This work highlights the development and results of a Rotating equipment predictive maintenance tool that allows to monitor the status of rotating machines through a synthetic "health index" and early detection of anomalies. The data-driven proposed solution is of great help to maintenance engineers, who, alongside the existing methodologies, can apply an effective tool based on artificial intelligence for early prevention of failures. Taking advantage of the high availability of remote sensors data, an anomaly detection machine learning model, which relies on Principal Component Analysis (PCA) and Kernel Density Estimation (KDE), has been built. This model is capable of estimating, in real time, the health status of the machine, by matching the sensors actual values with the reference ones based on the Normal Operating Conditions (NOC) periods, that have been previously identified. If an anomalous behavior is detected, the Fault Isolation step of the model allows to evaluate which are the most contributing sensors for the investigated anomaly. These outcomes, combined with a failure mode matrix, which links the sensors deviations with the possible malfunctions, allows to highlight the most likely failure modes to be associated to the investigated anomaly. The developed predictive tool has been implemented on operating sites and it has demonstrated the capability to generate accurate warnings and detect anomalies to be processed by the maintenance engineers. These alerts may be aggregated into events in order to be monitored and analyzed by remote and on site specialists. The availability of alerts gives to the users the possibility to predict any deterioration of the machines or process fluctuations, that could lead to unplanned events with consequent mechanical breakdowns, production losses and flaring events. As a consequence, tailored operative adjustment to prevent critical events can be taken. Thanks to the tool, it is also possible to monitor over time the equipment behavior in order to provide suggestions for maintenance plans optimization and other useful statistics concerning the most recurrent failure. The tool's innovative feature is the ability to utilize the giant amount of data and to reproduce complex field phenomena by means of artificial intelligence. The proposed tool represents an innovative predictive approach for rotating equipment maintenance optimization.

This work aims to present a methodology based on a proactive approach for the early detection of possible liquids carryover towards compressors installed in upstream process plants where the presence of heavy hydrocarbon inside gas stream is a recursive problem. Analyzing trough remote monitoring some parameters of the facilities that are upstream to the compressor and, simultaneously, some machine critical instrumentation that was selected through early detection algorithms, it is possible to develop real time synthetic indicators that warn the possibility of liquid ingestion. An in-depth analysis of the plant and of the process lines and facilities that are upstream to the compressors, permits to identify the critical parameters to monitor, which unequivocally indicate a change in both physical conditions and gas composition. This methodology has been applied to create tailor made synthetic indicators that have been implemented in operating sites, to highlight the upset in the process parameters that could have led to liquid carry over with consequent possible mechanical breakdowns. These indicators enhance the deep analysis of these phenomena, resulting in the development of tailored operative adjustment to be applied at the early stage of the liquid carryover occurrence. The combined monitoring both on the upstream facilities and on the on-board instrumentation of compressors allows to have an early detection of the liquid carryover events, giving time to the operators to understand the situation and act accordingly to avoid hazardous conditions. The methodology innovative feature is the ability to monitor, by remote, a combination of critical parameters at "plant level", together with cross-check trough machine-learning algorithms of the relevant on-board instrumentation at "compressor level" to have a complete picture of the phenomena and prevent the compressors from unplanned damages due to liquid carryover.