Abstract
In recent years, the digital transformation has been advancing in industrial companies, supported by the Key Enabling Technologies (Big Data, IoT, etc.) of Industry 4.0. As a consequence, companies have large volumes of data and information that must be analyzed to give them competitive advantages. This is of the utmost importance in fields such as Failure Detection (FD) and Predictive Maintenance (PdM). Finding patterns in such data is not easy, but cutting-edge technologies, such as Machine Learning (ML), can make great contributions. As a solution, this study extends Hybrid Unsupervised Exploratory Plots (HUEPs), as a visualization technique that combines Exploratory Projection Pursuit (EPP) and Clustering methods. An extended formulation of HUEPs is proposed, adding for the first time the following EPP methods: Classical Multidimensional Scaling, Sammon Mapping and Factor Analysis. Extended HUEPs are validated in a case study associated with a multinational company in the automotive industry sector. Two real-life datasets containing data gathered from a Waterjet Cutting tool are visualized in an intuitive and informative way. The obtained results show that HUEPs is a technique that supports the continuous monitoring of machines in order to anticipate failures. This contribution to visual data analytics can help companies in decision-making, regarding FD and PdM projects.
Highlights
In the industrial sector, there are several issues that most corporations are trying to address.As far as technology advances are concerned, some of these problems could be solved, or at least their negative impact could be reduced
Plenty of different visualizations have been generated by combining all the Exploratory Projection Pursuit (EPP) (PCA, MLHL, CMLHL, Classical Multidimensional Scaling (CMDS), Sammon Mapping (SM) and Factor Analysis (FA)) together with the clustering (k-means and agglomerative) techniques, tuned with different parameter values
This study has shown that Hybrid Unsupervised Exploratory Plots (HUEPs) are a technique that supports the monitoring of sensors and machines in order to anticipate failures
Summary
There are several issues that most corporations are trying to address.As far as technology advances are concerned, some of these problems could be solved, or at least their negative impact could be reduced. The concept of Industry 4.0 [1] has been proposed, involving several cutting-edge technologies such as robotics, Artificial Intelligence, Industrial Big. Data [2], Industrial Internet of Things (IIoT) [3], deep learning and deep analytics, computer vision, visual data analytics, visual computing and digital twins [4], among others. Data [2], Industrial Internet of Things (IIoT) [3], deep learning and deep analytics, computer vision, visual data analytics, visual computing and digital twins [4], among others These resources are greatly contributing to the solving of many of the problems in industrial manufacturing, and are improving manufacturing processes. Industrial companies are developing projects in order to adhere to the “smart factory” [5,6]. Factories want to be able to learn and adapt to changes in real time, and in order to do that, it is necessary to have permanent information and data regarding the elements
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