=Paper=
{{Paper
|id=Vol-3214/WS4Paper3
|storemode=property
|title=Manufacturing Data Security, Trustiness and Traceability
|pdfUrl=https://ceur-ws.org/Vol-3214/WS4Paper3.pdf
|volume=Vol-3214
|authors=Javier Pérez-Soler,Pau Garrigues,Imanol Fuidio,Stefan Wellsandt,Gennady Laventman,Mohammad Amin Khodamoradi,Santiago Gálvez-Settier,Juan-Carlos Pérez-Cortés
|dblpUrl=https://dblp.org/rec/conf/iesa/Perez-SolerGFWL22
}}
==Manufacturing Data Security, Trustiness and Traceability==
https://ceur-ws.org/Vol-3214/WS4Paper3.pdf
Manufacturing Data Security, Trustiness and Traceability
Javier Pérez-Soler1, Pau Garrigues1, Imanol Fuidio2, Stefan Wellsandt3, Gennady
Laventman4, Mohammad Amin Khodamoradi5, Santiago Gálvez-Settier1 and Juan-Carlos
Pérez-Cortés1
1
Instituto Tecnológico de Informática, Camino de Vera s/n, Valencia, 46022, Spain
2
Ikerlan Technology Research Center, Pº J.M. Arizmendiarrieta 2, Arrasate, 20500, Spain
3
Bremer Institut für Produktion und Logistik GmbH at the University of Bremen, Hochschulring 20, Bremen,
28359, Germany
4
IBM Israel-Science and Technology Ltd. 94, Derech Em-Hamoshavot, Petach Tikva, 49527, Israel
5
Instituto de Desenvolvimento de Novas Tecnologias, Campus da FCT NOVA, Caparica, 2829-516, Portugal
Abstract
European manufacturing companies collect a large amount of data during their
manufacturing processes thanks to the increasing use of sensors, actuators and instruments.
This amount of data is very valuable for improving manufacturing quality, with a view to the
Zero Defects objective. In order to take full advantage of the vast amount of accumulated
data, quality must be targeted, and its Security, Trustiness and Traceability must be
preserved. Data quality is influenced by several factors: including human errors,
communication problems or inaccuracies, so any system used must be sufficiently reliable.
This paper describes the technical approach to develop solutions to control manufacturing
Data Security, Trustiness and Traceability with a zero-defect approach and provided by the
i4Q project. The approach is exemplified with the industrial production line of injected
plastic spare parts for the automotive sector.
Keywords 1
Product quality, machine learning, artificial intelligence, zero-defect manufacturing, non-
destructive inspection, data security, trusted networks, data traceability, data repository
1. Introduction
The i4Q Project [1] presents a complete solution consisting of sustainable IoT-based Reliable
Industrial Data Services able to manage the huge amount of industrial data coming from cost-
effective, smart, and small size interconnected factory devices for supporting manufacturing online
monitoring and control [2].
This work describes the necessary strategies, methods, and key technologies to ensure data quality,
which is influenced by many factors, including human error, communication issues or inaccuracies.
Monitoring systems are expected to be reliable enough for decision-making, but sensors are
susceptible to provide unreliable information.
Trusted networks allow ensuring the reliability, integrity and privacy of the data exchanged. The
heterogeneity in technologies leads to a complex ecosystem of data models that is difficult to
contextualize, leading to misinterpretations and incomplete data analysis. Setting the guidelines for
Proceedings of the Workshop of I-ESA’22, March 23–24, 2022, Valencia, Spain
EMAIL: javierperez@iti.es (J. Pérez-Soler); pgarrigues@iti.es (P. Garrigues); ifuidio@ikerlan.es (I. Fuidio), wel@biba.uni-bremen.de (S.
Wellsandt), gennady@il.ibm.com (G. Laventman), a.khodamoradi@uninova.pt (M.A. Khodamoradi), sgalvez@iti.es (S. Gálvez-Settier),
jcperez@iti.es (J.C. Pérez-Cortés)
ORCID: 0000-0002-4195-9491 (J. Pérez-Soler); 0000-0003-3408-3249 (P. Garrigues); 0000-0003-4761-9532 (I. Fuidio), 0000-0002-0797-
0718 (S. Wellsandt), 0000-0002-2700-5384 (M.A. Khodamoradi), 0000-0003-3479-8367 (S. Gálvez-Settier), 0000-0001-6506-090X (J.C.
Pérez-Cortés)
© 2022 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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information models, needed metadata for traceability and interoperability is crucial for ensuring data
quality.
