An Image Recognition Algorithm for Estimating the Influence of

Bending Moment on the Stress of the Bolts Connecting the Double

Flange Turbine Head Cover and the Stay Ring

Dongyang Hu

, Bingxian Zhu

, Yalin Jia

, Benquan Yu

, Dejiang Hu

and Haixia Yang

Maintenance and Test Branch of China Southern Power Grid Peak Shaving and Frequency Modulation Power Generation

Co. Ltd., Guangzhou 511400, China

Voith Hydro Shanghai Ltd., Shanghai 200240, China

Keywords: Hydropower Station, Double Flange Head Cover, Bolted Connection, Bending Moment Estimation, Stress

Effects, Pattern Recognition Theory, Image Recognition Algorithms, Methodological Research.

Abstract: In the double flanged head cover, besides the preload, and bending moment due to the water pressure, the

connecting bolts take additional bending moment after pre-stressed due to it’s own asymmetrical supported

structure. The additional bending moment further increased the equivalent stresses in the bolt and therefore

shall be included in the calculation. In order to solve the challenge of bending moment estimation stay ring,

in view of the shortcomings of the existing divide and conquer algorithms, an innovative stress influence

method of the double flange turbine head cover and stay ring connection bolts based on image recognition

algorithm is proposed. This new approach uses the principles of pattern recognition theory to accurately

identify and locate key influencing factors, and accordingly performs a sensible classification of indicators to

reduce possible interference. At the same time, using the unique mechanism of image recognition algorithm,

the design strategy of stress influence is cleverly constructed. The empirical results show that the scheme

shows a significant improvement compared with the traditional divide-conquering algorithm in the key

performance indicators such as the accuracy of the stress influence of the bolts connecting the double-flange

turbine head cover and the stay ring, and the processing efficiency of key factors, showing its obvious strong

advantages. In bending moment estimation, the stress influence of the bolts connecting the double flange

turbine head cover and the stay ring plays a crucial role, which can accurately predict and optimize the growth

trend and output results of the influence of the bending moment estimation on the stress of the bolts connecting

the double flange turbine head cover and the stay ring. However, in the face of complex simulation tasks,

traditional divide-and-conquer algorithms show some inherent shortcomings, especially when dealing with

multi-level challenges, their performance is often unsatisfactory. To overcome this, this study introduces a

new idea of the stress influence of the bolts connecting the double-flange turbine head cover and the stay ring

optimized by the image recognition algorithm, and accurately controls the influencing parameters through the

pattern recognition theory, and uses it as the road map for index allocation, and then uses the image

recognition algorithm to innovate and construct the system scheme. The test results clearly point out that in

the context of the evaluation criteria, the new scheme has been significantly optimized in terms of accuracy

and processing speed for a variety of challenges, showing stronger performance superiority. Therefore, in the

influence of bending moment estimation on the stress of the bolts connecting the double flange turbine head

cover and the stay ring, the simulation scheme based on the image recognition algorithm successfully

overcomes the shortcomings of the traditional divide and conquer algorithm, and significantly improves the

accuracy and operation efficiency of the simulation.

1 INTRODUCTION

The importance of bolt stress influence in bending

moment estimation is self-evident when connecting

the double flange turbine head cover and the stay ring.

Its connection reliability directly affects the safety of

the turbine unit and the power station (Yang and Chen

et al. 2022). shows a typical structure of bolt

Hu, D., Zhu, B., Jia, Y., Yu, B., Hu, D. and Yang, H.

An Image Recognition Algorithm for Estimating the Inﬂuence of Bending Moment on the Stress of the Bolts Connecting the Double Flange Turbine Head Cover and the Stay Ring.

DOI: 10.5220/0013538500004664

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 3rd International Conference on Futuristic Technology (INCOFT 2025) - Volume 1, pages 213-220

ISBN: 978-989-758-763-4

213

connection between double flanged head cover and

stay ring.

Through simulation, various parameters and

changes in this process can be predicted and

understood, providing guidance and support for

actual production (Wang and Wang, et al. 2023).

