# A Decision-Making Tool Based on Exploratory Visualization for the Automotive Industry
## 1 Introduction  
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. Recently, 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. 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] concept. With such projects, 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 elementsinvolved in the plant. To be able to capture all this information, sensors and IoT devices need to be installed. Thanks to IIoT, it is possible to have millions of data items related to factories and their machines. However, all these datasets are useless and expensive unless they can be analyzed; here is where the proposals of Big Data and Visual Analytics appear. In general terms, companies are not able to accomplish the implementation of the smart factory paradigm in all their plants. The costs of becoming a smart factory are potentially unaffordable; therefore, companies are not sensorizing all their machines or processes. The storage resources (cloud) required to keep this huge quantity of information are not free. Normally, the data of a machine in a factory involves a great volume of information with a high dimensionality. Additionally, as there are many different machines being permanently monitored, the produced data are heterogeneous and not complete (sensors and communications may fail). When properly used, all these datasets could make a factory more efficient in several areas: resource-savings, cost-reduction, optimization of production times, increasing sustainability, minimizing failures and downtime, etc., but the investment in gathering, storing and analyzing these datasets is usually very high. Because of that, it is very important to be sure that sensors, devices and related datasets are carefully chosen, and have the proper features. These data would have a high dimensionality, that should be in balance with the costs and requirements of the company. In order to overcome this well-known problem, named \"the curse of dimensionality\", Machine Learning (ML) and interactive visualization for data exploration could make great contributions. As a result, any department of industrial companies could benefit from the use of visual analytics in decision-making. In keeping with this idea, the present study proposes the extension of Hybrid Unsupervised Exploratory Plots (HUEPs) [7], and the applying of them for sensor validation and condition monitoring as a decision-making tool based on visual analysis for subsequent predictive maintenance (PdM). HUEPs are a recently-proposed technique, wherein Exploratory Projection Pursuit (EPP) [8] and clustering [9] methods could be combined to generate informative and intuitive 3D visualizations of high-dimensional data (see Section 2). When using different colors and the glyph metaphor, they can display more information than a standard 2D or 3D projection, and in a more intuitive way. HUEPs are validated and extended in the present paper by incorporating some additional EPP techniques, namely Classical Multidimensional Scaling (CMDS), Sammon Mapping (SM) and Factor Analysis (FA). This study helps to analyze the datasets' structure and clearly identify issues in machines, anticipating potential failures.