# A Decision-Making Tool Based on Exploratory Visualization for the Automotive Industry
## 1 Introduction
If the initial collected dataset, once analyzed, reveals a defined structure, including clusters \"reporting\" issues, this can be seen as a sign of a representative data set. The data used in this study have been provided by Grupo Antolin [10] (a multinational company in the automotive industry sector), and refer to several waterjet industrial tools (machines to perform industrial cutting using extremely-high pressure water) located in an automotive plant. This proposal advances previous work, as the hybridization of unsupervised learning for visualization is applied for the first time in this real case study. Datasets collected for PdM purposes have never been analyzed before with HUEPs. As was stated before, it is necessary to balance costs and benefits. Data visualization contributes to finding such balance, especially in cases of high volumes of information with high dimensionality. Exploratory analysis must be one of the first steps in industrial data analytics [11], and visualizations are advisable with regards to knowing if the collected data make sense [12]. As some studies suggest [13], visual computing plays a vital role in both Industry 4.0 and advanced manufacturing. In [14], it is shown how the application of visual computing can empower workers in the framework of Industry 4.0. A use case is presented, showing how visual analytics incorporated in a plant's Human-Machine Interface can improve the cognitive process of supervising a production line and applying corrective maintenance. In [15], the authors focus on how the fusion of graphics, vision and media technologies can enrich the role of the new operator 4.0. Other surveys present a methodology for implementing a data-driven PdM not only in the machine decision making, but also in data acquisition and processing, with a visual analysis of the Remaining Useful Life (RUL) of a machining tool [16]. As stated, companies should not forget the cost-improvement trade-off involved in Industry 4.0, explained in [11], where a methodology based on data analytics has been proposed for the cost-efficient monitoring of Industry 4.0, with a real use case within the automotive industry. Some previous studies, that researched the application of ML to PdM and faults/anomalies detection, have proposed a classification approach, based on supervised learning. Less effort has been devoted to investigating the contributions of unsupervised learning, although some previous work does exist. For instance, in [17], the authors proposed the application of the \(k\)-means method combined with Fuzzy Logic for PdM purposes. In [18], a combination of \"constrained \(k\)-means clustering, fuzzy modelling and LOF-Based score\" has been applied in order to detect anomalies in an auxiliary marine diesel engine. Other research [19] applied five clustering methods (Hierarchical, \(k\)-medoids, \(k\)-means, DBSCAN and OPTICS) in a health condition monitoring model for a semiconductor company's chemical vapor deposition process. Other studies on fault diagnosis or PdM analyze kinds of datasets that have been very widely used and tested in the literature: motor bearing datasets [20; 21; 22], gear-box datasets [20; 23; 24] and the Tennessee Eastman Process (TEP) dataset [25]. In [26], another popular rolling bearing dataset has been used for testing a deep-distance metric learning method. For fault diagnosis in locomotive roller bearings, some authors proposed [27] Fuzzy c-means (FCM). Another paper proposed a deep neural network, named CatAAE, for unsupervised fault diagnosis of rolling bearings [28]. There are also studies that propose supervised learning for bearing defect classification [29]. In [30], the authors proposed a nearest and farthest distance preserving projection (NFDPP) algorithm to explore the relationships of a sample with its farthest and nearest neighbours, which was validated in identifying compound faults in locomotive bearings.