Abstract of Power Plant Equipment Defect Trend Analysis Method

Based on Apriori Algorithm

Fanqi Huang, LingLing Ming, Qiang Liu, Chuanhe Sun and Lixue Liang

China Southern Power Grid Energy Storage Co Ltd, China

Keywords: Apriori Algorithm, Power Plant Equipment Defects, Trend Analysis.

Abstract: As an important supporting industry in the national economy, the stability of equipment operation in the power

industry is extremely important. However, defects in power plant equipment will always constrain power

productivity. In this paper, this paper proposes a method for analyzing the defect trend of power plant

equipment based on Apriori algorithm. Based on the collection and preprocessing of important data such as

maintenance records and fault reports of power plant equipment, the Apriori algorithm is used to complete

the mining of the correlation rules between equipment defects, and carry out in-depth analysis of these rules,

so as to find the common factors that cause equipment defects and predict future trends. Finally, certain

countermeasures should be put forward to facilitate the scientific improvement of power plant equipment

management, improve the reliability of its equipment operation, and ensure the safety and stability of power

production.

1 INTRODUCTION

The operation of equipment in the power industry

needs to maintain a certain degree of reliability,

stability and safety. However, judging from the

current situation, due to the aging of equipment, the

influence of environmental factors, improper

operation, etc. (Atilgan, 1990), the problem of power

equipment defects always exists and cannot be

solved, which will have a direct impact on the

reliability and safety of power production (Crouch,

1993). Therefore, how to effectively prevent and

control equipment defects has become a key problem

that power companies need to solve as soon as

possible. In this regard, this paper will propose a

method to better promote the further optimization of

power plant equipment management (Esterby, 1993).

2 RESEARCH METHODS

First, Theoretical Analysis Method. In the initial part

of this paper, the researcher uses the theoretical

analysis method to analyze the solution strategy of

power plant defects, and based on this, the Apriori

algorithm is introduced (Hess, A and Iyer, H, et al.

2001), and then the algorithm is applied to the

analysis of power plant equipment defect trends.

Based on the elaboration of this theory, this paper can

better analyze the defect data in power plant

equipment, find the correlation between equipment

faults, and provide a basis for subsequent prediction.

In the process of research, this paper combines an

example of power plant equipment defects to carry

out a detailed research process, so as to better get

closer to the research topic (Kisi and Santos, et al.

2001). This paper mainly analyzes the data collection

and preprocessing, the mining of association rules

and trend prediction in this case, which can be

conducive to the effective combination of theory and

practice, and shows people vivid research results

(Okabe, 1982). When analyzing the association rules

and predicting the trend, this paper not only finds

some patterns from the data, but also puts forward

targeted and reliable countermeasures and

improvement suggestions based on actual business

scenarios (Porter and Rao et al. 2022). The

application of this method can ensure that the

research results are further close to the actual needs,

and its operability is also relatively strong. Based on

these research methods, this paper will dig deep into

the various valuable information contained in the

defect data of power plant equipment, and provide

reliable data and theoretical basis for power plant

116

Huang, F., Ming, L., Liu, Q., Sun, C. and Liang, L.

Abstract of Power Plant Equipment Defect Trend Analysis Method Based on Apriori Algorithm.

DOI: 10.5220/0013536500004664

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 116-121

ISBN: 978-989-758-763-4

managers, so as to help people solve related problems

(Rokaya and Al Azwari, 2022).

3 RESEARCH PROCESS A. DATA

COLLECTION

3.1 Data Sources and Collection

Methods

First, equipment maintenance records. First of all,

from the power plant maintenance management

system, obtain the maintenance records of the

equipment, which mainly include the name of the

equipment and the maintenance time, maintenance

personnel, maintenance type and other information.

The result is shown in formula in Equation 1.

22 2

d ln( )

()

kn k



−



)

Secondly, the use of automation means, such as

writing scripts, the use of data capture tools, etc., to

regularly extract some of the latest maintenance

record data from the maintenance management

system; First of all, collect the fault reports submitted

by the employees and operators of the power plant,

which need to contain the specific description of the

equipment failure, the time of occurrence, and the

scope of impact (Sloane, 1982). The result is shown

in formula in Equation 2.

