The Self-organizing map as a tool in knowledge engineering
Pattern recognition in soft computing paradigm
Information Visualization and Visual Data Mining
IEEE Transactions on Visualization and Computer Graphics
Novelty detection: a review—part 1: statistical approaches
Signal Processing
Internet-based remote supervision of industrial processes using self-organizing maps
Engineering Applications of Artificial Intelligence
Process Monitoring and Modeling Using the Self-Organizing Map
Integrated Computer-Aided Engineering
Expert Systems with Applications: An International Journal
ACM Computing Surveys (CSUR)
Engineering Applications of Artificial Intelligence
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Today's large scale availability of data from industrial plants is an invaluable resource to monitor industrial processes. Data-based methods can lead to better understanding, optimization or detection of anomalies. As a particular case, batch processes have attracted special interest due to their widespread presence in the industry. The aim of monitoring, in this case, is to compare different runs or implementations of a process with the baseline or normal operating one. On the other hand, visual exploration tools for process monitoring have been a prolific application field for self-organizing maps (SOM). In this paper, we exploit data-based models, obtained by means of SOM, for the visual comparison of industrial processes. For that purpose, we propose a method that defines a new visual exploration tool, called dissimilarity map. We also expose the need to consider dynamic information for effective comparison. The method is assessed in two industrial pilot plants that implement the same process. The results are discussed.