# Energy-Based Prognosis of the Remaining Useful Life of the Coating Segments in Hot Rolling Mill
## 2 Literature Review
### Methods for Assessing RUL  
Tool prognostics, especially the RUL evaluation, and condition/wear monitoring have been widely investigated within the last two decades. Many approaches use features extracted from vibration data, force measurements, acoustic emission, and/or current signals [15]. The applied methods can be divided into two main categories: (1) data-driven methods, which utilize computational intelligence techniques, such as Machine Learning (ML); and (2) model-based methods, using mathematics/physics-based models to perform data analysis through statistical processes. Several ML approaches for tool-wear monitoring and tool-condition identification have been released during the last two decades [4; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29]. A monitoring strategy establishing a combination of four static and two dynamic NNs was presented by [PERSON] et al. [4]. The static NNs were trained via the incremental Self-Organizing Map (SOM) algorithm, whereas the dynamic ones were trained with the Particle Swarming Optimization Algorithm (PSOA). This formulation has the advantage that the dynamic NNs can follow any development of wear, whereas protecting the monitoring system from possible data disturbances. Another technique was proposed by [PERSON] and [PERSON] [16], which uses a series of NNs: one dynamic that is trained online with the PSOA method and four static that are trained offline with Levenberg-Marquardt back-propagation. [PERSON] et al. [17] addressed several data fusion methods to extract features to use as input data for neural networks (NNs). The Index Multiplication Group (IMG) and the Indices Multiplication and Division Group (IMDG) fusion methods were found to be promising. In [PERSON] and [PERSON] [18], an artificial neural network (ANN) using a neural network output error (NNOE) model for predicting tool wear in machining process was tested, but the attempt was characterized by lack of diagnosis capability and inconsistency with process variations. In more recent ML approaches, an approach was released by [PERSON] et al. [19], making use of the random forest algorithm. Experimental results have shown that the random forest algorithm can generate very accurate predictions. Amongst the most popular ML methods are the approaches that make use of fuzzy logic systems [20; 21; 22; 23], such as the Adaptive Network Fuzzy Inference System (ANFIS) [20] or the Neuro-Fuzzy Network (NFN) [21]. Fuzzy logic systems appear to have great accuracy for tool-condition monitoring (TCM) in milling processes, but they are characterized by big computational times, affecting negatively their applicability for online monitoring. Support Vector Machines (SVM) are also popular ML alternatives [20; 24; 25; 26; 27]. SVMs exhibit more moderate accuracy compared to FL methods, but they are much faster and more suitable for online tool life prediction. A multisensor global feature extraction method has been researched in [PERSON] and [PERSON] [28]. A Kernel-based Extreme Learning Machine (KELM) executes the monitoring model, and a modified genetic algorithm (GA) is implemented to search the optimal parameter combinations in a two-objective optimization model. It was found that the overall error of the predicted tool wear was small. Although ML techniques are widely used nowadays in milling processes, many of the most promising methods are difficultly adaptable in real industrial operations, due to insufficient generalization capabilities for different manufacturing process conditions. However, in 2020, [PERSON] et al. [29] suggested a novel TCM approach based on order analysis (OA) and stacked sparse auto-encoder (SSAE), which was applied in actual industrial manufacturing data and appeared to outperform the most methods which utilize neural networks in varied manufacturing conditions.