# Energy-Based Prognosis of the Remaining Useful Life of the Coating Segments in Hot Rolling Mill
## 1 Introduction  
A continuous evolution concerning the methods for manufacturing high added value products is taking place nowadays, aiming to raise productivity rates and product quality, as well as reduce the number of defected products. The adoption of efficient maintenance strategies is a key element for manufacturing industries, as they cannot afford to perform corrective maintenance actions as soon as they have a critical failure (run-to-failure strategy). Maintenance is important from an economical point of view, since in many industrial plants the maintenance costs often exceed 30% of the operational costs. The occurrence of unscheduled maintenance in manufacturing industries can introduce costly delays and breakdowns if the problem cannot be rectified on time [1]. Furthermore, manufacturers cannot rely only on time-based preventive maintenance, since its results are vulnerable to unexpected variations in process parameters that are related to tool and machine conditions, material quality, and human intervention, affecting the effectiveness of such maintenance techniques. Such static approaches could end up with replacing parts that are still in good condition, or, on the other hand, they could result in unexpected failures. However, predictive maintenance strategies can be deployed for in-advance determining the needs and driving suitable maintenance activities. The expected outcomes of predictive maintenance in manufacturing processes include optimized part usage, reduced costs, increased machinery lifetime, and improved product quality [2]. In the context of Industry 4.0,predictive maintenance (PdM) strongly relies on the Internet of Things (IoT) for timely data exchange. IoT-enabled PdM is attracting considerable investment from industries nowadays [3]. The problem of Remaining Useful Life (RUL) prognosis for tool components in manufacturing is important as prompt wear condition identification may lead to a reduction of downtime costs, lifetime increase, a better quality of surface finishing, and pre-scheduling of maintenance activities. Therefore, RUL estimation is straightforwardly correlated with tool-condition monitoring (TCM), since, although degradation is a non-stationary process, it is gradual [4]. Several approaches have been released within the last two decades, including implementations with machine learning (ML) methods, such as neural networks (NNs) systems, as well as based on mathematics-physics models. However, no general solution has been deployed yet. Irregular production conditions and non-stationary phenomena during production processes (e.g., cutting/rolling speed variations and production pauses) cause ML systems to face difficulties in providing such a generalized solution. However, the wide range of predefined methods and training algorithms throughout the literature has made them popular. On the other hand, approaches based on mathematics-physics models look more appropriate for production non-stationarities. Nevertheless, feature-selection along with the identification of the appropriate models and analysis procedures remains challenging. The aim of this paper is to present a data-driven methodology for RUL estimation for the coating segments of a hot rolling mill that processes two different types of trailing arms. A prototype prediction software was designed and developed, to provide interval RUL estimations in terms of remaining products to process without quality degradation. The main contributions of this work are as follows: (1) the development of a novel method for the prediction of RUL in hot rolling milling machines that process different types of products, (2) the implementation of the RUL prediction method in a software prototype that enables the application in real industry cases and provides a graphical user interface for interacting with end-users, and (3) the evaluation of the method in an industrial case based on historical datasets. The results have shown that the method provides accurate predictions promptly.