Predictive Analytics and Diagnostics Drive Effectiveness in Condition Based Monitoring

Presented at GasTech Centre of Technical Excellence 23 March 2011

Gas compression equipment operators employ a wide range of analytical techniques in order to maintain availability, improve reliability, and decrease their maintenance expense. When viewed in context of reliability centered maintenance programs, most facilities rely on one of the following systems:
• time-based preventive maintenance analyses,
• continuous monitoring systems that have first-principles deterministic models, or
run until failure operating philosophy

Predictive analytics using empirically-derived pattern recognition algorithms can enhance problem detection and increase the effectiveness of an organization’s monitoring program. With robust early detection that has few false alarms, equipment operators can remove time-dependent randomness from their ability to discover problems. Skilled equipment analysts and technicians will no longer spend time devoted to analyzing healthy equipment. The same techniques can be applied to rotating equipment or to processing equipment.

When a company has multiple processing trains or facilities, scalability and cost-effectiveness become increasingly more important.  Empirically-derived pattern recognition speeds fleet deployment across a wide range of equipment types and models.  This reduces the dependence on the expertise that is required to establish and maintain an expert or first-principles based system.

Predictive analytics enables a bridge between the depth of coverage of a permanently installed full analytical system and the versatility of portable analyzers and preventive maintenance analyses. Equipment operators can use early detection from predictive analytics to focus their technicians on analyzing the right equipment at the right time.

The paper will describe the predictive analytics modeling philosophy around rotor dynamics, thermodynamics, and heat transfer. One or more case studies will show the work processes of event detection, diagnostics, collaboration, and information consolidation. The extension of predictive analytics is predictive diagnostics which combines detection with the context of how equipment operates.

Equipment operators can extend run times and maintenance intervals by using predictive analytics as a foundation. The observations, diagnoses, and feedback will then roll up into a total asset management system and bridge major gaps that occur in many reliability programs. Predictive diagnostic methodologies enable equipment owners to extend run times and to decrease maintenance with confidence.