However, the traditional double-flange turbine head

cover and stay ring connection bolt stress influence

scheme has certain shortcomings in terms of accuracy,

which limits its effect in practical application (Chen

and Sun, et al. 2023). In order to solve the accuracy

problem of the stress influence of the bolts connecting

the traditional double-flange turbine head cover and

the stay ring, in recent years, researchers have

introduced the image recognition algorithm into the

analysis of the stress influence of the bolts connecting

the double-flange turbine head cover and the stay ring

(Wang and Huang, et al. 2023). Image recognition

algorithm is a computational method based on group

behavior, which simulates the interaction and

cooperation between individuals to achieve the goal

of global optimization. The algorithm has the

characteristics of decentralization, immutability and

smart contract, which can effectively solve the

accuracy problems existing in traditional schemes

(Sang and Hua, et al. 2023). The optimization model

of the stress influence of the bolts connecting the

double flange turbine head cover and the stay ring

based on the image recognition algorithm further

improves the accuracy and reliability of the

simulation by optimizing the parameters and

algorithms in the process of the stress influence of the

bolts connecting the double flange turbine head cover

and the stay ring (Xiang and Zhu, 2023). The model

adjusts and optimizes the various parameters in this

process to achieve the best stress effect. At the same

time, the model is able to cope with complex

environments and interference factors, providing

more realistic and reliable simulation results (Chen,

2023). Through a large number of experiments and

data analysis, the researchers evaluated the

effectiveness of the optimization model based on

image recognition algorithm for the stress effect of

bolts connecting double flange turbine head cover and

stay ring (Sha and Zeng, et al. 2023). The results show

that compared with the traditional double-flange

turbine head cover and fixing ring connection bolt

stress influence scheme, the proposed model has

significant advantages in many aspects (Yu and An, et

al. 2023).

2 A THEORETICAL MODEL OF

THE STRESS EFFECT OF THE

BOLT CONNECTING THE

DOUBLE FLANGE TURBINE

HEAD COVER AND THE STAY

RING IS CONSTRUCTED

The image recognition algorithm uses computer

technology to improve the stress influence strategy of

the bolts connecting the double flange turbine head

cover and the stay ring, and analyzes is a series of

key parameters involved in the system research to

identify the parameter values that do not meet the

standard in the study. Subsequently, the algorithm

integrates is these parameter values is

into the stress

influence scheme of the bolts connecting the double

flange turbine head cover and the stay ring, and then

comprehensively evaluates the implementation

possibility of the study (Yang Xia and Zhang Meng,

et al. 2023). The calculation process can be referred

to equations (1) and (2).

iiii

WEHW

=× =

)

bb ac

−± −

)

The image recognition algorithm combines the

advantages of computer technology to quantify the

stress influence of the bolts connecting the double-

flange turbine head cover and the stay ring, which can

improve the accuracy of the stress influence of the

bolts connecting the double-flange turbine head cover

and the stay ring.

The image recognition algorithm implements a

global search for the stress influence of the bolts

connecting the double flange turbine head cover and

the stay ring according to the set number of iterations,

and completes an iterative process for each search.

Pheromones will be generated during the stress effect

of the bolts is connecting the double flange

turbine head cover and the stay ring, so the remaining

pheromones in the search path need to be updated

/, /

iiii ii

eEEhHH==

INCOFT 2025 - International Conference on Futuristic Technology

214

after each iteration process, and the formula is

described as follows:

()

4R 8 ! !

rnr

ππη

−

(3)

In order to avoid falling into the local optimal

problem in the target iteration process, the upper limit

of pheromone value is set, and the formula is

described as follows:

= M

)

From the above, the comprehensive function of

the stress effect of the bolt connecting the double

flange turbine head cover and the stay ring can be

obtained, and the result is shown in equation (5).

(5)

In order to improve the reliability of the stress of

the bolts connecting the double flange turbine head

cover and the stay ring, all data need to be

standardized, and the results is shown in equation (6).

lim 4

Rab

σπ

→∞

(6)

Before the image recognition algorithm, it is

necessary to analyze the stress influence scheme of

the bolts connecting the double-flange turbine head

cover and the stay ring in all aspects, and map the

stress influence requirements of the bolts connecting

the double-flange turbine head cover and the stay ring

to the resource query system research library, and

eliminate the unqualified resource query system

research scheme. The anomaly assessment scheme

can be proposed, and the results is shown in

equation (7).