000

() [()] exp j ()d

Tz E nz k nz z

−



= 



(2)

it is necessary to set up an online fault reporting

system to facilitate employees, so that they can report

equipment defects at any time, and ensure the

timeliness and integrity of their data; First of all, the

equipment should be inspected regularly, and the

inspection results should be recorded, such as the

operation status and abnormal conditions of the

equipment, potential problems, etc (Thevi. and

Schanzer, 2010). Secondly, the electronic inspection

system can be used to collect inspection records, or

the paper form can be used to record them, and then

they can be digitally processed. The result is shown

in formula in Equation 3.

()

// // 0

RE=+

(3)

First, the specific operation of the power plant

equipment is recorded, such as the daily equipment

operation time and various parameters such as

temperature. Secondly, based on the equipment

control system and data acquisition equipment, the

operation data of the equipment is obtained in real

time and stored as log files (or database records). The

result is shown in formula in Equation 4.

// 0y

⊥

)

3.2 Data Field

In the process of data collection, people should design

some appropriate numerical fields, which are

conducive to subsequent data analysis and modeling.

The result is shown in formula in Equation 5.

// 0

HRE

⊥

)

The data fields need to include the device

name/number, the type of fault (mechanical fault,

electrical fault, etc.), and the time when the fault

occurred (year, month, day, etc.). hours, minutes,

seconds), fault description, maintenance measures,

maintenance personnel, equipment parameters (such

as temperature or pressure, etc.), Equipment running

time, inspection time and inspection results, etc. The

result is shown in formula in Equation 6.

()

// // 0

HRE

=−

)

3.3 Data Quality Control

In order to ensure the quality and reliability of the

data, these measures need to be taken: first, regularly

check and verify the data, find and correct the data

with errors and missing problems, second, carry out

necessary cleaning and preprocessing for the data,

such as removing duplicate data or dealing with

outliers, etc., and third, establish a reasonable data

review and review mechanism to ensure a high degree

of consistency and accuracy of the data. The result is

shown in formula in Equation 7.

Abstract of Power Plant Equipment Defect Trend Analysis Method Based on Apriori Algorithm

117

()

// // 0

HRE

=−

)

3.4 Data Storage and Management

In order to facilitate subsequent data analysis and

mining, the collected data should be reasonably

stored and managed to improve the effectiveness of

storage and management. First, database storage. All

data is stored in a database for easy access and

management. Second, file backup. Regularly and

effectively back up data to prevent data loss and

damage. Third, access control. Carry out the

necessary permission control on the data to ensure

that only authorized personnel can access and modify

the data. Therefore, data collection is a crucial step in

the process of trending power plant equipment

defects. Only when the collected data is sufficient and

accurate can it provide a solid foundation for

subsequent data analysis and model building. The

result is shown in formula in Equation 8.

// 0y

⊥

(8)

4 DATA PREPROCESSING

4.1 Data Cleaning

First, remove duplicate data. Check whether there are

duplicate records in the dataset, such as device name,

occurrence time, fault type, and other consistent data,

and delete them; second, deal with missing values.

For records with missing values in certain fields,

more measures need to be taken, such as removing

records with real value or replacing missing values

with averages and medians, replacing missing values

with mode, using machine learning models to predict

missing values, and third, dealing with outliers.

Outliers in the dataset can be identified and

eliminated, such as data records where the failure

occurred beyond a reasonable range. Outliers can be

detected using statistical analysis or machine learning

methods, for example.

Table 1: Overall defect analysis of the equipment

serial

numb

The

name

of the

devic

Che

the

date

The

type

defect

Defect

level

Heati

syste

2023

-05-

Dense

pores

Moder

ate

Flush

the

water

syste

2023

-05-

Not

melte

Mild

Unit

2023

-05-

Not

solder

Severe

Unit

2023

-05-

Hole

decay

Moder

ate

4.2 Data Conversion:

Convert the data into the format required for the

Apriori algorithm. First, encode various discrete data

such as equipment name or fault type into numbers

and symbols; second, convert time data into discrete

time periods, such as monthly and quarterly statistics;

third, for continuous data, we should carry out

discretization processing, and divide the values into

different intervals and different levels.

