A Study on Engine Health Monitoring in the Frequency Domain

Borguet, Sébastien; Henriksson, Mattias; McKelvey, Tomas; Léonard, Olivier

doi:10.1115/1.4002832

Cited by 5 publications

(1 citation statement)

References 22 publications

(13 reference statements)

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“…These faults include foreign/domestic object damage, corrosion, erosion and/or fouling. Health monitoring methods to track and prevent these faults, predict remaining life and suggest future actions has become a widely researched domain, and the technology is constantly developing (Volponi, 2014;Borguet, Henriksson, McKelvey and Léonard, 2011). Several investigations have been carried out to review the developments in this area (Zhao, Wen and Li, 2016;Fentaye, Baheta, Gilani and Kyprianidis, 2019).…”

Section: Introductionmentioning

confidence: 99%

A Field Programmable Gate Array (FPGA) Based Non-Linear Filters for Gas Turbine Prognostics

Nayak

Prasad

Ganguli

2021

IJPHM

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The removal of noise from signals obtained through the health monitoring systems in gas turbines is an important consideration for accurate prognostics. Several filters have been designed and tested for this purpose, and their performance analysis has been conducted. Linear filters are inefficient in the removal of outliers and noise because they cause smoothening of the sharp features in the signal which can indicate the onset of a fault event. On the other hand, non-linear filters based on image processing methods can provide more precise results for gas turbine health signals. Among others, the weighted recursive median (WRM) filter has been shown to provide greater accuracy due to its weight adaptability depending on the signal type. However, sampling data at high rates is possible which needs hardware implementation of the filter. In this paper, the design, simulation and implementation of WRM filters on the FPGA (Field Programmable Gate Arrays) platforms Vivado Design Suite by Xilinx and Quartus Pro Lite Edition 19.3 has been performed. The architectural detail and performance result with the FPGA filters when subjected to abrupt and gradual fault signal is presented.

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Section: Introductionmentioning

confidence: 99%

A Field Programmable Gate Array (FPGA) Based Non-Linear Filters for Gas Turbine Prognostics

Nayak

Prasad

Ganguli

2021

IJPHM

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A dynamic prognosis scheme for flexible operation of gas turbines

et al. 2016

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The increase in energy demand has led to expansion of renewable energy sources and their integration into a more diverse energy mix. Consequently the operation of thermal power plants, which are spearheaded by the gas turbine technology, has been affected. Gas turbines are now required to operate more flexibly in grid supporting modes that include part load and transient operations. Therefore, condition based maintenance should encapsulate this recent shift in the gas turbine's role by taking into account dynamic operating conditions for diagnostic and prognostic purposes. In this paper, a novel scheme for performance-based prognostics of industrial gas turbines operating under dynamic conditions is proposed and developed. The concept of performance adaptation is introduced and implemented through a dynamic engine model that is developed in Matlab/Simulink environment for diagnosing and prognosing the health of gas turbine components. Our proposed scheme is tested under variable ambient conditions corresponding to dynamic operational modes of the gas turbine for estimating and predicting multiple component degradations. The diagnosis task developed is based on an adaptive method and is performed in a sliding window-based manner. A regression-based method is then implemented to locally represent the diagnostic information for subsequently forecasting the performance behavior of the engine. The accuracy of the proposed prognosis scheme is evaluated through the Probability Density Function (PDF) and the Remaining Useful Life (RUL) metrics. The results demonstrate a promising prospect of our proposed methodology for detecting and predicting accurately and efficiently the performance of gas turbine components as they degrade over time.

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An improved extended Kalman filter with inequality constraints for gas turbine engine health monitoring

Lü

Huang

2016

Aerospace Science and Technology

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A Study on Engine Health Monitoring in the Frequency Domain

Cited by 5 publications

References 22 publications

A Field Programmable Gate Array (FPGA) Based Non-Linear Filters for Gas Turbine Prognostics

A Field Programmable Gate Array (FPGA) Based Non-Linear Filters for Gas Turbine Prognostics

A dynamic prognosis scheme for flexible operation of gas turbines

An improved extended Kalman filter with inequality constraints for gas turbine engine health monitoring

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