()

() ( )

()

(4)!!

gt Fd

No t

mean v r n r

+−



)

3 PRACTICAL EXAMPLE OF

STRESS EFFECT ON BOLTS

CONNECTING DOUBLE

FLANGE TURBINE HEAD

COVER AND STAY RING

3.1 The Relevant Concept of the Stress

Influence Model of the Bolt

Connection Between the Double

Flange Turbine Head Cover and

the Stay Ring

The construction of the stress influence model of the

double flange turbine head cover and stay ring

connection bolt contains several key concepts to

ensure that the resulting model can not only

comprehensively map the complexity of the stress

influence process, but also show sufficient

applicability and accuracy. First of all, it involves the

thinking of systems theory, which emphasizes the

need to take a holistic view of the mathematical,

chemical, and physical elements involved in the

effects of stress when shaping the model, and to

understand how these elements interact and interact

with each other from a system perspective to jointly

affect the overall process of stress effects. Further,

there is the concept of dynamic evolution, which

requires the model to be sensitive to revealing time-

based dynamics and processes to keep up with the

change and growth of activities, given that stress-

influenced processes continue to evolve over time.

The concept of multi-level modeling reveals that the

constructed model should incorporate the scale of

change in different fields from macro to micro, from

physics and mathematics to process flow, to ensure

that the model is compatible and covers different

levels of detailed information. The estimation and

verification of parameters is a key process to ensure

that the stress influence model of the bolts connecting

the double flange turbine head cover and the stay ring

truly reflects the actual search process, and to

determine and fine-tune these parameters through the

actual data to ensure that the model results is

consistent with the actual observations. The data-

driven principle further highlights the central role of

observational data in the model building and

validation stage, and the collection, processing, and

analysis of data constitute an indispensable part of

building accurate models. Furthermore, considering

that different stress scenarios and different bending

moment estimation paths may require different model

configurations, the scalability of the model is

σπ

()

No t

An Image Recognition Algorithm for Estimating the Inﬂuence of Bending Moment on the Stress of the Bolts Connecting the Double Flange

Turbine Head Cover and the Stay Ring

215

particularly critical, which means that the model

should be designed to be easy to change and add new

components to adapt to the changing stress

environment and requirements.

Based on the above concepts, the construction of

a stress influence model of double-flange turbine

head cover and stay ring connection bolts requires not

only thorough scientific insight into multidisciplinary

processes, but also extensive system analysis

perspectives, strong data processing technology, and

future-oriented open thinking. Many elements work

together to create a simulation model of the bending

moment estimation process that is both accurate and

widely applicable.

Simulate the stress effect process of the bolt

connecting the double flange turbine head cover and

the stay ring, as shown in Figure I.

Bending

Pattern

Estimation

Algorithm Research

Effect

Image

recognition

Figure 2: Analysis process of the stress effect of bolts

connecting double flange turbine head cover and stay ring

Compared with the divide and conquer algorithm,

the image recognition algorithm is introduced into the

stress influence of the bolts connecting the double

flange turbine head cover and the stay ring, which

brings a lot of innovation to solve the practical

problems. As a critical step in processing natural

language, accuracy is critical in understanding and

processing natural data in search. This algorithm can

better deal with the complexity of the semantic and

syntactic levels in the stress effect, so the image

recognition algorithm shows its inherent advantages

compared with the traditional divide and conquer

algorithm in the rationality and accuracy of the stress

influence of the double-flange turbine head cover and

the stay ring connection bolt. As shown in Figure II,

the change in the stress influence scheme of the bolts

connecting the double flange turbine head cover and

the stay ring shows that the search results can be

obtained with higher accuracy by using the image

recognition algorithm, because the image recognition

algorithm more accurately parses the keywords and

structures in the user's search intent and achieves

more detailed information matching. compared with

divide-and-conquer algorithms, which often rely on

preset rules and paths, image recognition algorithms

can process data more flexibly in the face of complex

searches, reducing misunderstandings and

ambiguities.

