## 1 Introduction  
The steelmaking process looks critical for energy consumption, power losses, environmental pollution, and the safety of human operators [1]. The hot rolling mill exploits a huge amount of energy, while in cold rolling are even critical vibration, friction, and chattering [2]. Moreover, the risk of fire, due to anomalous heating of mill components and lubricant, makes the system design rather difficult.  
Nowadays, the enabling technologies of smart manufacturing allow mitigating those problems [3]. They help in reducing mill vibration and material friction, to make higher the product quality, control the state of lubricant and temperature, and even save energy in terms of heat, vibration, and water flow control when it is used to keep the temperature of roll surface as low as possible. Moreover, early detection of failures and damages allows for applying effective predictive maintenance.  
A distributed monitoring system with capillary access to the most hidden parts of the mill cage may operate those actions and prevent failures. It warns operators about any incipient faults [4] but even collects data from the machine elements [5]. It usually exploits a network of distributed sensors, although some machine components, such as bearings, may play the role of physical nodes of the network, to monitor the whole machinery when they embed transducers [6, 7]. Particularly, mechanical bearings are referred to as \"smart\" when sensors monitor \"in situ\" the overall system behavior [8, 9]. They constitute a node of the distributed monitoring system to enhance the mill connectivity for monitoring, diagnosis, and prognosis purposes, leading to \"machinery condition monitoring\" (MCM) [10, 11]. Some network services provided by the Internet of Things (IoT) assure that connectivity [12].  
In the literature, wide use of smart bearings to detect vibration, noise, and resonances is proposed [13], but a relevant part of MCM for safety belongs to the early detection of temperature changes, especially in the most hidden elements of the rolling mill.  
This paper investigates the specific use of smart bearing to detect any anomalous heating in the rolling mill, through the fiber optics sensors, by monitoring elements of the mill cage, being deeply embedded and poorly accessible. The mill system architecture is analyzed. The study proposes access to backup bearings through their supporting and fixed pins or through the structure of the mill cage, constraining the bearing rings. This choice depends on the mill layout and its operation in either cold or hot rolling processes. The investigation then identifies the fiber path inside the rolling mill and sets up the interface to the bearing ring.  
A preliminary experimental validation demonstrates the effectiveness of the fiber Bragg gratings (FBG) [14] when embedded inside the mill system. An assessment of sensors specifications includes a preliminary investigation of the fluidic interface exploited to help the measurement in operation by considering the availability of water and lubricant oil, respectively, in the mill cage. This preliminary investigation encourages finalizing the design assessment of this approach for industrial application and for the proposed implementation of artificial intelligence packages.