Moving from Periodic Preventive to Condition Based Predictive Maintenance Utilising More Process and Device Big Data

Moving from Periodic Preventive to Condition Based Predictive Maintenance Utilising More Process and Device Big Data

04 Sep 2018, 15:45 - 17:15

Gordon A
Language:
English

Session Title: Moving from Periodic Preventive to Condition Based Predictive Maintenance Utilising More Process and Device Big Data

Abstract TBC

1545-1615: Reducing Deferment with Predictive Data Analytics

Sankesh Sundareshwar, Shell     

Abstract:

Unintended loss of uptime (trips) in gas compression systems is one of the top causes for unscheduled deferment across Shell operated ventures. Causes for compressor failures could be attributed to lack of or inappropriate maintenance, incorrect operating practices and integrity issues, as identified in the Oil and Gas UK compressor study. The focus of this presentation is on compressor systems in production facilities that have major production deferments associated with them. Shell is developing and deploying machine learning models to determine anomalous behaviour and predict potential “trip” conditions and probable root cause with sufficient warning times to allow for intervention.

1615-1645: Reduced Maintenance Cost By Using Process Data, Failure Mode Analytics and Condition Monitoring For Planning of Maintenance Services.

Espen Storkaas, ABB

1645-1715: Panel Discussion: Data driven analytics versus deterministic approach

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