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The Industry 4.0 paradigm is being increasingly adopted in the production, distribution and commercialization chains worldwide. The integration of the cutting-edge techniques behind it entails a deep and complex revolution – changing from scheduled-based processes to smart, reactive ones – that has to be thoroughly applied at different levels. Aiming to shed some light on the path towards such evolution, this work presents an Industry 4.0 based approach for facing a key aspect within factories: the health assessment of critical assets. This work is framed in the context of the innovative project SiMoDiM , which pursues the design and integration of a predictive maintenance system for the stainless steel industry. As a case of study, it focuses on the machinery involved in the production of high-quality steel sheets, i.e. the Hot Rolling Process , and concretely on predicting the degradation of the drums within the heating coilers of Steckel mills (parts with an expensive replacement that work under severe mechanical and thermal stresses). This paper describes a predictive model based on a Bayesian Filter , a tool from the Machine Learning field, to estimate and predict the gradual degradation of such machinery, permitting the operators to make informed decisions regarding maintenance operations. For achieving that, the proposed model iteratively fuses expert knowledge with real time information coming from the hot rolling processes carried out in the factory. The predictive model has been fitted and evaluated with real data from ∼ 118k processes, proving its virtues for promoting the Industry 4.0 era. • A Bayesian Filter is proposed for predicting the degradation state of machinery. • Such a model iteratively fuses expert knowledge with real time data from the factory. • A set of recipes for analyzing and processing plant and processes data are provided. • As a case of study, it is applied to the machinery within the Hot Rolling process. • The proposal has been assessed with data from +118k processes from a real factory. [ABSTRACT FROM AUTHOR]

Olfaction is a valuable source of information about the environment that has not been sufficiently exploited in mobile robotics yet. Certainly, odor information can contribute to other sensing modalities, e.g., vision, to accomplish high-level robot activities, such as task planning or execution in human environments. This paper organizes and puts together the developments and experiences on combining olfaction and vision into robotics applications, as the result of our five-years long project IRO: Improvement of the sensory and autonomous capability of Robots through Olfaction. Particularly, it investigates mechanisms to exploit odor information (usually coming in the form of the type of volatile and its concentration) in problems such as object recognition and scene–activity understanding. A distinctive aspect of this research is the special attention paid to the role of semantics within the robot perception and decision-making processes. The obtained results have improved the robot capabilities in terms of efficiency, autonomy, and usefulness, as reported in our publications. [ABSTRACT FROM AUTHOR]

This paper addresses the localization of a gas emission source within a real-world human environment with a mobile robot. Our approach is based on an efficient and coherent system that fuses different sensor modalities (i.e., vision and chemical sensing) to exploit, for the first time, the semantic relationships among the detected gases and the objects visually recognized in the environment. This novel approach allows the robot to focus the search on a finite set of potential gas source candidates (dynamically updated as the robot operates), while accounting for the non-negligible uncertainties in the object recognition and gas classification tasks involved in the process. This approach is particularly interesting for structured indoor environments containing multiple obstacles and objects, enabling the inference of the relations between objects and between objects and gases. A probabilistic Bayesian framework is proposed to handle all these uncertainties and semantic relations, providing an ordered list of candidates to be the source. This candidate list is updated dynamically upon new sensor measurements to account for objects not previously considered in the search process. The exploitation of such probabilities together with information such as the locations of the objects, or the time needed to validate whether a given candidate is truly releasing gases, is delegated to a path planning algorithm based on Markov decision processes to minimize the search time. The system was tested in an office-like scenario, both with simulated and real experiments, to enable the comparison of different path planning strategies and to validate its efficiency under real-world conditions. [ABSTRACT FROM AUTHOR]