At the very beginning of the Industry 4.0 era, automated systems and automatic knowledge conceptualization are becoming more and more essential. The ever faster, ever more resource-demanding processes are raising a pressing need for highly efficient handling of the systems. While the industries produce ever more, there is less and less time for back-up and quality assessment; a piece of solution may come along a real-time and automated monitoring, based on sensors’ data so as to assess products’ validity: this is the areas of Behavior Identification and Anomaly Detection. In this paper, we propose to use Machine Learning and data-driven clustering to automatically identify the real behaviors of a system, and therefore its possible anomalies. More than the methodology, we mostly propose a more stable clustering method based on the Self-Organizing Maps to achieve that purpose. We apply both methodology and that improved clustering method to real industrial data, and we show that it is more efficient, more stable and more relevant to dynamic systems.

We go deeper and deeper in automation: for decades, and even centuries, we have been dreaming of a completely automated world with the capacity to self-adapt to any imaginable context. A very promising paradigm to do so is Machine Learning, which allows an automatic and often reliable modeling of any unknown system, by analyzing its data and by deriving knowledge from them. The first tasks of such an approach are data preparation and knowledge extraction, which are areas of research on themselves; as such, experts are generally required in order to manually identify and characterize the studied systems, what can be a long, exhaustive and expensive task. Wouldn't be great if this step could be executed automatically? In this paper, we will present some avenues of reflection in order to achieve such a cognitive system, by digging into the data through clustering approaches and by extracting any knowledge possible.

For two centuries, the industrial sector has never stopped evolving. Since the dawn of the Fourth Industrial Revolution, commonly known as Industry 4.0, deep and accurate understandings of systems have become essential for real-time monitoring, prediction, and maintenance. In this paper, we propose a machine learning and data-driven methodology, based on data mining and clustering, for automatic identification and characterization of the different ways unknown systems can behave. It relies on the statistical property that a regular demeanor should be represented by many data with very close features; therefore, the most compact groups should be the regular behaviors. Based on the clusters, on the quantification of their intrinsic properties (size, span, density, neighborhood) and on the dynamic comparisons among each other, this methodology gave us some insight into the system’s demeanor, which can be valuable for the next steps of modeling and prediction stages. Applied to real Industry 4.0 data, this approach allowed us to extract some typical, real behaviors of the plant, while assuming no previous knowledge about the data. This methodology seems very promising, even though it is still in its infancy and that additional works will further develop it.

This paper introduces a non-parametric methodology based on classical unsupervised clustering techniques to automatically identify the main regions of a space, without requiring the objective number of clusters, so as to identify the major regular states of unknown industrial systems. Indeed, useful knowledge on real industrial processes entails the identification of their regular states, and their historically encountered anomalies. Since both should form compact and salient groups of data, unsupervised clustering generally performs this task fairly accurately; however, this often requires the number of clusters upstream, knowledge which is rarely available. As such, the proposed algorithm operates a first partitioning of the space, then it estimates the integrity of the clusters, and splits them again and again until every cluster obtains an acceptable integrity; finally, a step of merging based on the clusters’ empirical distributions is performed to refine the partitioning. Applied to real industrial data obtained in the scope of a European project, this methodology proved able to automatically identify the main regular states of the system. Results show the robustness of the proposed approach in the fully-automatic and non-parametric identification of the main regions of a space, knowledge which is useful to industrial anomaly detection and behavioral modeling.