## 1 Introduction
Maintenance is performed based on the estimated health of the component. The PdM systems allow for detecting the fault before its occurrence, thus giving the possibility to intervene in advance. All this is possible thanks to historical data, statistical methods, machine learning and engineering approaches. In particular, machine learning techniques help defining useful methods on the data for predicting future failures and for intervening proactively. This maintenance strategy tries to overcome the limitations of PvM by trying to exploit the component until the end of its life. It is therefore used for particularly critical components due to their high cost of use and for possible losses in terms of safety. In this work, we will focus and employ a predictive approach based on data analysis and machine learning techniques to predict future failures. The case study is represented by the analysis of the time series of the failures of the components of a revamping topping plant (RT2) belonging to the SARAS group. It will show which issues present the raw data and which pre-processing techniques we have used to clean and complete the data with the goal of having a number of features and different samples that can be organized in a data structure to be fed to the predictive models. Indeed, in real-world applications, the dataset that it has to be managed can get extremely massive, complicated, and incomplete, and mostly, it can contain data of all sorts, including missing or inconsistent values. Therefore, data pre-processing is a crucial step in the implementation of machine learning algorithms to extract valuable information, making the database complete and more precise. Thus, the achieved quality of the well-structured dataset makes the ML algorithm able to accurately predict future observations. Simple natural language processing (NLP) approaches are used to categorize each occurred fault and thus create a taxonomy that can be used to classify future failures. The goal is to forecast the next failure for each component in the RT2. In particular, predictions will be made by exploiting the time series of the past related to single-component failures using simple algorithms such as linear regression, ARIMA, and a polynomial interpolation strategy we propose. ARIMA has already been widely used in the predictive field to make predictions on time series [21] and has already been used in the petroleum environment to make predictions on the performance of crude oil wells [22]. The remainder of this paper is organized as follows. Section 2 shows examples of works similar to ours and the differences between our proposal and the state of the art. In Section 3, the industrial plant that we have considered for its failure data will be described. Section 4 contains the description of the historical data that will be processed. In Section 5, we will explain the methodology with which data have been analyzed and fed to different machine learning techniques. In Section 6, the results obtained using the methodology developed in Section 5 will be shown. In particular, the results will be illustrated, step by step, starting from the reconstruction of the data and ending with the forecasts of the faults. Finally, Section 7 ends the paper with conclusions drawn as a function of the obtained results, discussions on best practices that we define, and future directions we are headed toward to improve the performances we obtained and generalize our approach.