=Paper=
{{Paper
|id=Vol-3293/paper47
|storemode=property
|title=Conceptual Framework of Predictive Maintenance in a Canning Industry
|pdfUrl=https://ceur-ws.org/Vol-3293/paper47.pdf
|volume=Vol-3293
|authors=Panagiotis Mallioris,Georgios Kokkas,Alexandros Styliadis-Heinz,Ioannis Margaritis,Fotios Stergiopoulos,Dimitrios Bechtsis
|dblpUrl=https://dblp.org/rec/conf/haicta/MalliorisKSMSB22
}}
==Conceptual Framework of Predictive Maintenance in a Canning Industry==
https://ceur-ws.org/Vol-3293/paper47.pdf
Conceptual Framework of Predictive Maintenance in a Canning
Industry
Panagiotis Mallioris 1, Georgios Kokkas 1, Alexandros Styliadis-Heinz 1, Ioannis Margaritis 2,
Fotios Stergiopoulos 1 and Dimitrios Bechtsis 1
1
International Hellenic University, Department of Industrial Engineering and Management, P.O. Box 141,
Sindos, 57400, Greece
2
SAVVYCAN S.A., Karavaggeli 6, Kalohori, 57009, Greece
Abstract
In the context of Industry 4.0, Industrial Internet of Things devices, Cyber Physical systems
and Big Data solutions constitute the main core of state-of-the-art industrial production. The
majority of research in this area focuses on smart manufacturing in order to improve the
production process, increase the reliability and safety of industrial machinery, reduce
downtimes and optimize the maintenance schedule. This paper proposes a smart solution for
production monitoring incorporating a decision support system for failure diagnosis in a tin
can manufacturing process. This study focuses on a predictive maintenance strategy framework
that will prevent machine malfunction, bottlenecks in the production process and long
downtimes. In this paradigm, optical sensors will be embedded alongside vibration and
environmental sensors (i.e., humidity, temperature) for the data acquisition of important Key
Performance Indicators (KPI). Programmable Logical Controllers and the KEP Open Platform
Communications (OPC) Server will be utilized in order for the acquired data to be dynamically
transferred and stored in a MySQL relational database. Afterwards, the database will be
integrated with a Big Data analytics platform for the process of data mining and decision
making. As a final step, a dashboard with real time descriptive statistics and an alarm-based
system will inform the maintenance personnel for upcoming potential failures. This approach
will connect malfunctions with features/sensors and improve the existing production process
which will eventually lead on minimizing the production costs and increasing machine
reliability.
Keywords 1
Canning Industry, Big Data, Decision Support System, Predictive maintenance
1. Introduction
Big data solutions in manufacturing process have been widely researched in recent years. The advent
of information technologies combined with big data approaches, enable a flexible, responsive and
decentralized process which leads to smart manufacturing [1]. In order to be competitive, modern
enterprises will be prompted to modify their production processes and manipulate efficiently data of
high velocity, variability, veracity, volume and value [2]. In this context, industrial machinery should
be capable of analysing real-time data and prognosing upcoming potential malfunctions, preventing
production disruptions (Gokalp et al., 2017).