Finally, the increasing amount of collected data requires flexible data storage and data analysis
infrastructure able to process information without affecting data quality. Distributed systems can help
improve the performance of data storage and data processing, opening the door to the use of
Distributed Ledger Technologies.
The proposed approach is exemplified with the use case named “Automatic Advanced Inspection
of Automotive Plastic Parts”, and industrial production line of injected plastic spare parts for the
automotive sector.
2. Technical approach
The i4Q Project aims at providing methodologies, tools and infrastructure to ensure the necessary
data quality to enable operational intelligence and improve data analysis results effectiveness.
In order to achieve this goal, the work is divided into five work areas with different purposes:
• To provide a common information model that ensures the quality and traceability of collected
data, including metadata regarding accuracy and reliability of the data measured. This helps to
consolidate the information and implements best practices.
• To provide security tools and mechanisms, such as Public Key Infrastructure and project level
certificates, as well as best practices and recommendations, to ensure security and privacy on
every step of the data cycle.
• To provide a secure and safe oriented communication infrastructure that guarantees integrity,
reliability and privacy of manufacturing data gathered.
• To analyze and assess the suitability of Distributed Ledger Technologies for ensuring security,
trustiness and immutability of data in the i4Q framework.
• To provide the distributed storage architecture and infrastructure capable of handling an
exponential increase in data without affecting its quality.
2.1. Manufacturing data quality strategy
This area of work focuses on a strategy to manage data quality. Its first part focuses on a guideline
outlining key aspects of data and information quality management in manufacturing. It describes
production systems in a way that data and information activities become more visible. Besides, it
clarifies key stakeholders and how they can manage information quality issues, for instance, by
training and technical measures integrated in information systems. The second part of this work area
develops an Open-Source software tool called QualiExplore to raise awareness for information quality
in manufacturing.
QualiExplore grounds on a knowledge base containing information quality factors. These factors
describe phenomena that influence data characteristics and organizations should manage them to
minimize information quality problems. QualiExplore organizes data quality factors using a data life
cycle model and acknowledged quality characteristics, such as accuracy, precision, timeliness, and
completeness. An optional conversational user interface will allow users to interact with QualiExplore
intuitively via natural language. For instance, users in the exemplified use case will be able to
describe their work and interest in information quality in a plastic spare parts production line and
receive the matching factors they should manage. i4Q’s complementary quality management
guidelines will contain approaches and techniques the users could use to manage the identified
factors.
The manufacturing data quality strategy focuses on two main data quality standards for Industry
[4]: ISO 8000 [5] for data quality in the production line and automation systems and ISO/IEC 5259
[6] for data quality for analytics and machine learning (ML). i4Q uses these standards because they
are widely used in organizations and also because ISO is an acknowledged standardization body.
The ISO 8000 standard series contains several documents introducing vocabulary, concepts, and
processes concerning data quality. One important document outlines a related management
framework that introduces roles and responsibilities and elaborates some principles to exchange data
characteristics like defining specific syntax, encoding data semantically, and conformance to data
specification. Besides, the ISO 8000 series gives some guidelines to measure data completeness and
accuracy.
The ISO/IEC 5259 standard package focuses on providing qualitative data for analytics and ML.
This standard overviews data quality measures and provides terminology, examples and details for
frameworks to create data quality processes while providing management guidelines and
measurement indicators to monitor the state of such data quality processes.
2.2. Manufacturing data trustiness and traceability
This area of work provides easier, trustable and traceable access to data coming from many
different sources. For this, it is necessary to analyze and model incoming data, in order to standardize
the information models and metadata required.
The solution provides tools to ensure data trustiness and full traceability. The goal is to enhance
the level of trust by employing a blockchain based data service, to support data traceability. This
enhances trust and acceptability by providing security and trust in the data that flows directly to the
blockchain, serving as a single point of truth, preserving provenance and supporting non-repudiation.
Information stored on the blockchain cannot be changed or erased and is proved to be authentic.
Blockchain-based services ensure that the information is not to be tampered with. Moreover,
differential visibility scope of information shall be supported for cases in which a subset of the
participating entities needs to share some information. The blockchain capabilities shall be exposed to
other components and microservice applications via REST interfaces, with possibility to use several
programmable SDKs.
The underlying blockchain solution shall use Orion Blockchain DB [7], an open-source project
that provides a Key-Value (KV) datastore functionality with the addition of rich provenance and
blockchain capabilities. Orion provides a multiparty approval mechanism between required
participants using its multi-sign transactions and use its provenance and blockchain capabilities to
govern the actions that take place upon the arrival of new data. Using its Read-Write Key Level
Access Control mechanism, different visibility scope over existing data and its history is defined. The
REST interface [8] provides API similar to regular KV databases, thus providing an easy way to
invoke transactions to manipulate data in KV store, and each one of these manipulations
automatically stored to the blockchain ledger. Same REST interface provides support for various rich
queries to KV data, provenance data and blockchain ledger data, including rich range queries, history
queries and non-repudiation and data integrity proofs.
