UDAVA: an unsupervised learning pipeline for sensor data validation in manufacturing

Manufacturing has enabled the mechanized mass production of the same (or similar) products by replacing craftsmen with assembly lines of machines. The quality of each product in an assembly line greatly hinges on continual observation and error compensation during machining using sensors that measure quantities such as position and torque of a cutting tool and vibrations due to possible imperfections in the cutting tool and raw material. Patterns observed in sensor data from a (near-)optimal production cycle should ideally recur in subsequent production cycles with minimal deviation. Manually labeling and comparing such patterns is an insurmountable task due to the massive amount of streaming data that can be generated from a production process. We present UDAVA, an unsupervised machine learning pipeline that automatically discovers process behavior patterns in sensor data for a reference production cycle. UDAVA performs clustering of reduced dimensionality summary statistics of raw sensor data to enable high-speed clustering of dense time-series data. It deploys the model as a service to verify batch data from subsequent production cycles to detect recurring behavior patterns and quantify deviation from the reference behavior. We have evaluated UDAVA from an AI Engineering perspective using two industrial case studies.publishedVersio

Cybersecurity Awareness and Capacities of SMEs

Small and Medium Enterprises (SMEs) are increasingly exposed to cyber risks. Some of the main reasons include budget constraints, the employees’ lack of cybersecurity awareness, cross-sectoral cyber risks, lack of security practices at organizational level, and so on. To equip SMEs with appropriate tools and guidelines that help mitigate their exposure to cyber risk, we must better understand the SMEs’ context and their needs. Thus, the contribution of this paper is a survey based on responses collected from 141 SMEs based in the UK, where the objective is to obtain information to better understand their level of cybersecurity awareness and practices they apply to protect against cyber risks. Our results indicate that although SMEs do apply some basic cybersecurity measures to mitigate cyber risks, there is a general lack of cybersecurity awareness and lack of processes and tools to improve cybersecurity practices. Our findings provide to the cybersecurity community a better understanding of the SME context in terms of cybersecurity awareness and cybersecurity practices, and may be used as a foundation to further develop appropriate tools and processes to strengthen the cybersecurity of SMEs.publishedVersio

Uncertainty-aware Virtual Sensors for Cyber-Physical Systems

Abstract—Virtual sensors in Cyber-Physical Systems (CPS) are AI replicas of physical sensors that can mimic their behavior by processing input data from other sensors monitoring the same system. However, we cannot always trust these replicas due to uncertainty ensuing from changes in environmental conditions, measurement errors, model structure errors, and unknown input data. An awareness of numerical uncertainty in these models can help ignore some predictions and communicate limitations for responsible action. We present a data pipeline to train and deploy uncertainty-aware virtual sensors in CPS. Our virtual sensor based on a Bayesian Neural Network (BNN) predicts the expected values of a physical sensor and a standard deviation indicating the degree of uncertainty in its predictions. We discuss how this uncertainty awareness bolsters trustworthy AI using a vibration-sensing virtual sensor in automotive manufacturing.Acknowledgement The work has been conducted as part of the InterQ project (958357) and the DAT4.ZERO project (958363) funded by the European Commission within the Horizon 2020 research and innovation programme

Taming Data Quality in AI-Enabled Industrial Internet of Things

We address the problem of taming data quality in artificial intelligence (AI)-enabled Industrial Internet of Things systems by devising machine learning pipelines as part of a decentralized edge-to-cloud architecture. We present the design and deployment of our approach from an AI engineering perspective using two industrial case studies.acceptedVersio

Virtual sensors for erroneous data repair in manufacturing a machine learning pipeline

