Advanced Prognostics for a Centrifugal Fan and Multistage Centrifugal Pump Using a Hybrid Model

Predictive maintenance is fully implemented in the oil and gas industry, and the impressive development of field sensors, big data, and digital twins offers a wide field for the ongoing experimentation and development of diagnostic and prognostic tools for machinery. Although a wide range of technologies and sensors is available, vibration analysis remains the preferred predictive technique for rotating machinery diagnostics. It is well-known, widely used, and has proven efficacious in evaluating the health of rotating machinery and preventing failures. Taking advantage of vibration analysis development and computing capabilities, this study develops three digital twins of one multistage centrifugal pump and two centrifugal fans using real vibration data and synthetic data. This hybrid model approach permits the use of failure data which are not usually found in the normal operation of these machines. The study improves and tunes the accuracy of those models using real operating data obtained from a distributed control system (DCS), thus obtaining results in accordance with process conditions. Maintenance decisions can be supported by these models. They are based on online vibration and process data; they diagnose the health of a machine and give its remaining useful life (RUL). The models may also be used for other API plant assets (multistage centrifugal pumps or centrifugal fans) by changing the configuration parameters and process DCS tags.