Abel A. Cuadrado

A new approach to exploratory analysis of system dynamics using SOM. Applications to industrial processes

The self-organizing map (SOM) constitutes a powerful method for exploratory analysis of process data that is based on the so-called dimension reduction approach. The SOM algorithm defines a smooth non-linear mapping from a high-dimensional input space onto a low-dimensional output space (typically 2D) that preserves the most significant information about the input data distribution. This mapping can be used to obtain 2D representations (component planes, u-matrix, etc.) of the process variables that reveal the main static relationships, allowing to exploit available data and process-related knowledge in an efficient way for supervision and optimization purposes. In this work we present a complementary methodology to represent also the process dynamics in the SOM visualization, using maps in which every point represents a local dynamical behavior of the process and that, in addition, are consistent with the component planes of the process variables. The proposed methodology allows in this way to find relationships between the process variables and the process dynamics, opening important ways for the exploratory analysis of the dynamic behavior in non-linear and non-stationary processes. Experimental results from real data of two different industrial processes are also described, showing the possibilities of the proposed approach.

Visual dynamic model based on self-organizing maps for supervision and fault detection in industrial processes

Visual data mining techniques have experienced a growing interest for processing and interpretation of the large amounts of multidimensional data available in current industrial processes. One of the approaches to visualize data is based on self-organizing maps (SOM), which define a projection of the input space onto a 2D or 3D space that can be used to obtain visual representations. Although these techniques have been usually applied to visualize static relations among the process variables, they have proven to be very useful to display dynamic features of the processes. In this work, an approach based on the SOM to model the dynamics of multivariable processes is presented. The proposed method identifies the process conditions (clusters) and the probabilities of transition among them, using the trajectory followed by the input data on the 2D visualization space. Furthermore, a new method of residual computation for fault detection and identification that uses the dynamic information provided by the model of transitions is proposed. The proposed method for modeling and fault identification has been applied to supervise a real industrial plant and the results are included.

Internet-based remote supervision of industrial processes using self-organizing maps

In upcoming years, the strategies for maintenance, traceability, management and operation of productive processes will demand the use of novel information and communication technologies. Supervisory systems in these new scenarios will have to be able to integrate large volumes of information and knowledge coming both from local and remote points of large processes. These systems will therefore require new tools for management and integration of information and knowledge. In this work, the authors present an internet-based remote supervision system of industrial processes that incorporates powerful data and knowledge visualization tools based on selforganizing maps (SOM). This architecture adds an intermediate layer (database) to the well-known client and server layers, that isolates the client part from the industrial process, allowing to incorporate the required data management and neural network processing tasks. Remote users have access to advanced information visualization tools based on SOM, including both static visualizations, such as component planes or distance maps, and dynamical ones, such as residuals and state trajectory, allowing the interpretation of knowledge extracted by the SOM as well as the analysis and detection of possible abnormal conditions. This architecture has been validated through the supervision of an industrial pilot plant. r 2006 Elsevier Ltd. All rights reserved.

Visual data mining and monitoring in steel processes

Steel processes are often of a complex nature and difficult to model. All information that we have at hand usually consists of more or less precise models of different parts of the process, some rules obtained on the basis of experience, and typically a great amount of high-dimensional data coming from numerous sensors and variables of process computers which convey a lot of information about the process state. We suggest in this paper the use of a continuous version of the self-organizing map (SOM) to project a high dimensional vector of process data on a 2D visualization space in which different process conditions are represented by different regions. Later, all sorts of information resulting from the fusion of knowledge obtained from data, mathematical models and fuzzy rules can be described in a graphical way in this visualization space.

A visual approach for fuzzy rule induction

Models are descriptions of real facts that serve us to think and reason. In building models, a compromise always exists between accuracy, that is, how precisely the model describes reality, and simplicity, without which the model would be useless. So, a good model must be simple and intuitive while being accurate enough. In this paper we propose a novel approach based on visual techniques aiming to help the human in fine-tuning fuzzy decision trees to enhance its interpretability and insightfulness with a minimal loss of accuracy. By involving the human in the design process, these techniques allow to include prior knowledge in the selection of membership functions as well as to assess the significance of rules in the model to help in the pruning stage.

Visual analysis of a cold rolling process using a dimensionality reduction approach

The rolling process is a strategical industrial and economical activity that has a large impact among world-wide commercial markets. Typical operating conditions during the rolling process involve extreme mechanical situations, including large values of forces and tensions. In some cases, these scenarios can lead to several kinds of faults, which might result in large economic losses. Thereby, a proper assessment of the process condition is a key aspect, not only as a fault detection mechanism, but also as an economic saving system. In the rolling process, a remarkable kind of fault is the so-called chatter, a sudden powerful vibration that affects the quality of the rolled material. In this paper, we propose a visual approach for the analysis of the rolling process. According to physical principles, we characterize the exit thickness and the rolling forces by means of a large dimensional feature vector, that contains the energies at specific frequency bands. Afterwards, we use a dimensionality reduction technique, called t-SNE, to project all feature vectors on a visual 2D map that describes the vibrational states of the process. The proposed methodology provides a way for an exploratory analysis of the dynamic behaviors in the rolling process and allows to find relationships between these behaviors and the chatter fault. Experimental results from real data of a cold rolling mill are described, showing the application of the proposed approach.

Analysis of electric power consumption using Self-Organizing Maps.

Abstract Self-organizing maps (SOM) are excellent tools to extract and visualize information from large scale systems. In this paper, these maps are used to analyze data from an electric power system in a group of buildings. An experiment has been proposed to study the electric power consumption in these buildings according to the electricity tariff in order to achieve energy and economic savings. The input space of the SOM is mainly composed of a set of electric, meteorological and time period variables. Component planes and hit maps have been used for visualization. It has been proven that hit maps produce a close approximation to the real energy consumption.

Thickness quality control

This article describes a series of methods to detect thickness defects in DWI tinplate. These methods provide information to identify the origin of the defects, allowing fast corrections and, therefore, improving mill performance. In this approach, an adapted thickness signal is spatially sampled from the input stages of the mills last X-ray gauge. The system is also able to detect other problems related to thickness quality such as third-octave chatter using only the thickness measures from an X-ray gauge. The usage of this system resulted in an improvement in the factory performance and considerable money savings.

Visual Analysis of a Cold Rolling Process Using Data-Based Modeling

In this paper, a method to characterize the chatter phenomenon in a cold rolling process is proposed. This approach is based on obtaining a global nonlinear dynamical MISO model, relating four input variables and the exit strip thickness as the output variable. In a second stage, local linear models are obtained for all working points using sensitivity analysis on the nonlinear model to get input/output small signal models. Each local model is characterized by a high dimensional vector containing the frequency response functions (FRF) of the four SISO resulting models. Finally, the FRF’s are projected on a 2D space, using the t-SNE algorithm, in order to visualize the dynamical changes of the process. Our results show a clear separation between chatter condition and other vibration states, allowing an early detection of chatter as well as being a visual analysis tool to study the chatter phenomenon.

VISUAL ANALYSIS OF RESIDUALS FROM DATA-BASED MODELS IN COMPLEX INDUSTRIAL PROCESSES

The use of data-based models for visualization purposes in an industrial background is discussed. Results using Self-Organizing Maps (SOM) show how through a good design of the model and a proper visualization of the residuals generated by the model itself, the behavior of essential parameters of the process can be easily tracked in a visual way. Real data from a cold rolling facility have been used to prove the advantages of these techniques.