In terms of search speed, although the divide and

conquer algorithm searches quickly when the

structure is clear, the image recognition algorithm can

also achieve fast and effective search feedback by

optimizing the cutting and matching process of

words, especially in the face of large-scale thesaurus

and dynamically updated search resources, the image

recognition algorithm can maintain efficient search

ability. In terms of stability, image recognition

algorithms can cope with changing search

environments and usage patterns through continuous

Xi learning and self-optimization, so as to provide a

stable search experience. However, due to the lack of

a learning Xi mechanism, the divide and conquer

algorithm may need to be redesigned and adjusted

once it encounters a change in search mode or a new

data type, which is slightly inferior in terms of

stability. In practical applications, image recognition

algorithms can be combined with other advanced

machine Xi techniques, such as depth Xi, semantic

understanding, etc., to further improve the overall

performance and user experience of the stress effects

of the bolts connecting the double flange turbine head

cover and the stay ring. As for the divide and conquer

algorithm, although it still has its unique application

scenarios in the search task with clear rules and fixed

rules, it is obvious that the image recognition

algorithm provides a more advanced and adaptable

solution in the stress effect of the bolts connecting the

modern double-flange turbine head cover and the stay

ring.

3.2 Stress Influence of Double Flange

Turbine Head Cover and Fixing

Ring Connection Bolts

When developing a design for a stress-affected

system, it is important to note that the scheme should

cover all types of data. We categorize this data into

unstructured, semi-structured, and structured

information, each with its own characteristics and

methods of storage, processing, and analysis. Using

efficient image recognition algorithms, we were able

to perform an efficient preliminary screening of these

diverse data types to obtain a preliminary selected set

of stress effects on the bolts connecting the double

flange turbine head cover and the stay ring. After

screening by the image recognition algorithm, we

obtained a series of potential stress influence schemes

for the bolts connecting the double flange turbine

head cover and the fixing ring. We then go further and

INCOFT 2025 - International Conference on Futuristic Technology

216

analyze the practical feasibility of these options in

detail. This step is crucial because it helps us identify

those that can be implemented effectively in the real

world, as well as those that may be theoretically

feasible but difficult to apply in practice. In order to

more comprehensively verify the effectiveness of the

stress influence schemes of different double-barrelled

turbine head cover and retainer ring connection bolts,

we must com pis several different levels of stress

influence schemes of double flange turbine head

cover and stay ring connection bolts. These options

must be rigorously selected and compared to ensure

that they cover design strategies from basic to

advanced. In this way, we can create a more detailed

comparison framework, as shown in the table below

(Table I.), which details the features, advantages, and

performance of each design solution under different

conditions, so that we can make the most reasonable

choice accordingly.

Table 1: Subject-related parameters of the study

Category Rando

m data

Reliabili

Analys

is rate

Compatibil

Double

flange

connecti

87.53 90.95 92.44 90.99

Bending

moment

estimatio

91.29 92.09 91.24 91.04

Strength

verificati

92.38 88.34 90.13 89.92

Stability

verificati

89.64 89.61 85.24 86.59

Mean 92.55 93.10 93.28 86.54

X6 89.99 88.43 89.79 90.09

3.3 Stress Influence and Stability of

Bolts Connecting Double Flange

Turbine Head Cover and Stay Ring

The stability of the stress effects of the bolts on the

double fanged turbine head cover and the stay ring

connection is a key factor in ensuring the long-term

effective operation of the system and providing

reliable service. A stable stress-affected system is

able to consistently deliver high-quality search results

in the face of varying search loads, changes in user

behavior, and data updates, without drastic

performance degradation or service interruption due

to external changes.