Table 2: Repair results

Fix the

situation

Reason for the

extension

remark

Fixed

The construction

eriod is insufficient

not

To be fixed

The quality of

maintenance is not

strict

not

Fixe

not not

4.3 Feature Engineering

First, based on business requirements, some new

characteristic variables can be derived, such as

equipment failure frequency and mean time between

failures, etc., and second, the original features are

INCOFT 2025 - International Conference on Futuristic Technology

118

combined and transformed to obtain representative

features with the best prediction ability.

4.4 Data Set Segmentation

Directly divides the dataset into training sets and test

sets, so as to facilitate subsequent model training and

evaluation.

Based on the above data preprocessing, the data

quality and practicability can be guaranteed, so as to

lay a solid foundation for the subsequent analysis of

the Apriori algorithm. In addition, based on feature

engineering, the valuable information contained in

the data can be further mined and the accuracy and

reliability of the analysis process can be improved.

Figure 1: Shows the overall change ratio

The mining of association rules.is based on the

Apriori algorithm, which can carry out association

rule mining on the preprocessed data. As a classic,

frequent itemset mining algorithm, the Apriori

algorithm can discover the collection of items that

frequently appear in the dataset and generate

association rules. First, frequent itemset mining. The

Apriori algorithm can be used to mine frequent

itemsets on the preprocessed data. The algorithm is

based on an iterative approach and discovers frequent

itemsets, i.e., set items that often occur together. To

do this, people need to set a minimum support

threshold to filter out the set of frequent items to

ensure that people can find reliable association rules

and improve their representativeness, and second,

generate association rules. Correlation rules should be

generated based on the set of frequently mined items

and using metrics such as confidence level.

Confidence is also a measure of the credibility of a

rule, which indicates the probability that a conclusion

condition will occur at the same time if a precondition

occurs. In addition, based on the setting of the

minimum confidence threshold, you can also filter

out rules with strong correlation. These rules allow

people to better recognize and understand the

potential relationships between different attributes in

the data, and thirdly, rule evaluation and screening.

Evaluate and filter the generated association rules.

Not only do you have to have confidence, but you also

have other metrics, such as lift. Then, the rules are

sorted and filtered to find the high-quality rules that

have greater significance and value for business

decisions. Firstly, the excavated association rules are

visually displayed, so as to intuitively present the

relationship between different attributes and their

influence. Secondly, the business decision-makers

and domain experts are explained accordingly, and

analyzed and interpreted in combination with the

actual business scenarios.

4.5 Analysis of Association Rule

Interpretation and Classification.

First, conduct a careful analysis of the excavated

correlation rules and understand the meaning behind

each rule and its business implications. Secondly,

according to the characteristics of the rules, they are

classified and sorted, for example, according to the

type of equipment, the type of failure, the degree of

impact and other dimensions, and second, the analysis

of relevance. First, based on the rules after

classification, we can find the correlation between

different devices and devices. For example, some

fault types tend to occur on some devices at the same

time, or some devices often cause other device

failures. Secondly, some potential reasons behind

these associations are analyzed, such as whether there

is a device-equipment dependency or process flow,

common components, etc., and thirdly, the mining of

common factors. Conduct a deeper analysis of the

rules to uncover common factors that may contribute

to the occurrence of equipment defects. These factors

may include environmental conditions and improper

operation, aging equipment, etc. In addition, based on

the comparison and analysis of different association

rules, it is necessary to find out the specific

performance and influence degree of these common

factors in different equipment failures; First, the

historical data is combined with the mined correlation

rules to predict the trend of equipment defects that

may occur in the future. Secondly, the changes in the

time dimension in the rules are analyzed to find out

whether there is a significant increase or decrease

trend in the types and frequency of certain faults. The

results of the analysis are fed back directly to the

management of the power plant, so as to provide a

certain basis for the formulation of more targeted

maintenance plans and preventive measures. At the

same time, the analysis results are continuously

tracked, and the maintenance strategy can be adjusted

Abstract of Power Plant Equipment Defect Trend Analysis Method Based on Apriori Algorithm

