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.
