## 2 Related Work
[41] investigated the transfer learning method for rotating machinery by considering variable working conditions, while [PERSON] et al. [42] proposed a scheme based on hierarchical symbolic analysis (HSA) and a convolutional neural network (CNN). In [18; 19; 43; 44], the authors proposed different ML approaches to be applied to roller-bearing fault diagnosis. [PERSON] et al. [1] compared two ML algorithms based on support-vector machines (SVM) and multi-layered perceptron (MLP) by using a real dataset from sensors placed on a centrifugal pump. [PERSON] and [PERSON] [45] and [PERSON] et al. [46] applied a deep learning model for a smart-grid stability prediction. In [47; 48; 49], the authors used an SVM algorithm for failure detection, while [PERSON] and [PERSON] [14] proposed two different techniques based on artificial neural networks (ANN) and adaptive neuro-fuzzy inference systems (ANFIS). An ANN model was also presented by [PERSON] et al. [50] to monitor the actual condition of the stator electrical insulation of three-phase induction motors and by [PERSON] et al. [51] to predict failures of an oil-immersed transformer. [PERSON] et al. [52] presented a random forest model for fault diagnosis of a cutting machine. [PERSON] et al. [53] adopted an estimation algorithm of the probability density function of current signals for the failure detection and diagnosis of an electric motor. [PERSON] et al. [54] developed an approach based on the combination between CNN and extreme learning machine (ELM) to be applied to a gearbox and motor bearing. In [55; 56; 57], the authors adopted ensemble learning models for different rotating machinery. [PERSON] and [PERSON] [58] proposed a framework for the proper implementation of an ML-based PdM strategy for pumping systems. [PERSON] et al. [59] reported a novel adaptive learning rate deep belief network combined with Nesterov momentum applied to a gearbox and locomotive bearing test rigs. [PERSON] et al. [60] and [PERSON] et al. [61] proposed a machine learning-based approach for the health condition monitoring of a radial fan impeller and machine tool system elements, respectively. The reported literature review depicts a scenario where machine learning turns out to be a very appealing field of research, especially for industrial maintenance management since it emerges as a powerful resource for the development of intelligent predictive algorithms [62]. However, ML adoption in real factories is still considered challenging mostly due to several barriers that limited its implementation regarding current industrial applications [11; 26; 63]. Some of the main challenges are: (i) the acquisition of a suitable and relevant dataset since it strongly affects the predictive model efficiency and (ii) the selection of a proper ML algorithm. The former challenge highlights the importance of preprocessing data as a vital process impacting the results, especially when real raw data are used, while the latter assumes that each manufacturing problem is unique, and each algorithm performs differently depending on the data available, as well as parameter settings. Therefore, this requires the analysis of the different models, aiming at selecting the one capable of solving the targeted manufacturing problem. To deal with these open challenges, this work proposes a preprocessing technique for the historical data of failures of different plant equipment operating in the oil and gas sector. Moreover, the achieved structured dataset is used to develop machine learning models and statistical approaches aiming at performing reliable failure predictions to support the maintenance decision-making process.