Machine Learning (ML) plays a crucial role in Industry 4.0, enabling predictive fault detection (FD) by analyzing vast amounts of log data. However, current ML approaches often rely on single-task learning, neglecting the diverse nature of log data and the prediction of interrelated faults. Moreover, multi-view learning (MVL) and multi-task learning (MTL) are usually applied disjointly without applying joint learning across tasks and views. To address these gaps, we a novel approach which leverages multi-task and multi-view learning frameworks, augmented by a multi-label Cross Entropy loss (MTMVL-CE). MTMVL-CE improves generalization performance between and within different fault types, enabling the classification of multiple faults in complex industrial machines. Indeed, MTMVL-CE optimizes classification performance by learning across numerous faults simultaneously, achieving an accurate representation of heterogeneous log data, robust fault classification, and feasible generalization over time. We tested our approach through extensive experiments on an real use case involving FD in a complex banknote recirculator device inside Automated Teller Machines. Our results demonstrate MTMVL-CE’s superior performance compared to MTL and MVL competitors in capturing fault interdependencies and providing accurate, reliable predictions. • MTMVL-CE: unified framework for multi-source industrial fault detection. • Jointly optimizes within-task and between-task fault dependencies. • Correlation-based regularizer models task relationships. • Validated on 7400 heterogeneous real-world ATM log records. • Superior temporal generalization compared to state-of-the-art approaches.

• Automated CAD to XR workflow for interactive Photorealistic Virtual Prototype (iPVP) • Unique texture generation module using a tailored approach based on GANs. • Shrinkwrap modelling for efficient 3D model simplification and texture UV mapping. • Significant time savings in virtual prototyping process. • Framework validated with a case study on sporting rifles, showing high-quality iPVP. CAD-to-XR is the workflow to generate interactive Photorealistic Virtual Prototypes (iPVPs) for Extended Reality (XR) apps from Computer-Aided Design (CAD) models. This process entails modelling, texturing, and XR programming. In the literature, no automatic CAD-to-XR frameworks simultaneously manage CAD simplification and texturing. There are no examples of their adoption for User-Centered Design (UCD). Moreover, such CAD-to-XR workflows do not seize the potentialities of generative algorithms to produce synthetic images (textures). The paper presents a framework for implementing the CAD-to-XR workflow. The solution consists of a module for texture generation based on Generative Adversarial Networks (GANs). The generated texture is then managed by another module (based on Shrinkwrap modelling) to develop the iPVP by simplifying the 3D model and UV mapping the generated texture. The geometric and material data is integrated into a graphic engine, which allows for programming an interactive experience with the iPVP in XR. The CAD-to-XR framework was validated on two components (rifle stock and forend) of a sporting rifle. The solution can automate the texturing process of different product versions in shorter times (compared to a manual procedure). After each product revision, it avoids tedious and manual activities required to generate a new iPVP. The image quality metrics highlight that images are generated in a “realistic” manner (the perceived quality of generated textures is highly comparable to real images). The quality of the iPVPs, generated through the proposed framework and visualised by users through a mixed reality head-mounted display, is equivalent to traditionally designed prototypes.