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Condition-based Maintenance: Gas Compressors

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reduced maintenance cost

Key Facts


Leading Major oil company


Oil & Gas

Reducing equipment maintenance costs without increasing failures

Condition-based monitoring solution for gas compressors

Reduced frequency of routine maintenance prioritizing critical repairs


A large Major oil company sought to reduce its yearly equipment maintenance costs by 10%. Compressor failure accounted for a significant share of the company’s lost production, resulting in a strict routine maintenance policy on all gas compressors. Non-productive time dropped significantly over the following years, but annual maintenance costs rose 300% given the shortened maintenance interval. Management sought to implement a data-driven, condition-based maintenance approach where preventative maintenance is prioritized based on real-time data streaming from the equipment. This allows for extended maintenance intervals through just-in-time scheduling. The company selected Arundo through an RFP to deliver the fully integrated CBM solution.



Arundo’s team of data scientists worked with the customer to implement a condition-based monitoring solution for their gas compressors. Arundo developed a machine-learning model to map real-time hydrocarbon readings against a standardized set of operating ranges and deployed the model within the Arundo platform. Given the variance of individual signals, a cluster-based approach was used to aggregate multiple individual signals and elimi- nate white noise. When sensors detected a deviation from the predicted operating range, the system immediately alerted the user and provided a likelihood of compressor failure. Prior to implementation in the production environment, the model was validated against anonymized historical data and successfully predicted equipment failure with a 95% accuracy rate, far superior to status quo forecasts.



After a short pilot phase, the client unveiled the condition-based monitoring platform across twelve of its North Sea sites. With real-time monitoring of asset health, management successful reduced the frequency of routine maintenance by 75% while prioritizing critical repairs.

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