LISSI Laboratory EA 3956, Sénart-FB Institute of Technology, Campus of Sénart, University of Paris-Est Créteil, 36-37 Rue Georges Charpak, F-77567 Lieusaint, France.
Sensors (Basel). 2022 Apr 12;22(8):2939. doi: 10.3390/s22082939.
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.
两个世纪以来，工业领域从未停止过发展。自第四次工业革命（通常称为工业 4.0）开始以来，对系统的深入和准确理解对于实时监测、预测和维护变得至关重要。在本文中，我们提出了一种基于数据挖掘和聚类的机器学习和数据驱动方法，用于自动识别和描述未知系统的不同行为方式。它依赖于这样一种统计特性，即规则行为应该由许多具有非常接近特征的数据表示；因此，最紧凑的组应该是规则行为。基于聚类，以及对其内在特性（大小、跨度、密度、邻域）的量化和相互之间的动态比较，这种方法使我们对系统的行为有了一些了解，这对于建模和预测阶段的下一步非常有价值。将该方法应用于实际的工业 4.0 数据，使我们能够提取出工厂的一些典型、真实的行为，同时假设对数据没有先验知识。尽管该方法仍处于起步阶段，还需要进一步的研究来完善，但它似乎很有前途。
