Dynamic stress analysis and a fracture mechanics approach to life prediction of turbine blades

Vyas, Nalinaksh S.; Sidharth,; Rao, J. S.

doi:10.1016/s0094-114x(96)00067-5

Cited by 22 publications

(13 citation statements)

References 6 publications

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“…Turbines are widely used in aero-engines as the key energy transformation component, and their health conditions are highly concerned with the regular operation of the aeroengine. Since turbines usually work in the harsh operating environment caused by high temperature, high stress, and high speeds during operation [1], they are easily prone to…”

Section: Introductionmentioning

confidence: 99%

A Novel Intelligent Fault Diagnosis Approach for Early Cracks of Turbine Blades via Improved Deep Belief Network Using Three-Dimensional Blade Tip Clearance

et al. 2021

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Aero-turbines usually work in various non-stationary and harsh operating environments. Its blades easily appear early crack faults in long-time operation, and it is difficult to dig out discriminant features of early crack. To solve these problems, in the paper, a novel intelligent approach for early crack diagnosis of turbine blades using three-dimensional blade tip clearance is presented. In order to improve feature learning ability to obtain better generalization ability, the paper firstly develops a novel deep learning method based on deep belief networks (DBNs). Considering the fact that the feature degradation easily occurs in deeper layers because of the change of the distribution in each layer's outputs with the increase of the layers, it is hard to decide which layer to learn features is useful for fault diagnosis. Accordingly, in the pre-training process, the global back-reconstruction (GBR) mechanism is introduced into DBNs to optimize the feature learning ability. The GBR mechanism can be realized between the input layer and hidden layers by ''shortcut connection'', and the layer to learn more discriminant features can be determined automatically without prior knowledge. Moreover, due to three-dimensional blade tip clearance (3-DBTC) acquired from three different directions of turbine blades contains much more useful crack failure multiscale information, it is suitable to be used as an input from which to extract multiscale discriminant features. Eventually, in the supervised training, the softmax regression model is employed to classify the health conditions of turbine blades using these sensitive features learned from 3-DBTC. The experimental results show that the proposed method can effectively identify the crack of turbine blades with fairly high diagnostic accuracies and significantly outperform other methods considered in the paper.INDEX TERMS Turbine blade, three-dimensional blade tip clearance, deep belief networks, feature extraction, fault diagnosis.

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Section: Introductionmentioning

confidence: 99%

A Novel Intelligent Fault Diagnosis Approach for Early Cracks of Turbine Blades via Improved Deep Belief Network Using Three-Dimensional Blade Tip Clearance

et al. 2021

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“…As a core power component, the blisk works in the environment of high temperature, high pressure and high rotation speed, and it is subjected to the combined action of centrifugal load, aerodynamic load and vibration alternating load. 1,2 The harsh working environment leads to the failure of blades, such as creep, 3,4 erosion, 5,6 corrosion, 7 oxidation 8 and so on. In addition, because of manufacturing tolerances, non-uniform material properties, wear, and operational damages, there are always small and random deviations called mistuning in blade properties.…”

Section: Introductionmentioning

confidence: 99%

Mistuning identification for coated blisks using small amount of experimental data

Yan

Sun

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part C:

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Because the coatings are likely to produce new mistuning which aggravates the vibration failure of coated blades, it is particularly important to study the mistuning identification for coated blades. In this study, the degrees of freedom of the whole disk of the coated blisk is completely constrained, and the orders of the dynamic model of the coated blisk based on component mode mistuning (CMM) method is further reduced. Based on this model, a novel mistuning identification method for the coated blade of the coated blisk by small amount of experimental data is proposed, which can identify the mass, stiffness and damping mistuning of the blade substrates and coatings. The input parameters with high amount of data, such as modal shape matrix, are removed from identification formulas, thus, both the amount of data for identification calculation and the workload of vibration testing are reduced. The mistuning identifications of the numerical case and actual case are carried out respectively, and the identification process of the mistuning identification method in this paper is compared with the one based on the classical CMM model. It is shown that the mistuning identification method is effective and feasible, and the identification efficiency is higher than the classical CMM identification method.

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“…Scholars at home and aboard have carried out significative research on the impeller failure modes, summing up the following two main reasons for the cause of the impeller failure fracture: one is its own defects or material processing defects and the other is the alternating stress fatigue. The material and manufacturing defects are well understood [2][3][4][5][6][7][8]. It is shown that alternating stress in internal compressor is mainly from unsteady flow and aerodynamic loads [5,[9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

“…It is shown that alternating stress in internal compressor is mainly from unsteady flow and aerodynamic loads [5,[9][10][11][12]. Combining the preceding research, the fatigue life design theory is proposed [3,4,13,14].…”

Section: Introductionmentioning

confidence: 99%

Study on Impeller Fracture Model Based on Vibration Characteristics and Fractal Theory

Zhang

Yuan

Xie

2015

Advances in Mathematical Physics

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During the operation of centrifugal compressor, failure easily occurs in the presence of complicated external forces. The failure process characterizes with strong nonlinearity, and hence it is difficult to be described by conventional methods. In this paper, firstly, the cracks in different positions are described using crack fractal theory. The basic failure modes of the impeller are summarized. Secondly, a three-dimensional finite element model of the impeller is constructed. Then the von Mises stress under the centrifugal force is calculated, and the corresponding impeller failure process is simulated by “element life and death technology” in ANSYS. Finally, the impeller failure mechanism is analyzed. It can be found that the static stress is not the main cause of the impeller failure, and the dynamic characteristics of the impeller are not perfect because of the pitch vibration modes which appeared in the investigated frequency range. Meanwhile, the natural frequency of the impeller also cannot avoid the frequency of the excitation force.

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Dynamic stress analysis and a fracture mechanics approach to life prediction of turbine blades

Cited by 22 publications

References 6 publications

A Novel Intelligent Fault Diagnosis Approach for Early Cracks of Turbine Blades via Improved Deep Belief Network Using Three-Dimensional Blade Tip Clearance

A Novel Intelligent Fault Diagnosis Approach for Early Cracks of Turbine Blades via Improved Deep Belief Network Using Three-Dimensional Blade Tip Clearance

Mistuning identification for coated blisks using small amount of experimental data

Study on Impeller Fracture Model Based on Vibration Characteristics and Fractal Theory

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