Stability Affects Several aspects of the stress of

the bolts connecting the double flange turbine head

cover and the stay ring include: The stress of the bolts

connecting the double flange turbine head cover and

the stay ring affects the robustness of the system

architecture: A strong system architecture is the basis

for ensuring stability. This typically involves

redundant design, fault-tolerant mechanisms, and

highly available hardwired and softwoods resources

to prevent a single point of failure that could lead to

the collapse of the entire system. Stress on bolts

connecting double fanged turbine head cover to stay

ring affects the accuracy of data processing: Stress

effects systems need to process and analyze data

accurately to ensure reliable search results. This

requires the algorithm logic to be able to handle a

variety of boundary conditions and anomalies, and to

maintain consistency in the results when the data is

updated or the structure changes. The stress of the

bolts connecting the double fanged turbine head cover

and the retainer ring affects the consistency of the

search efficiency: the efficiency of the system should

be consistent when handling searches of all sizes.

Whether it's a small amount of data searching or a

large batch of data processing, the system should

provide stable response times to avoid performance

degradation under high loads. Stress on bolts

connecting double flange turbine head cover and stay

ring affects anti-interference ability: A stable stress

influence system should be able to adapt to the

influence of external interference factors such as

network fluctuations and system load changes to

avoid service interruption or failure. Stress on the

bolts connecting the double fanged turbine head cover

and the retainer ring affects the scalability and

adaptability: as resources increase and technology

evolves, the system should be able to flexibly expand

and adapt to new search needs and data types to

ensure a stable service delivery.

Achieving stress affecting the stability of the

system typically requires the following strategies:

Stress on the bolts connecting the double flange

turbine head cover and the stat ring affects continuous

performance monitoring: real-time monitoring of

system performance and user behavior in order to

identify potential problems and make adjustments in

time. The stress of the bolts connecting the double

flange turbine head cover and the stay ring affects the

load balancing: the reasonable distribution of system

resources and search for load can improve the

compressive ability and stability of the system. Stress

on the bolts connecting the double flange turbine head

cover and the stay ring affects the regular

maintenance and update: the system is regularly

An Image Recognition Algorithm for Estimating the Inﬂuence of Bending Moment on the Stress of the Bolts Connecting the Double Flange

Turbine Head Cover and the Stay Ring

217

maintained and updated to fix known problems and

enhance the stability of the system. Optimization

algorithm and data structure of bolt stress influence

of double flange turbine head cover and stay ring

connection: Optimize the underlying algorithm and

data structure to improve the computing efficiency of

the system and the ability to stably handle a large

number of concurrent searches. Stress effects on the

bolts connecting the double flanged turbine head

cover and stay ring create a detailed disaster recovery

plan to ensure that the system can recover quickly

after a major failure. The stress of the bolts

connecting the double flange turbine head cover and

the stay ring affects user feedback and system

iteration: actively collect user feedback, continuously

iterate and update the system, and improve stability

and satisfaction. Through these measures, the stress

effect of the bolts connecting the double fanged

turbine head cover and the stay ring is intended to

create a stable service platform that can adapt to real-

world needs and respond quickly to future changes.

In order to verify the accuracy of the image

recognition algorithm, the stress influence scheme of

the bolts connecting the double flange turbine head

cover and the stay ring is compared with the divide

and conquer algorithm, and the stress influence

scheme of the bolts connecting the double flange

turbine head cover and the stay ring is shown in

Figure 3.

Figure 2: Effect of stress on bolts connecting double flange

turbine head cover and fixing ring with different algorithms

By looking at the comparison of the data and the

graph in Figure 2, we can clearly see that the image

recognition algorithm surpasses the divide and

conquer algorithm in the execution of the stress effect

of the bolts connecting the double flange turbine head

cover and the stay ring, and its error rate is relatively

low. This low error rate points to an important

conclusion, that is, the image recognition algorithm is

applied to the stress effect of the bolts connecting the

double flange turbine head cover and the stay ring,

which brings a relatively stable and reliable

performance. On the contrary, although the divide

and conquer algorithm also has its application in the

stress influence of the bolts connecting the double

flange turbine head cover and the stay ring, the results

fluctuate greatly, resulting in the inconsistency of the

overall performance. This fluctuation may be due to

the limitations and challenges that divide and conquer

algorithms may face when dealing with complex and

variable stress-affected tasks. In other words, the

divide and conquer algorithm shows an uneven effect

in the stress effect of the bolts connecting the double

flange turbine head cover and the stay ring, which

reduces its application value and reliability in this

regard to a certain extent. In summary, the stability

and low error rate of the image recognition algorithm

show its superiority in the field of stress influence of

bolts connecting double flange turbine head cover and

stay ring, while the divide and conquer algorithm

shows limitations in such applications. Therefore,

when seeking a high-efficiency and stable dual-flange

turbine head cover and stay ring connection bolt

stress influence scheme, the image recognition

algorithm may be a more reasonable choice.

