A comprehensive review on mechanical failures cause vibration in the gas turbine of combined cycle power plants

Fahmi, Al-Tekreeti Watban Khalid; Kashyzadeh, Kazem Reza; Ghorbani, Siamak

doi:10.1016/j.engfailanal.2022.106094

Cited by 29 publications

(12 citation statements)

References 75 publications

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“…Eventually, during the manufacturing and handling of any stainless steel, precautions should be taken to avoid contamination. To analyze failure and identify its main causes in industrial parts, it is necessary to research all the possible causes, and in some cases, in addition to using evidence, the answer can be obtained by rejecting the possible causes [11,[165][166][167][168].…”

Section: Practical Solutionsmentioning

confidence: 99%

“…This device converts the kinetic energy of running water into the rotary motion required to turn a generator, and then generates electricity. According to the published reports, cavitation [1][2][3], silt erosion [4][5][6][7], and fatigue [8][9][10][11] are the common reasons for damages to hydro turbines, specifically Kaplan turbines. Since the finding of the first type of hydro turbine (Francis) in 1848 [12], the erosion of hydraulic machines due to cavitation and solid particles has been challenging for engineers involved in the design, construction, operation, and maintenance of HPPs.…”

Section: Introductionmentioning

confidence: 99%

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Common Failures in Hydraulic Kaplan Turbine Blades and Practical Solutions

2023

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Kaplan turbines, as one of the well-known hydraulic turbines, are generally utilized worldwide for low-head and high-flow conditions. Any failure in each of the turbine components can result in long-term downtime and high repair costs. In a particular case, if other parts are damaged due to the impact of the broken blades (e.g., the main shaft of the turbine), the whole power plant may be shut down. On the other hand, further research on the primary causes of failures in turbines can help improve the present failure evaluation methodologies in power plants. Hence, the main objective of this paper is to present the major causes of Kaplan turbine failures to prevent excessive damage to the equipment and provide practical solutions for them. In general, turbines are mainly subjected to both Internal Object Damage (IOD) and Foreign Object Damage (FOD). Accordingly, this paper presents a state-of-the-art review of Kaplan turbine failures related to material and physical defects, deficiencies in design, deficits in manufacturing and assembly processes, corrosion failures, fatigue failure, cavitation wear, types of cavitation in hydro turbines, hydro-abrasive problems, and hydro-erosion problems. Eventually, the authors have attempted to discuss practical hints (e.g., nanostructured coatings) to prevent damages and improve the performance of Kaplan turbines.

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Section: Practical Solutionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Common Failures in Hydraulic Kaplan Turbine Blades and Practical Solutions

2023

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“…The rod-fastening rotor (RFR) system is the crucial power component of gas turbines and aero engines, and its health status significantly influences equipment performance [1,2]. In the pursuit of engine lightness and high thrust-weight ratio, the RFR system has adopted the discontinuous rotor system structure [3,4].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, it is necessary and crucial to study the intelligent diagnosis of tie rod bolt loosening for the safe operation of RFR systems. Three challenging conditions make this task difficult: (1) lack of fault samples due to data collection difficulties and the data privacy protection issue; (2) insufficient sample labels because manual labeling is costly; (3) large distribution discrepancies of the collected data caused by factors including rotor system installation error, working condition change, noise interference, among others.…”

Section: Introductionmentioning

confidence: 99%

DDAGCN: an unsupervised cross-domain identification method for tie rod bolt loosening in a rod-fastening rotor system under different working conditions

Zhou,

He,

Yang

et al. 2024

Meas. Sci. Technol.

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The cross-domain diagnosis of tie rod bolt loosening is essential for guaranteeing the healthy operation of rod-fastening rotor (RFR) systems. The unsupervised domain adaptation (UDA) method effectively alleviates the impact of domain discrepancy and has been applied for cross-domain diagnosis. Traditional UDA methods mainly focus on the marginal and conditional distributions with fixed weights to adapt the domain distribution discrepancy. However, the fixed distribution combination cannot satisfy the requirement of feature domain alignment under different working conditions, and the relative importance of the two distributions cannot be evaluated quantitatively. This paper proposes an improved dynamic distribution adaptive graph convolutional network (DDAGCN) for the cross-domain diagnosis of tie rod bolt loosening under variable working conditions. This method can quantitatively evaluate the relative significance of each distribution in representing the distribution discrepancy. First, it combines the convolutional neural network (CNN) and the graph convolutional network (GCN) to extract the features in the graph structure by using the connection relationship between nodes, and realizes the full extraction of local neighbourhood information of nodes. Then, the dynamic distribution adaptive alignment strategy is introduced to construct the dynamic linear combination of marginal and conditional distributions, so as to measure the distribution discrepancy between domains. Meanwhile, the domain adversarial module is combined to further reduce the domain gap and finally realize feature alignment. The extracted domain invariant features can effectively enhance the generalization ability and fault identification ability of the model. The case of the public bearing dataset verifies that the effectiveness and generalization ability of the proposed method for cross-domain fault diagnosis under variable working conditions is superior to other compared methods. In addition, the identification ability of the proposed method for the degree of tie rod bolt loosening is verified by the self-made bolt loosening dataset of the RFR system.

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“…Gas turbines are important prime movers and widely used in power generation, ships, and other fields [1,2]. Because gas turbine components will spend their overall lifecycle in severe working environments with high pressures or temperatures , it is obvious that many faults could occur in the gas turbine.…”

Section: Introductionmentioning

confidence: 99%

Improved and accurate fault diagnostic model for gas turbine based on 2D-wavelet transform and generative adversarial network

Yao

Wang

Song

et al. 2023

Meas. Sci. Technol.

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severe working environment causes gas turbines to break down, which can directly affect their performance. Research on the diagnostic methods for gas turbine faults, such as, gas path faults and sensor failures, has always raised concerns. However, traditional fault diagnosis algorithms mostly use instantaneous data rather than time-series data, because they cannot efficiently use time-series analysis to extract fault features and improve algorithm accuracy. Problems with sparse fault samples and categories are also encountered with these algorithms. In this study, a gas turbine fault diagnostic method based on a 2D-wavelet transform and generative adversarial network (GAN) was proposed. The data preprocessing method, 2D-wavelet transform, of multiple time series (MTS) images was used to obtain fault features. Based on the Fréchet inception distance (FID), a performance evaluation index, an optimal generator built from a deep convolutional generative adversarial network model was selected to solve sparse or imbalanced datasets. The classification accuracy of the four algorithms, namely, random forest, support vector machine, convolutional neural network, and deep neural network, verified the performance of the data preprocessing and dataset building methods mentioned earlier. Compared with the original data, the 2D wavelet transform effectively improved the model accuracy. The generated samples also improved the misclassification issue caused by the imbalanced dataset; however, the ratio of real and generated samples in datasets still requires more attention.

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A comprehensive review on mechanical failures cause vibration in the gas turbine of combined cycle power plants

Cited by 29 publications

References 75 publications

Common Failures in Hydraulic Kaplan Turbine Blades and Practical Solutions

Common Failures in Hydraulic Kaplan Turbine Blades and Practical Solutions

DDAGCN: an unsupervised cross-domain identification method for tie rod bolt loosening in a rod-fastening rotor system under different working conditions

Improved and accurate fault diagnostic model for gas turbine based on 2D-wavelet transform and generative adversarial network

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