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Performance-Based Gas Turbine Health Monitoring, Diagnostics, and Prognostics: A Survey

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TLDR
This paper focuses on surveying state-of-the-art condition monitoring, diagnostic and prognostic techniques using performance parameters acquired from gas-path data that are mostly available from the operating systems of gas turbines.
Abstract
Health monitoring is an essential part of condition-based maintenance and prognostics and health management for gas turbines. Various health monitoring systems have been developed based on the measurement and observation of the fault symptoms including turbine performance parameters such as heat rate, and nonperformance symptoms such as structural vibration. This paper focuses on surveying state-of-the-art condition monitoring, diagnostic and prognostic techniques using performance parameters acquired from gas-path data that are mostly available from the operating systems of gas turbines. Performance parameters and the corresponding effective factors are presented in the beginning. Structure of performance monitoring and diagnostic systems are systematically laid out next, and the recent developments in each section are surveyed and discussed. Observing the importance of the prognostics in the recent trend of health monitoring research, an emphasis is given on the prognostic frameworks and their implementation for the remaining useful life prediction. A conclusion along with a brief discussion on the current state and potential future directions is provided at the end.

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Citations
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Journal ArticleDOI

A hybrid prognostic method for system degradation based on particle filter and relevance vector machine

TL;DR: The experimental results show that the proposed hybrid prognostic scheme with the capability of uncertainty assessment is a reliable prognostic method which can ensure the accuracy of the deterministic prediction result and provide precise assessment for the prediction uncertainty.
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Transfer learning for remaining useful life prediction based on consensus self-organizing models

TL;DR: In this paper, a feature-representation based transfer learning (TL) method for predicting the remaining useful life (RUL) of an equipment was proposed, under scenarios that samples with previously unseen conditions are presented in the target domain and the labels are available only for the source domain, but not the target domains.
Journal ArticleDOI

A sequential model-based approach for gas turbine performance diagnostics

TL;DR: A novel performance diagnostic method that partitions the engine diagnosis into a series of steps to remove the “smearing effect” and reduce the matrix dimensions in the iterative diagnostic algorithm, which provides an accurate diagnosis with a reduced set of measurements.
Journal ArticleDOI

A review of condition-based maintenance: Its prognostic and operational aspects

TL;DR: Several kinds of prognostic models that use monitoring information to estimate the reliability of complex systems or products and facilitates operational decisions in production planning, spare parts management, reliability improvement, and prognostics and health management are summarized.
Journal ArticleDOI

Gas turbine aero-engines real time on-board modelling: A review, research challenges, and exploring the future

TL;DR: A historical review of on-board modelling applied on gas turbine engines is offered and its limitations, and consequently the challenges, which should be addressed to apply the on- board real time model to new and the next generation gas turbine aero-engines are established.
References
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Journal ArticleDOI

Support-Vector Networks

TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
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ANFIS: adaptive-network-based fuzzy inference system

TL;DR: The architecture and learning procedure underlying ANFIS (adaptive-network-based fuzzy inference system) is presented, which is a fuzzy inference System implemented in the framework of adaptive networks.
Journal ArticleDOI

An introduction to variable and feature selection

TL;DR: The contributions of this special issue cover a wide range of aspects of variable selection: providing a better definition of the objective function, feature construction, feature ranking, multivariate feature selection, efficient search methods, and feature validity assessment methods.
Journal ArticleDOI

The Elements of Statistical Learning

Eric R. Ziegel
- 01 Aug 2003 - 
TL;DR: Chapter 11 includes more case studies in other areas, ranging from manufacturing to marketing research, and a detailed comparison with other diagnostic tools, such as logistic regression and tree-based methods.
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