An Innovative Machine Learning driven approach to detect anomalous behavior of Dry Gas Seal Heaters for Centrifugal Compressors
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Abstract
Compressors are used in various industries to elevate the pressure of a gas to meet the process requirements. Dry Gas Sealing (DGS) system is used in compressors to contain the process gas within the casing with near zero leakage. Due to efficient sealing capabilities, Dry Gas Seals became key technology in industry to prevent the leakage to environment and to ensure compliance with health and safety regulations.
DGS is very sensitive to process gas conditions. To ensure health and integrity of DGS and to maintain required process gas conditions, Dry Gas Seal systems include various Auxiliary Equipment viz. Filters, Heaters, Control valves etc. Heaters maintain the Seal gas inlet temperature at a specified value to avoid presence of condensate, which can otherwise result in Seal failure. To maintain this temperature, Heaters operate in an on-off toggling pattern. Failure of these Heaters can lead to unit unavailability, Early detection of anomalous Heater operation can ensure timely action to avoid any possible negative impacts on Dry Gas Seal health which in turn can impact Compressor operation.
The paper makes a theoretical survey of existing pattern recognition algorithms for time series and examines their applicability in detecting anomalous operation of Seal Gas Heaters. Finding these methods not directly useful, the paper presents a state-of-the-art physics plus data-driven approach. The method is developed by combining LSTM type Neural Network with Periodogram and Auto-correlation monitor to detect any deviations from normally expected operating behavior of Seal Gas Heaters. The LSTM learns an exhibited pattern and looks for the similar patterns in Heater signals. Auto-correlation monitor coupled with Periodogram helps in determining the dominant frequencies and window-size required for the LSTM component. The method is tuned to accommodate different operating modes of Heater based on Compressor running conditions. If Heater deviates from working in an established toggling mode, user is alerted before Seal gas temperature is impacted.
Applied in real-time, the method alerts engineers for any anomalies observed in Heater behavior, thus enabling swift action to prevent any harm to the Dry Gas Seals, caused by temperature upsets. The paper demonstrates the performance of this method when applied on 50 compressors, thus validating the applicability and accuracy in prognostic health management of Dry Gas Seal systems of Compressors.
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Machine Learning approach to detect anomalies in DGS Auxiliary Heaters, Dry Gas Seals, Auxiliary heaters, Compressors
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