Orion uses x.509 certificate [9] based PKI mechanism for both user and server authentication.
This area of work enhances the level of trust in the exemplified use case by employing a
blockchain based data service, to support data traceability in the data that flows directly to the
blockchain, thus serving as a single point of truth, preserving provenance and supporting non-
repudiation.
2.3. Manufacturing data trusted communication and distribution
The trusted networks area of work implements reliable data collection, providing connectivity to
industrial data sources through Trusted Networks able to assess and ensure precision, accuracy, and
reliability. Technologies such as Time Sensitive Networks (TSN) for wired communications, and
wireless access networks (Industrial Wireless Sensor Network (IWSN), Low Power Wide Area
Networks (LPWAN), ad-hoc connections, etc.), are integrated or merged with other solutions such as
Software Defined Network (SDN), Network Function Virtualisation, or Network Slicing in order to
improve reliability of the communication infrastructure and therefore the integrity and reliability of
data collected.
This trusted network infrastructure, in which the TSN and the low power IWSN resources are
orchestrated with a centralized SDN controller, will be validated ensuring that the expected Quality of
Service (QoS) is achieved.
This area of work includes the development of software-defined wireless industrial interfaces for
data communication, paying special attention to requirements such as predictability and determinism,
high reliability and trustability, low consumption and efficient and fast network deployment [10],
while reducing the installation cost of new-wired infrastructure. Time-sensitive transmission of data
over deterministic Ethernet networks is also required for applications that require very low
transmission latency and high availability and can use the floor plant wired network infrastructure.
The wireless solutions based on WSN shall be used in the exemplified use case, and LPWAN for
long range requirements, are envisaged to merge and implement state of the art techniques to enhance
the QoS and robustness in these types of networks, with the introduction of SDN and adaptative
mechanisms.
2.4. Manufacturing data security
Collected data can be delivered by diverse types of devices and be transmitted through and
processed by a significant number of layers and technologies. The type of devices in an Industrial
Automation and Control System (IACS) are mainly DCS (Distributed Control Systems), RTU
(Remote Terminal Units), PLC (Programable Logic Controler) and SCADA (Supervisory Control and
Data Adquisition). The operation of these devices translates into the necessity of recommendations
and guidelines to enable multilayer cyber security features in Industrial Internet of Things, as well as
tools to implement these recommendations, enabling Industrial Internet of Things devices to interact
with the whole system securely in all stages of a manufacturing scenario.
This area of work comprises the analysis of the architecture and methodology to provision signed
certificates with Hardware Security Module and trusted material to devices with a Trusted Platform
Module or Secure Element using asymmetric encryption architecture. Furthermore, the solution
targets OPC-UA certificate management mechanism [11] in order to provide a real-world example
where is envisioned the union between the state-of-the-art security technology and a protocol
specification.
The security mechanisms may differ between communication solutions for Distributed Ledger
Technology tools proposed, which means applying security by design during development, adjusting
security, and safety policies at different levels to ensure the trustability and privacy of data.
In the exemplified use case, this is a piece of software that distributes trust using x.509
certificates[9] and asymmetric cryptography. Once the trust is distributed, every module has a digital
identity that uses to provide security between different endpoints using asymmetric cryptography
considering different trust policies, adjusting security and safety policies at different levels. In the
case of OPC-UA, the Global Discovery Server (GDS) provides role management policies which can
be integrated with existing user and role management systems. Roles have access permission for
nodes within the OPC UA Information Model.
The solution enriches the current state of the art with tools to provide seamless management of
security by design components in an IACS environment with Hardware Security Modules (HSM).
2.5. Manufacturing data storage and use
This data repository solution is devoted to design and implement a distributed storage system
considering those aspects unique to the Industry 4.0 paradigm [12]. One of the main aspects to
consider is the high degree of digitization expected in companies, resulting in most manufacturing
devices acting as sensors or actuators and generating vast amounts of data.
The infrastructure is, then, able to absorb large volumes of data coming into the system at high
speeds. Similarly, it is as elastic as possible to adapt the computing resources, it requires to the
existing demand and be ready to use additional resources, either local to the factory or from remote
systems like public or private clouds if needed, although bearing in mind possible data privacy
restrictions. In addition to the storage capabilities, the system must provide easy ways to access this
data so other components and microservice applications can easily consume and use it to improve the
efficiency of the system.
