and dimensional conditions of the electric furnace aims to detect and prevent failures that may affect its physical integrity. A network of thermocouples distributed radially and at different heights from the furnace wall, are responsible for monitoring the temperatures in the electric furnace lining. In order to optimize the operation of the electric furnace, it is important to predict the temperature at some points. However, this can be difficult due the number of variables which it depends on. To predict the temperature behavior in the electric furnace lining, a deep learning model for time series prediction has been developed. Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and other combinations were tested. GRU characterized by its multivariate and multi output type had the lowest square error. A study of the best input variables for the model that influence the temperature behavior is also carried out. Some of the input variables are the power, current, impedance, calcine chemistry, temperature history, among others. The methodology to tune the parameters of the GRU deep learning model is described. Results show an excellent behavior for predicting the temperatures 6 h into the future with root mean square errors of 3%. This model will be integrated to a software that obtains data for a time window from the Distributed Control System (DCS) to feed the model. In addition, this software will have a graphical user interface used by the operators furnace in the control room. Results of this work will improve the process of structural control and health monitoring at CMSA.

Within a model of scientific and technical cooperation between the smelting company Cerro Matoso S.A. (CMSA) and the Universidad Nacional de Colombia (UNAL), a project was developed in order to take advantage of the data that were obtained from a sensor network in a ferronickel electric arc furnace at CMSA to improve the structural health monitoring process. Through this sensor network, online data are obtained on the temperature measurement along the refractory lining of the electric furnace, as well as heat fluxes and chemical characterization of the minerals on each stage of the process. These data are stored in a local database, which stores several years of historical data with valuable information for control and analysis purposes. These data reflect the behavior of the industrial process and can be used in the development of machine learning models to predict some of the electric arc furnace operation parameters, and thus improve the decision-making process. Currently, most of the data are analyzed by the experts of the structural control department, but, due to the large amount of data, the development of analytical tools is necessary to support their work. This paper proposes a data cleaning approach for improving data quality by creating a set of rules and filters based on both expert judgment and best practices in data quality. A statistical analysis was also carried out in order to detect variables with anomalies and outliers, which do not reflect real operation parameters and belong to anomalous data that should not be considered for modelling. With the proposed process, the quality of the data was improved and abnormal data were eliminated in order to consolidate a clean data set for later use in the development of machine learning models. This work contributes on understanding data cleansing rules that must be considered in order to reflect the real behavior of the electric furnace operation for further analysis and modeling tasks.