This paper focuses on developing a smart manufacturing solution by integrating Industry 4.0
technologies namely Industrial Internet of Things (IIoT), Cyber-Physical Systems (CPS), Big data in a
Can manufacturing process. Our research will significantly impact the competitiveness and productivity
Proceedings of HAICTA 2022, September 22–25, 2022, Athens, Greece
EMAIL: panmalliw@gmail.com (A. 1); geokokkas87@gmail.com (A. 2); alexstyliad@gmail.com (A. 3); i.margaritis@savvycan.gr (A. 4)
fstergio@ihu.gr (A. 5) dimbec@ihu.gr (A. 6)
ORCID: 0000-0001-5781-8362 (A. 1); 0000-0002-9351-5542 (A. 5); 0000-0003-3110-7292 (A. 6)
©️ 2022 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
253
of the organisation by constructing an autonomous real-time data processing approach. In addition, a
predictive maintenance solution with data-driven methodologies will be examined in order to increase
the reliability and safety of production machinery. Several examples exist in literature of predictive
maintenance applications and bottleneck reduction solutions. A machine learning solution for failure
classification and identification on motor shafts was presented [4]. The MatrikonOPC simulation server
was used for the interface between data sources, while the National Instruments (NI) LabVIEW
software has been used for data acquisition and for developing the solution systems. Accelerometers
were installed on the motor shaft to measure the vibration on different axes to be used as an input on
the classification algorithm. The proposed implementation successfully detected the vibration failure of
the motor shaft. Additionally, [5] presented a machine learning approach for anomaly detection and
production bottleneck prediction in cold forming manufacturing line. Acoustic emissions, maintenance
logs and statistical measures, such as mean and standard deviation, were implemented as the input
features of the classification algorithm. The results indicated a classification of the healthy state of cold
forming manufacturing line using acoustic emissions with an F1 score of 0,632.
In more depth, in our research optical sensors will be implemented in each production stage,
vibration sensors in the frequently malfunctioning beader machine and temperature and humidity
sensors in the oven respectively. Moreover, the sensors will be integrated in an OPC Server through
Programmable Logic Controllers (PLCs). As a next step, the OPC Server will establish a real-time
connection with a relational MySQL database for data storage and further analysis. Furthermore, the
Pandas big data analysis library and python programming language will be implemented for the process
of data mining and decision making on critical production processes, i.e. predicting the failure of
machinery and preventing production bottlenecks. Finally, real-time statistical measurements of KPI
parameters, prediction outcomes and an alert-based system will inform the production engineers. Figure
1 shows the proposed methodology as a stepwise process.
This paper is organized as follows. Section 2 describes the framework of OPC server and the
connections between PLC and database. Section 3 presents the Big data platform and the conceptual
methodology of data mining. Section 4 concludes with some remarks and propositions for future
research.
2. PLC- OPC Server – Database
The main scope of the proposed Decision Support System (DSS) is improving the productivity
through a dynamic process of data acquisition and analysis. The monitoring framework will ensure the
reliability and safety of machinery, determine production losses at various stages and collect
quantitative and qualitative characteristics related to the use of raw materials (namely, tinplate, lacquer,
powder). In combination with photo electric sensors used for controlling the operation of the production
line in order to avoid bottlenecks, a variety of sensors will be integrated at different production stages
collecting important measurements of vibrations temperatures, humidity. The acquisition of such
important indicators will enable the researchers to output descriptive statistical information regarding
the production process and additionally implement a set of data-driven algorithms for predictive
maintenance. A set of Siemens PLC will be used in order for the acquired data to be transferred in real
time with a sampling period of 10sec to the KEP OPC Server. As a proof-of-concept, basic hardware
connection between a PLC and optical sensors is presented in Figure 2.
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Figure 1: Proposed methodology as a stepwise process
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Figure 2: Connection of a PLC to optical sensors (proof-of-concept)
As a next step, the OPC Server inherently stores production data in a relational database and provides
three data sources to the rest of application. More specifically, the data sources consist of a Data Logger,
a HTTP Server and a HTTP Client.
• A data Logger is used to store data directly into a MySQL database at a steady rate of 10
seconds. The database, being static, also serves as a long-term repository for storing historic data of
the production line.
• An HTTP Server is an endpoint that enables bilateral communication between the webserver
and the OPC Server permitting remote Read/Write functionality for the rest of the application.
• An HTTP Client broadcasts sensor generated data to a designated web endpoint in real-time.
Our proposed framework is quite innovative, reducing the otherwise complicated process of
aggregating heterogenous data sources of production process and facilitates their presentation to all the
stakeholders using state-of-the art software tools. In the following section the process of data mining
and decision making will be described thoroughly.