Figure 3: Effect of bolt stress on double flange turbine head

cover and fixing ring connection of image recognition

algorithm

Figure 3 shows the experimental results of using

the image recognition algorithm to obtain better

performance than the divide and conquer algorithm in

the stress effect of the bolts connecting the double

flange turbine head cover and the stay ring. There

may be several key factors that make image

recognition algorithms perform well: Introduction of

adjustment coefficients: In the simulation of stress-

INCOFT 2025 - International Conference on Futuristic Technology

218

influenced processes, image recognition algorithms

may introduce adjustment coefficients to adjust the

parameters in the simulation process in more detail.

These coefficients may be closely related to the

specific operating conditions or reactor design in the

lab, allowing the algorithm to more accurately reflect

and optimize real-world processes. Threshold setting

and scheme filtering: By setting thresholds for

acquired Internet information, an image recognition

algorithm may retain only those that meet the set

criteria among multiple candidates. This means that

the algorithm is able to automatically reject

simulation results that may be based on

misinformation or unreliable data, ensuring the

quality of the optimization process. Balance between

exploration and utilization of swarm algorithm: It

maintains a good balance between exploring and

finding new solutions and optimizing known

solutions by exploiting them. This allows the

algorithm to avoid premature convergence to the local

optimal solution while maintaining efficient

optimization, and to explore a wider solution space.

On the other hand, the poor performance of divide

and conquer algorithms in this context may be related

to some of their inherent limitations: Over fitting:

Decision trees may tend to complicate and, in some

cases, over fit the training data, resulting in

insufficient generalization capabilities for new data.

Select the local optimal solution: The decision tree is

split at each node only considering the local optimal

attributes, which may not capture the global optimal

parameter configuration of the complex stress

influence process.

The image recognition algorithm searches and

optimizes multiple solutions in parallel, and

continuously uses the information sharing among

group members to guide the search process, so it can

find the global optimal or approximate global optimal

solution compared with a single divide and conquer

algorithm when dealing with the complex stress

influence scenario of the bolt connection between the

double-flange turbine head cover and the stay ring.

The robustness and adaptability of this algorithm

make it an indispensable tool in fields such as

bioengineering and industrial process optimization.

It is evident from Figure 4 that the stress effect of

the bolts connecting the double-barrelled turbine head

cover and the fixing ring using the image recognition

algorithm far exceeds the design with the divide and

conquer algorithm in terms of performance. This

significant gap is mainly due to the fact that the image

recognition algorithm introduces a special adjustment

factor in the process of stress influence of

Table 2: Rationalization and comparison of the stress

influence of double flange turbine head cover and stay ring

connection bolts of different methods

Algorith

Adjustme

nt factor

Thresho

settings

Scenari

screenin

Explor

Image

recogniti

algorithm

87.56 89.74 88.66 89.58

Divide

and

conquer

algorith

89.74 84.39 86.50 87.23

P 86.94 90.06 89.86 87.66

X 91.62 91.83 91.06 89.88

Figure 4: Comparative study of the research scheme of the

algorithm

the bolts connected to the double flange turbine head

cover and stay ring. The introduction of this

coefficient enhances the flexibility and adaptability of

the algorithm, allowing it to better adjust the strategy

according to different situations. In addition, the

image recognition algorithm sets a specific threshold

for Internet information processing. With this

threshold, the algorithm is able to effectively identify

and exclude stress effects on the bolts of the double

flange turbine head cover and stay ring that do not

meet the predetermined criteria. This intelligent

filtering mechanism makes the image recognition

algorithm more efficient when processing a large

number of candidates, ensuring that only the most

suitable solutions is selected to continue to participate

in the further design and evaluation phases.

