Trip Reduction in Turbo Machinery



Published Sep 4, 2023
Pranay Mathur Carlo Michelassi Simi Madhavan Karatha Gilda Pedoto Miguel Gomez Alguacil


Industrial plant uptime is top most importance for reliable, profitable & sustainable operation. Trip and failed start have major impact on plant reliability and all plant operators focussed on efforts required to minimise the trips & failed starts. The performance of these Critical to Quality (CTQs) are measured with 2 metrics, MTBT (Mean time between trips) and SR (Starting reliability). These metrics helps to identify top failure modes and identify units need more effort to improve plant reliability.
Baker Hughes Trip reduction program structured to reduce these unwanted trip
1. Real time machine operational parameters remotely available and capturing the signature of malfunction including related boundary condition.
2. Real time alerting system based on analytics available remotely.
3. Remote access to trip logs and alarms from control system to identify the cause of events.
4. Continuous support to field engineers by remotely connecting with subject matter expert.
5. Live tracking of key Critical to Quality (CTQs)
6. Benchmark against fleet
7. Break down to the cause of failure to component level
8. Investigate top contributor, identify design and operational root cause
9. Implement corrective and preventive action
10. Assessing effectiveness of implemented solution.
11. Develop analytics for predictive maintenance

With this approach, Baker Hughes team is able to support customer in achieving their Reliability Key performance Indicators for monitored units, huge cost savings for plant operators. This paper explains this approach while providing successful case studies, in particular where 12nos. of Liquified Natural Gas (LNG) and Pipeline operators with about 140 gas compressing line-ups has adopted these techniques and significantly reduce the number of trips and improved MTBT.

Abstract 37 | PDF Downloads 26



Reliability, Availability, Trip Reduction, Reliability Growth Model

Carmine Allegorico, Stefano Cioncolini, Marzia Sepe, Illaria Parrella, Liliana Arguello & Ernesto Escobedo (2020). Enhanced Early Warning Diagnostic Rules for Gas Turbines leveraging on Bayesian Network. ASME Turbo Expo 2020: Turbomachinery Technical Conference and Exposition, September 2020, doi: 10.1115/GT202016082

Fausto Carlevaro, Stefano Cioncolini, Marzia Sepe, Illaria Parrella, Carmine Allegorico, Laura De Stefanis, Mariagrazia Mastroianni & Ernesto Escobedo (2020). Use of Operating parameters, digital replicas and models for condition monitoring and improved equipment health. ASME Turbo Expo 2018: Turbomachinery Technical Conference and Exposition, June 2018, doi: 10.1115/GT2018-76849

Dincer Ozgur, Arkalgud N. Lakshminarasimha, Richard Rucigay, Mahesh Morjaria, & S. Sanborn (2000). Remote Monitoring and Diagnostics System for GE Heavy Duty Gas Turbines. ASME Turbo Expo 2000: Power for Land, Sea and Air, May 2000, doi: 10.1115/2000-GT-0314

ReliaSoft. (n.d.) Reliability engineering, reliability theory and reliability data analysis and modeling resources for reliability engineers.
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