3. Decision Support Systems (DSS)
In a decision support system where a huge amount of heterogenous is processed, data mining is
essential. Data mining is the process of manipulating raw data through cleaning techniques, finding
patterns, creating models, and evaluating the outputs. It includes descriptive statistics and data-driven
methodologies for decision making. Furthermore, data mining can be divided in three different
subsections namely, data cleaning or cleansing, data pre-processing and decision making. [6], [7]
presented an extensive research of data manipulation and data analysis. In the following sections the
procedure of data mining is described.
3.1. Data Cleaning
It is essential for a decision support system that the processed data to be complete and in the proper
format. In a production process, where real-time data is collected in large quantities from heterogeneous
sources, data cleaning techniques avoid processing them in the wrong or incomplete form. The data
cleansing process involves identifying incorrect, incomplete or inaccurate data and replacing or deleting
them. In our use case we implement NumPy and Pandas libraries of the python programming language
for data cleaning. Additionally, commonly used software is Apache Spark and pyspark libraries.
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3.2. Data Pre-processing
Data pre-processing in a data-flow application further manipulates important variables prior to their
implementation in the decision algorithms. Data-pre-processing can be divided as follows:
• Conversion of the data type into an appropriate format, thus avoiding inequalities between the
variable types of each column.
• Data normalization which is important when variables from heterogenous sources show high
redundancy and thus improving data integrity.
• Homogenization of the data in order to avoid biased results and ensure data consistency for the
prediction algorithm.
• Fabrication of additional features which can be produced by dividing the information of a
crucial parameter (namely datetime).
• Deviating the structure of the columns in order to provide the prediction algorithm the
possibility to find additional correlations.
• Further analysis with descriptive statistics, i.e. mean, median, mode, standard deviation,
variability.
• Additional correlation examination using methods such as Pearson, Kendall, Spearman, Point-
Biserial.
Figure 3 shows a heatmap of important manufacturing variables using the Spearman correlation
method, python programming language and the seaborn library.
Figure 2: Heatmap of important manufacturing variables
3.3. Decision-making
Prior to data visualization, the final step in the decision support process is decision making.
Depending on the predicted value the researcher is opted to select and examine a variety of predictive
algorithms in order to output an accurate solution. Relatively, the confronted issue can be defined as
classification, regression, remaining useful life (life expectancy) or a clustering problem. It is essential
for the decision-making process, the input features and the predictive value through the data mining
procedures to be thoroughly analyzed and categorized [8]. The limitations of a decision-making
algorithm is that it requires a large dataset containing non-failure and failure measurements and it can
be computationally expensive. In this research, where we have to examine heterogenous data, we will
select the optimal predictive solution relatively to the emerging issue and the production needs. Python
programming language and the scikit-learn, TensorFlow and keras libraries will be used for the
algorithm implementation.
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3.4. Data Visualization
A dashboard containing real-time crucial parameters and predictive outcomes of the production
process in combination with an alarm-based system are designed for the proposed use case. As part of
this process, a service that filters the incoming data is set up before it is stored in the database. If the
algorithm detects an anomaly, a warning message is triggered and forwarded to the front-end
application to prompt the user for corrective action. The visual data presentation (Figure 4) is
implemented using the React framework in combination with a charting engine of the echarts library
developed by the Apache Foundation. More specifically, the frontend web application queries an
Express server upon user request and, upon receiving a response, forwards the requested data to the
echarts React component library in order to be visualized for the user.
Figure 3: Example of visual data presentation
4. Conclusion
In this article a conceptual framework of a predictive maintenance process for a canning industry
has been proposed. The added value of the research is the proposition of a smart manufacturing
framework which enables a real-time condition monitoring significantly improving the manufacturing
process. The complete data-flow process from heterogenous sources up until the data visualization was
extensively presented and analysed proposing the implemented software and algorithms for each
process respectively. As future research, a more extensive analysis on the data-driven algorithms for
the improvement of the decision-making process and the reliability of industrial machinery is proposed.
5. Acknowledgements
This research has been co‐financed by the European Regional Development Fund of the European
Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship
and Innovation, under the call RESEARCH–CREATE–INNOVATE (project code: T2EDK-01806)
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