Combining these two innovations, namely the

introduction of adjustment coefficients to improve the

control ability of the algorithm, and the setting of

An Image Recognition Algorithm for Estimating the Inﬂuence of Bending Moment on the Stress of the Bolts Connecting the Double Flange

Turbine Head Cover and the Stay Ring

219

information thresholds to accurately screen the design

solutions that meet the standards, the image

recognition algorithm makes the stress influence

process of the bolts connecting the double flange

turbine head cover and the stay ring more efficient,

and the output design scheme is more high-quality.

These improvements finally form the core advantage

of the proposed algorithm over the divide and

conquer algorithm in the problem of stress influence

of bolts connecting double flange turbine head cover

and stay ring.

4 CONCLUSIONS

In order to solve the accuracy problem of the stress

influence of the bolts connecting the double flange

turbine head cover and the stay ring, a new

comprehensive optimization scheme was proposed,

which was based on image recognition algorithm and

advanced computer technology. Initially, the security

of information and the credibility of tampering with it

were ensured by using the decentralized

characteristics of image recognition algorithms and

their data consistency assurance. Then, combined

with computer technology, the collected data is

deeply analyzed and processed in detail, so as to dig

out the intrinsic attributes and potential value of the

data. This study also delves into the key performance

indicators required to ensure that the stress effects of

the double flange turbine head cover and stay ring

connection bolts is accurate and credible, and

constructs a comprehensive network information

collection platform that plays a crucial role in

ensuring the accuracy of the research output.

However, it is worth noting that when applying the

image recognition algorithm, it is necessary to be

cautious in the selection of the stress influence

evaluation system of the bolt connecting the double

flange turbine head cover and the stay ring, so as to

effectively explore and use the advantages of the

image recognition algorithm and further improve the

accuracy and practical application value of the

research results.

ACKNOWLEDGEMENTS

The study is supported by: Research on Life

Prediction of Fixed Parts of Pump Turbine Based on

Dynamic-Static Interference, Fluid-Structure

Interaction and Fracture Mechanics - Research on

Pre-tightening Force Control and Failure Mechanism

of Bolted Connections of Pumped Storage Units

(204002019030304SY00004).

REFERENCES

Yang Yiming, Chen Jing, Sun Guohui, Shen Yang, Zhong

Xiaohong,&Fan Huiyu, etc (2022) A device and

method for improving the uniformity of abrasive flow

polishing in complex flow channels CN202011413194.

Wang Chunlian, Wang Yuejiang,&Roger (2023) The

development and application of coal quality detection

technology under the background of carbon neutrality

Chinese Science and Technology Journal Database

(Full Text Edition) Engineering Technology (011), 000

Chen Guisheng, Sun Min, He Ru, Shen Yinggang, Yang

Jie,&Yang Shun (2023) Research on the Effect of

Different Boosting Modes on the Performance of

Compression Ignition Aviation piston engines

Propulsion Technology (011), 044

Wang Minjuan, Huang Hao, Wang Bao, Han Bo, Yang

Pinghua,&Huang Xu (2023) Application and research

progress of continuous sic fiber reinforced titanium

matrix composites Journal of Aeronautical Materials,

43 (6), 1-19

Sang Shuxun, Hua Kaimin, Tu Kunkun, Guo Zhenkun, Wei

Fu,&Guo Yuliang (2023) The development direction

and research progress of the coupling technology

system (Beccs) for efficient utilization of biomass

energy and carbon dioxide capture, utilization, and

storage Journal of China University of Mining and

Technology (005), 052

Xiang Ze,&Zhu Zhiwen (2023) Multi objective

optimization of open rib steel uhpc composite bridge

deck based on nsg a - Ⅱ Progress in Building Steel

Structures (010), 025

Chen Zhong (2023) Comparative monitoring of continuous

emission monitoring system (CEMS) for incinerator

flue gas in sludge treatment plant Leather

Manufacturing and Environmental Technology (018),

004

Sha Qianqian, Zeng Wen, Liang Yijian,&Du Ailian (2023)

Clinical characteristics and 5-year follow-up study of

congenital myasthenia gravis syndrome caused by Colq

gene mutations (with a family report) Journal of

Clinical Neurology (005), 036

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