# An Unsupervised Anomaly Detection Based on Self-Organizing Map for the Oil and Gas Sector
## 5 Conclusions  
The purpose of the paper was to implement a Self-Organizing Map to monitor the health of the two-phase air-water plant within the Department of Industrial Engineering and Mathematical Sciences (DIISM) of the Universita Politecnica delle Marche (Ancona, Italy).  
This algorithm is an example of unsupervised learning, which is advantageous because it does not require the user to label the training data manually. Moreover, the SOM algorithm offers the benefit of decreasing the dimensionality of the input dataset to yield an output that is simple to interpret. The input dataset is multidimensional and consists of readings from all the flow, level, and pressure sensors directly connected to the experimental system during various tests. In contrast, the output of the algorithm is a two-dimensional map that is easy to interpret, even for an operator who is not an expert in artificial neural networks. The algorithm implemented in this research has an accuracy of 90%. It can identify the state of the two-phase system by associating a particular state with each node on the output map. The output map comprises six macro-areas representing the possible states of the two-phase system. Thus, the operator can quickly identify the state of the plant by observing which area the system reading is projected onto. If the system deviates from normal operating conditions, the system reading will be projected into the anomaly area of the output map, allowing the operator to pinpoint the location of the fault within the system.  
The research study has identified certain limitations. The first constraint pertains to the synchronization of data acquisition on the system. Anomaly tests are conducted by manually closing the shut-off valves to generate obstructions in the line to create the training dataset. However, there is often a lack of synchronization between the operator closing the valve and the one initiating the recording. To overcome this limitation in future research, replacing manual valves with solenoid valves is being considered to have complete control over the system and reduce acquisition errors. Another limitation is associated with the anomalies of air at level 1 and level 2. When these two types of anomalies are recorded, the readings in the output map are projected close to the steady-state and transient areas. This is because, in such scenarios, the system behaviour is very similar to that of the steady-state state. In the next research work, it will be necessary to discriminate these anomalous areas more efficiently to generate a detailed map.  
In addition, in the transient area, the algorithm cannot accurately identify the type of anomaly but only indicates that the system is no longer in steady-state conditions. New readings will be recorded in the upcoming research work to overcome this limitation by combining multiple anomalies in the same test. This method may enable the study of individual boundaries of the output macro-areas in greater detail.  
Conceptualization, G.M. and L.C.; investigation, G.M. and L.C.; methodology, G.M. and L.C.; supervision, M.B.; validation, G.M. and L.C.; writing--original draft, L.C.; writing--review and editing, G.M. and M.B.; project administration, F.E.C.; funding acquisition, F.E.C. All authors have read and agreed to the published version of the manuscript.  
This paper was funded by European Union's Horizon Europe research and innovation programme under grant agreement No. 101057294, project AIDEAS (AI Driven industrial Equipment product life cycle boosting Agility, Sustainability and resilience).  
Not applicable.  
Data Availability Statement: The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.