Manufacturing converts raw materials into finished products using machine tools for controlled material removal or deposition. It can be observed using sensors installed within and around machine tools. These sensors measure quantities, such as vibrations, cutting forces, temperature, currents, power consumption, and acoustic emission, to diagnose defects and enable zero-defect manufacturing as part of the Industry 4.0 vision. The continuity of high-quality sensor data streams is fundamental to predicting phenomena, such as geometric deformations, surface roughness, excessive coolant use, and imminent tool wear with adequate accuracy and appropriate timing. However, in practice, data acquired by some sensors can be of poor quality and unsuitable for prediction due to sensor faults stemming from environmental factors. In this paper, we answer if we can repair erroneous data in a faulty sensor based on data simultaneously available in redundant sensors that observe the same process. We present a machine learning pipeline to synthesize virtual sensors that can step in for faulty sensors to maintain reasonable quality and continuity in sensor data streams. We have validated the synthesized virtual sensors in four industrial case studies.publishedVersio

A blockchain-based framework for trusted quality data sharing towards zero-defect manufacturing

There is a current wave of a new generation of digital solutions based on intelligent systems, hybrid digital twins and AI-driven optimization tools to assure quality in smart factories. Such digital solutions heavily depend on quality-related information within the supply chain business ecosystem to drive zero-waste value chains. To empower zero-waste value chain strategies with meaningful, reliable, and trustful data, there must be a solution for end-to-end industrial data traceability, trust, and security across multiple process chains or even inter-organizational supply chains. In this paper, we first present Product, Process, and Data quality services to drive zero-waste value chain strategies. Following this, we present the Trusted Framework (TF), which is a key enabler for the secure and effective sharing of quality-related information within the supply chain business ecosystem, and thus for quality optimization actions towards zero-defect manufacturing. The TF specification includes the data model and format of the Process/Product/Data (PPD) Quality Hallmark, the OpenAPI exposed to factory system and a comprehensive Identity Management layer, for secure horizontal- and vertical quality data integration. The PPD hallmark and the TF already address some of the industrial needs to have a trusted approach to share quality data between the different stakeholders of the production chain to empower zero-waste value chain strategies.publishedVersio

Standardization and flexibility in a shared component platform

For generic software such as a health information system to be effective it must be properly adapted to its local environment. This localization often requires that applications be remade from scratch, something that puts an increased burden on the development team responsible for localization. Despite the often-strict time constraints, developing new applications can be slow and tedious. One solution could be for local developers to reuse application components made for similar purposes by other localization teams to avoid redundancy. For this purpose, a platform for sharing components between different members and groups within the ecosystem could be imagined needed, but there is little knowledge on how such a platform should be designed and positioned. There is lacking research on component sharing platforms that allow for users to contribute to the platforms content, and this unique feature of the platform calls for knowledge on how it needs to be managed. To attempt to uncover principles on how to position such a platform in the larger ecosystem this thesis asks the question: How to balance standardization and flexibility for a shared component platform as a boundary resource in a diverse ecosystem. The thesis attempts to answer this question by means of design science research, and a platform prototype is created as part of the process as a tool to gather information. The result of the project is five design principles that apply to creating a component reuse platform in such a context

Cybersecurity Awareness and Capacities of SMEs

Small and Medium Enterprises (SMEs) are increasingly exposed to cyber risks. Some of the main reasons include budget constraints, the employees’ lack of cybersecurity awareness, cross-sectoral cyber risks, lack of security practices at organizational level, and so on. To equip SMEs with appropriate tools and guidelines that help mitigate their exposure to cyber risk, we must better understand the SMEs’ context and their needs. Thus, the contribution of this paper is a survey based on responses collected from 141 SMEs based in the UK, where the objective is to obtain information to better understand their level of cybersecurity awareness and practices they apply to protect against cyber risks. Our results indicate that although SMEs do apply some basic cybersecurity measures to mitigate cyber risks, there is a general lack of cybersecurity awareness and lack of processes and tools to improve cybersecurity practices. Our findings provide to the cybersecurity community a better understanding of the SME context in terms of cybersecurity awareness and cybersecurity practices, and may be used as a foundation to further develop appropriate tools and processes to strengthen the cybersecurity of SMEs