The data repository area of work includes a number of off-the-shelf tools. Currently all of them are
open-source. Some of them are database managers (like MongoDB, MySQL, etc.). Others offer other
types of storage (such as MinIO, which offers file and blob storage). The database managers cover a
variety of structures and formats, including standard relational SQL-based data, JSON documents and
other structured formats (like key-value pairs, graphs, etc.). Each tool offers its own indexing
mechanisms.
In the scope of the exemplified use case, the objective of this area of work is to create a suitable
storage system for the collected data of plastic spare parts for the automotive sector. This system or
repository oversees receiving, storing, and serving the data in a proper way to the other components in
the architecture. It provides the proper tools for administrators to consult and transform the
information contained inside it, as well as the ways for data scientist to use this data in their
experimentation. These tools are provided through a suitable user interface. The solution also
oversees data protection, serving as a secure system for the information by means of encryption, both
in flight and at rest.
The result is an efficient repository, ready to provide its service to the rest of the components
present in the system.
3. Conclusions
This article has introduced the i4Q project’s areas of work for data security, trustiness and
traceability for an industrial inspection approach as conceived in the exemplified use case “Automatic
Advanced Inspection of Automotive Plastic Parts”.
Inspecting and predicting techniques for control of product quality are based on ML models
analyzing secured, trustable and traceable data collected from production in-field sensors and other
production related information.
These outcomes are available in the scope of i4Q to allow the design and construction of zero-
defect production lines to maximize product quality.
The other i4Q use cases cover five more industrial scenarios, namely white goods, wood
equipment, metal machining, ceramics pressing, and metal equipment. Each case makes use of the
here described functionalities for Data Security, Trustiness and Traceability, in order to ensure
prediction, inspection, and supervision of a given production line.
4. Acknowledgements
The research leading to these results received funding from the European Union H2020 Program
under grant agreement No. 958205 “Industrial Data Services for Quality Control in Smart
Manufacturing (i4Q)”
5. References
[1] i4Q Project Website, 2021. URL: https://www.i4q-project.eu/
[2] R. Poler, A. Karakostas, S. Vrochidis, A. Marguglio, S. Galvez-Settier, P. Figueiras, A. Gomez-
Gonzalez, B. Mandler, S. Wellsandt, R. Trevino, S. Bassoumi, C. Agostinho, An IoT-
based Reliable Industrial Data Services for Manufacturing Quality Control, in: 2021 IEEE
International Conference on Engineering, Technology and Innovation (ICE/ITMC), IEEE,
Cardiff, 2021, pp. 1–8. doi: 10.1109/ICE/ITMC52061.2021.9570203.
[3] A. Karakostas, R. Poler, F. Fraile, S. Vrochidis, Industrial data services for quality control in
smart manufacturing–the i4q framework, in: 2021 IEEE International Workshop on Metrology
for Industry 4.0 & IoT (MetroInd4. 0&IoT), IEEE, Rome, 2021, pp. 454-457. doi:
10.1109/MetroInd4.0IoT51437.2021.9488490.
[4] R. Perez-Castillo, A. G. Carretero, M. Rodriguez, I. Caballero, M. Piattini, A. Mate, S. Kim, D.
Lee, Data Quality Best Practices in IoT Environments, in: 11th International Conference on the
Quality of Information and Communications Technology (QUATIC), IEEE, Coimbra, 2018, pp.
272-275. doi: 10.1109/QUATIC.2018.00048.
[5] ISO 8000. URL: https://www.iso.org/standard/50798.html
[6] ISO/IEC 5259. URL: https://www.iso.org/standard/81093.html
[7] Orion. URL: https://github.com/hyperledger-labs/orion-server
[8] Orion REST example. URL: http://labs.hyperledger.org/orion-server/docs/getting-
started/transactions/curl/datatx
[9] x.509 certificate. URL: https://en.wikipedia.org/wiki/X.509
[10] J. Vera-Pérez, J. Silvestre-Blanes, V. Sempere-Payá, D. Cuesta-Frau, Multihop Latency Model
for Industrial Wireless Sensor Networks Based on Interfering Nodes, Applied Sciences 11 (2021)
8790. doi: 10.3390/app11198790
[11] OPC Foundation, OPC 10000-2 - Part 2: Security Model. URL:
https://reference.opcfoundation.org/v104/Core/docs/Part2/.
[12] Industries 4.0 paradigm. URL: https://en.wikipedia.org/wiki/Fourth_Industrial_Revolution