Failure analysis of blades and vanes of a compressor for a gas turbine engine

Kwon, Hyeok Jun; Lee, Dooyoul; Lee, Young Kook

doi:10.1016/j.engfailanal.2021.105386

Cited by 19 publications

(6 citation statements)

References 23 publications

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“…Laplacian scores ranks the features based on the intrinsic characteristic and distinction from other features. 40 The nearest neighbor graph G can be calculated using the nearest neighbor method, as expressed in equation (3).…”

Section: Hybridized Feature Selection Using Laplacian Random Forest S...mentioning

confidence: 99%

“…2 With the presence of an artificial flaw and in-service loading of centrifugal force, the early defects are damaged and tended to cracks, which release the acoustic emission (AE) waves during this process. 3 For the structural health monitoring (SHM) of compressor, the crack propagation and potential safety risks can be effectively avoided by monitoring the onset of crack in advance. 4 Therefore, it is significant to effectively monitor the condition of compressor and identify the cracked blades under different working conditions.…”

Section: Introductionmentioning

confidence: 99%

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Crack damage monitoring for compressor blades based on acoustic emission with novel feature and hybridized feature selection

Song

2022

Structural Health Monitoring

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Nowadays, acoustic emission (AE) has been widely used to the nondestructive evaluation (NDE) and structural health monitoring (SHM) for compressor blades. However, traditional AE features and feature selection methods are generally difficult to identify the cracked blades of compressor due to its complex structure and background noise. To solve this problem, the crack damage monitoring method based on novel feature and hybridized feature selection is proposed to identify crack of compressor blades, which is aimed at improving the crack identification accuracy with optimal features. First, the novel feature of spectral centroid with energy shift (SCES) is established. Besides, the hybridized feature selection method is proposed based on Laplacian random forest scores (LRFS), which can evaluate and select features adaptively. By fusing information of selected features from AE sensors, the long short-term memory (LSTM) network is used to classify cracked blades. The proposed method is applied experimentally to identify cracks at different speeds and locations of AE sensors, which has the average accuracy of 98.93%. The comparative results demonstrate the effectiveness and superiority of the proposed method in AE-based SHM for compressor blades under different working conditions.

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Section: Hybridized Feature Selection Using Laplacian Random Forest S...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Crack damage monitoring for compressor blades based on acoustic emission with novel feature and hybridized feature selection

Song

2022

Structural Health Monitoring

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“…Chi W. et al [2] established a relationship model between fatigue strength and defect area after the occurrence of surface defects on TC-4 titanium alloy on the compressor blade and verified it in the test. Kwon H. et al [3] analyzed the fatigue failure of compressor blades and found that the grains of the failed blades are coarser than those of the intact blades, confirming that the coarse grains will shorten the fatigue life of the blades. A.…”

Section: Overview Of the Digestmentioning

confidence: 99%

Research on foreign object damage and grinding repair schemes of compressor blades

Zuo

2022

J. Phys.: Conf. Ser.

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Foreign object damage (FOD) on compressor blades of aero-engines lowers the capacity of blades and poses a serious threat to civil aviation safety, so the repair of blades with FOD deserves more attention. The finite element model of the aero-engine compressor blade was established, and the finite element analysis was carried out on the damaged blades with FOD notches and the blades after grinding and repair. The natural frequencies of different types of blades and the global and local peak stress under rotating conditions were compared. It was confirmed that the stiffness of the repaired blades was acceptable, and the optimal grinding repair scheme was selected.

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“…Gas turbine operates in the hostile conditions of high temperature, high pressure and overload, which means that a wide range of faults are prone to occur (Zdzisiaw et al , 2008). The performance of gas turbine deteriorates with so many factors, and the primary one may be related to its mechanical nature (Kwon and Lee, 2021; Salilew and Karim, 2021). As a pillar of supporting heavy radial loads, roller bearings can work under harsh and unstable conditions, so higher failure rates and repair costs are inevitable (Liu et al , 2020; Jalaja et al , 2021).…”

Section: Introductionmentioning

confidence: 99%

Wear failure analysis of ball bearings based on lubricating oil for gas turbine

Sun¹,

Bu²,

Yang³

et al. 2022

ILT

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Purpose Ball bearings in gas turbine have played a critical role in supporting heavy radial loads but with higher failure rates and repair costs. Therefore, the purpose of this study is to introduce and study a method for their failure analysis with an actual industrial example to guarantee operation reliability and safety. Design/methodology/approach Spectrometric oil analysis was used as an early abnormal wear indicator, based on which emergent in-use oil replacement was carried out to reduce the wear rate. However, with wear deterioration, further wear failure investigation was conducted by LaserNet Fines and ferrography to detect the imminent wear failure. Finally, with the assistance of elemental analysis of the typical wear particles, the root cause and worn components were determined by scanning electronic microscope and energy-dispersive X-ray spectroscopy. Findings The results have shown that an extraneous source led to wear failure, which later caused overheat between the outer bearing ring and ball. It is in accordance with visual inspection of the disassembled engine. Originality/value This method has specified the occasion under which the suitable measurement can be taken. It can achieve the rapid wear condition assessment allowing for root cause and worn parts identification. In addition, wear rate reduction by change of oil can be efficient for most of the time to avoid premature disassemble, especially with the possibility of contamination. It has provided experience to address similar industry-level practical wear failure analysis problems.

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Failure analysis of blades and vanes of a compressor for a gas turbine engine

Cited by 19 publications

References 23 publications

Crack damage monitoring for compressor blades based on acoustic emission with novel feature and hybridized feature selection

Crack damage monitoring for compressor blades based on acoustic emission with novel feature and hybridized feature selection

Research on foreign object damage and grinding repair schemes of compressor blades

Wear failure analysis of ball bearings based on lubricating oil for gas turbine

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