# An Unsupervised Anomaly Detection Based on Self-Organizing Map for the Oil and Gas Sector
## 1 Introduction  
Regarding efficiency and safety, switching from a traditional industry to Industry 4.0 has several advantages [1]. These benefits are connected to adopting technologically advanced machinery with a high level of digitalization and communication [2]. However, the cost and time involved in replacing obsolete machinery can be unsustainable for many companies instead of retrofitting machinery with new digital technologies [3]. The energy sector can be considered to have felt the potential of this transition the most. Petrochemical industries, for example, are keen to embrace digital technologies with all the predictive benefits that come with them [4]. For instance, by combining plant sensors and artificial intelligence algorithms, it is possible to build a model that can discriminate the degradation profiles and estimate the remaining useful life of each system component [5, 6]. Nevertheless, some of the largest companies still base their maintenance activities on sporadic, mostly manual inspections to monitor and ensure the proper functioning of machinery. Thus, if equipment such as heat exchangers, pumps, or valves are only checked periodically, the risk of breakdowns, interruptions, or more severe situations affecting the health and safety of operators and the plant itself is not reduced [7]. For these reasons, the oil and gas industry aims to become increasingly innovative and implementsmart technologies to increase levels of operational efficiency and resource utilization while minimizing health, safety, and environmental risks and, not least, operating costs [8; 9]. The Internet of Things (IoT) and Digital Twin (DT) [10] can be considered as the guiding force for this crucial digital transformation, as they enable the real-time gathering, handling, and interpretation of data to accomplish these objectives [11; 12; 13; 14]. The transition from a traditional factory to a smart factory brings a higher level of integration of physical production with digital technologies [15; 16] with particular attention to standards such as ISO14224:2016 for oil and gas industries [17]. For example, oil particle smart sensors monitor the contamination levels in lubrication systems such as gearboxes through a laser beam and photodetector, sending warnings when the permissible pollution limit is exceeded [18]. However, it is also true that the proliferation of sensors in Industry 4.0 can only lead to effective and efficient monitoring and control capability if the data are structured for a systematic overview [19]. In addition, the increase in available data implies the necessary development of data-driven machine learning techniques to understand processes and their reliability defining effective maintenance policies able to support companies in dealing with process interruptions while preventing significant profit losses [20]. Moreover, the increase in available information increases the computational effort of the various algorithms because of their size and nature, requiring such algorithms to be necessarily faster and more efficient [21]. However, although artificial intelligence and machine learning have been successfully applied in many sectors, their potential for maintenance has not been fully recognized. In this regard, [PERSON] and [PERSON] [22] analysed the main challenges preventing a profound application of artificial intelligence in the oil and gas sector regarding data, people, and all the new forms of collaboration emerging with Industry 4.0. At the same time and for the same sector, [PERSON] et al. [23] highlighted how artificial intelligence technology increases attention from researchers devoted to it. Although technological innovation can support the production of many companies, it can become a problem if all operators are not suitably well trained in using new technologies. Therefore, the skills of operators need to be improved through specific training courses geared toward hands-on learning of these new technologies to increase safety and operating conditions [24].