119

at any time to ensure that it can respond to the defect

trend of the equipment. Based on the in-depth

analysis of association rules, it can help people to

fully understand the internal relationship between

power plant equipment defects, find out the common

influencing factors of problems, and predict the future

or trend, so as to provide sufficient scientific

decision-making support for power plants.

Figure 2: Accurate rate of change

5 ACTUAL CASE ANALYSIS

5.1 Background:

The boiler equipment in a thermal power plant has

serious defects, which affects the safe and stable

operation of the unit. Based on this research, this

paper uses the Apriori algorithm to mine various

important correlation rules, and now further analyzes

these rules to predict the future trend of contingent

equipment defects, and proposes certain

countermeasures: first, trend prediction. Based on the

in-depth analysis of the excavated correlation rules, it

can be seen that, firstly, rule 1 - "Boiler pipe corrosion

fault -> boiler water level abnormality". The support

and confidence of this rule have been increasing year

by year in the last 2 years. Combined with historical

data, it is predicted that abnormal boiler water level

failures caused by pipe corrosion problems will be

further exacerbated in the next 1 year. Secondly, Rule

2 – Boiler Flue Blockage -> Abnormal increase in

boiler temperature. The support and confidence of

this rule have remained relatively high in the last three

quarters, and it shows obvious seasonal fluctuations.

That is, in the autumn and winter of each year, it is

noticeably more prominent. Therefore, it can be

predicted that in the next 1 year, the temperature

anomaly caused by the blockage of boiler pipes will

become more serious in the autumn and winter; Based

on the trend prediction conclusions, this paper

proposes these countermeasures: a. Increase the

regular inspection and maintenance of boiler pipes,

find and repair corrosion problems in time, and avoid

water level abnormalities caused by pipeline failures.

b. Before the autumn and winter of each year,

organize personnel to comprehensively clean the

boiler flue to ensure the smooth flow of the flue, so as

to avoid failure caused by excessive temperature. c.

Establish a comprehensive equipment status

monitoring and early warning mechanism, and grasp

the operation of the equipment in real time, and find

some signs in time. d. Improve the emergency plan,

formulate targeted and reliable emergency response

measures, and improve the efficiency and success rate

of fault handling. e. Strengthen the training of

enterprise personnel equipment maintenance and

operation skills, and improve the professional level

and sense of responsibility of personnel.

6 CONCLUSIONS

OF this paper show that the power plant equipment

defect trend analysis method based on Apriori

algorithm has the following advantages:First, the

correlation between defects and defects of excavated

equipment. Based on the Apriori algorithm to analyze

the maintenance and failure data of power plant

equipment, people can find that there are some

potential correlations between equipment failures,

such as some types of faults tend to occur at the same

time, or some equipment failures will lead to various

failures of other equipment. This can help people

better recognize and understand the internal

mechanism of defects in power plant equipment, and

secondly, identify the common factors that contribute

to defect problems. After analyzing the association

rules, the system can further uncover the common

factors that contribute to the problem of equipment

defects, such as aging or improper operation of the

equipment, environmental conditions, etc. This can

provide a certain basis for the formulation of

subsequent preventive measures, and thirdly, predict

the future defect trend. The system can combine

historical data and the associated rules mined to

predict the future or occurrence of equipment defect

trends, such as certain fault types and fault frequency,

whether they show an increasing or decreasing trend.

This helps people prepare for power plants in

advance, and fourth, improves the effectiveness and

relevance of equipment management. Based on the

previous summary, power plants can develop more

targeted and reliable equipment maintenance plans

and contingency plans, such as increasing inspections

of equipment that are prone to failure or enhancing

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the prevention of certain types of failures during

specific seasons. This can greatly improve the

effectiveness and relevance of device management.

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