Machine Learning in Gas Turbines

Michelassi, Vittorio; Allegorico, Carmine; Cioncolini, Stefano; Graziano, Antonino; Tognarelli, Leonardo; Sepe, Marzia

doi:10.1115/1.2018-sep5

Cited by 13 publications

(7 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Data is sampled at 1/60 Hz and an extreme learning machine (ELM) is adapted for anomaly detection. [Michelassi et al, 2018] presents a very similar work. [Michau et al, 2018] uses deep-learning based anomaly detection methods to identify potential faults in gas turbine data.…”

Section: Turbinesmentioning

confidence: 95%

System-Level Predictive Maintenance: Review of Research Literature and Gap Analysis

Miller¹,

Dubrawski²

2020

Preprint

View full text Add to dashboard Cite

This paper reviews current literature in the field of predictive maintenance from the system point of view. We differentiate the existing capabilities of condition estimation and failure risk forecasting as currently applied to simple components, from the capabilities needed to solve the same tasks for complex assets. System-level analysis faces more complex latent degradation states, it has to comprehensively account for active maintenance programs at each component level and consider coupling between different maintenance actions, while reflecting increased monetary and safety costs for system failures. As a result, methods that are effective for forecasting risk and informing maintenance decisions regarding individual components do not readily scale to provide reliable sub-system or system level insights. A novel holistic modeling approach is needed to incorporate available structural and physical knowledge and naturally handle the complexities of actively fielded and maintained assets.

show abstract

Section: Turbinesmentioning

confidence: 95%

System-Level Predictive Maintenance: Review of Research Literature and Gap Analysis

Miller¹,

Dubrawski²

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…The injection of domain knowledge consists on calculated parameters added to input dataset, like asset performances and fleet baselines calculation and the knowledge-based map between anomaly signature and the specific sensor or system issue. The overall health assessment model is then, again, an example of hybrid approach combining data-driven analytics with physics-based modelling (Michelassi et al, 2018).…”

Section: Functional Health Assessmentmentioning

confidence: 99%

A physics-informed machine learning framework for predictive maintenance applied to turbomachinery assets

Sepe¹,

Graziano²,

Badora³

et al. 2021

J. Glob. Power Propuls. Soc.

View full text Add to dashboard Cite

The paper presents an overview of Baker Hughes digital framework for a predictive maintenance service boosted by Machine Learning and asset knowledge, applied to turbomachinery assets. Optimization of the maintenance scenario is performed through a risk model that assesses online health status and probability of failure, by detecting functional anomalies and aging phenomena and evaluating their impact on asset serviceability. Turbomachinery domain knowledge is used to create physics-based models, to configure a severity assessment layer and to properly map maintenance actions to anomaly types. The implemented analytics framework is able also to forecast engine behaviour over the future in order to optimize asset operation and maintenance, minimizing downtime and residual risk. Predictive capabilities are optimized thanks to the hybrid approach, where physics-based knowledge empowers long term prediction accuracy while data-driven analytics ensure fast-events prognostics. Accuracy of the hybrid approach is a differentiator for maintenance optimization, allowing activities to be planned properly and in early advance with respect to outage execution.

show abstract

“…Analysis of the relationship between the reduction of indicators of the technical state of turbine plants and their individual units with the development of defects will allow predicting the likelihood of emergencies, optimizing maintenance and repairs [2][3][4]. Due to the large amount of operational data, one of the tools for solving such problems can be machine learning [5][6]. The method of determining the technical condition by the effective power of the unit [4] is widely used in assessing the operating conditions of a gas turbine plant.…”

Section: Introductionmentioning

confidence: 99%

Technical Condition Estimation of the Gas Turbine Axial Compressor

Блинов

Deryabin

2022

IOP Conf. Ser.: Earth Environ. Sci.

View full text Add to dashboard Cite

The paper describes an approach for determining the technical state of an axial compressor as part of a gas turbine unit, based on thermogasdynamic parameters that are measured at the operating facility. As a criterion for the technical condition, it is proposed to use the ratio of the actual efficiency of an axial compressor to the reference efficiency in such modes. To do this, it is enough to know the temperatures and pressures of the air at the inlet and outlet of the axial compressor. A method for selecting such modes is described and some features of filtering the operating parameters of a gas turbine plant are noted. In the proposed approach, the choice of modes for analyzing the technical state of an axial compressor is carried out according to the value of the effective power of the gas turbine unit. Some results of determining the criterion of the technical condition of an axial compressor for a gas turbine unit, which drives a centrifugal compressor of natural gas, are presented. Based on the analysis of the degradation of the technical condition criterion, it is possible to determine its predicted value. The results of the work can be used to create automated systems for assessing and predicting the technical condition of drive gas turbine units at their facilities. The application of the obtained results of the work expands the possibilities of servicing gas turbine plants according to the actual state. The proposed approach can be adapted and applied for gas turbine plants for various purposes, as well as for their individual units.

show abstract

Machine Learning in Gas Turbines

Abstract: This paper describes a selection of Baker Hughes, a GE company (BHGE) activities to support Gas Turbine (GT) design and operation from simple to more elaborate applications of Machine Learning (ML).

Cited by 13 publications

References 1 publication

System-Level Predictive Maintenance: Review of Research Literature and Gap Analysis

System-Level Predictive Maintenance: Review of Research Literature and Gap Analysis

A physics-informed machine learning framework for predictive maintenance applied to turbomachinery assets

Technical Condition Estimation of the Gas Turbine Axial Compressor

Contact Info

Product

Resources

About