Life Cycle Data Research Articles

An Architecture for information consistent identification systems in circular value creation

Efficient management of circular value creation requires holistic traceability systems providing consistent collection and sharing of product lifecycle data among various stakeholders in a supply chain, even when individual parts are reused or repurposed in new products. In these traceability systems, unique identification of products and parts is paramount for maintaining consistency in the lifecycle data. However, as supply chains become increasingly complex and dynamic, challenges in consistent identification arise. Therefore, this paper discusses data consistency challenges for identification systems, focusing on creating and managing unique identifiers in the physical and digital world. Based on that discussion, this paper proposes an architecture for information-consistent part identification systems in complex circular value chains. The architecture uses AI-based fingerprinting technology to ensure marking-free product identification, the Asset Administration Shell to model relevant traceability data, and blockchain technology to ensure consistent product identifiers.

Packaging Stretch film alternatives in a circular economy perspective: comparison of three scenarios with a Life Cycle Assessment approach

The plastic litter issues make urgent the implementation of circular economy practices in the packaging sector. To this purpose, the present study environmentally analyses, through a Life Cycle Assessment, three plastic film alternatives used in Europe for pallets wrapping. Considering the impact categories of the Environmental Product Declaration (EPD) 2018 methodology, a traditional linear low-density polyethylene LLDPE film was compared not only to a thinner film with 60% post-consumer recycled LLDPE but also to a thinner high pre-stretch film. The ISO 14040 and 14044 as well as the Packaging Product Category Rule were followed in the study. Primary data of the films life cycle were collected thanks to the collaboration of a plastic film producer, while secondary data were modelled through the Ecoinvent database, available in the SimaPro software used in the study. The results highlight that the best option to reduce the environmental impact of these films is related to the reduction of plastic use thanks to lower thickness and higher stretching.

The role of BIM and GIS in HS2 historic environment data management, an overview of HS2 Phase 1, UK

The HS2 historic environment programme undertaken for Phase One of HS2 between London and the West Midlands has resulted in a substantial digital archive, including Geographic Information Systems (GIS) data. According to the BIM (Building Information Modelling) approach, HS2 historic environment assets are considered as part of the construction assets, alongside the other disciplines assets such as highways, bridges, tunnels, and fencing. The GIS and spatial data play the role of a glue to demonstrate the interrelationship and hierarchy between archaeological assets, recording their location and geometry. Designating a unique asset ID (UAID) to each archaeological asset, and joining them to their attributes table and relating documents, creates a relationship between historic environment assets GIS data and their respective non-GIS data. According to HS2 digital engineering with BIM approach, HS2 historic environment core and primary assets have been identified, which the hierarchical order of them is as follows: Location Specific Written Scheme of Investigation (LS-WSI) and Project Plans (PPs) as core assets, and Written Scheme of Investigation Interventions (WSI-Interventions), Archaeological Features, and Archaeological Objects as historic environment primary assets. Such an efficient, transparent, and readable asset data structure provides a lasting and valuable legacy for the lifecycle of the project digital data. Four different systems combine to form the digital legacy of the project, which complement the physical archive. These four systems are: HS2 Asset Information Management System (AIMS) and GIS systems, as well as Online Access to the Index of Archaeological Investigations (OASIS), and supporting digital data curated with the Archaeological Data Service (ADS) systems. As significant parts of the programme legacy, HS2's historic environment physical and digital archive establish an unprecedented opportunity for knowledge creation.

Metadata for Discovery. Planning for an Information Network

In advance of building a new high speed rail line aimed at connecting the North and South of England, the UK is undertaking the largest archaeological intervention in its history. While the High Speed 2 (HS2) project has created a wealth of new information, before any of that information can be disseminated to the general public, it must first be properly collected, documented, and linked. To this end, data collection is integral to facilitate effective data dissemination and FAIR - Findable, Accessable, Interoperable and Reusable - data to achieve the greatest public value for an archive. The most exciting search results and maps come from what is often seen as scary technical jargon. This paper will discuss some of the limitations the Archaeology Data Service (ADS) experiences as a digital repository, what is currently being done to maximise the reach of the ADS collections, and what tools have been created to aid both depositors and digital archives alike. Starting at the beginning of the data life cycle, this article will show how large infrastructure projects like HS2 allow the ADS to work with depositors to raise issues about data collection, generation, and description during project development and how collaborative efforts can improve the creation and import of data and metadata into the archive. The ADS has made steps towards keeping our data FAIR but simple, both by streamlining what metadata is essential to foster better discovery and reuse within an archive, and from there, how metadata can be passed to external data catalogues such as ARIADNEplus and The National Archives in the UK.

Data from start to finish: A System Life Cycle Data Map

The megatrend sustainability is changing the way industrial companies view their products and services and how they interact with the environment. In combination with digitalization, the way their systems (products and services) evolve is undergoing a significant shift, offering huge potential for optimization in favor of sustainability. Large volumes of data from the systems-in-use, software tools, and databases reshape how the system life cycle unfolds. This results into complex interrelationships between the life cycle phases and the created and used artifacts (i.e. models or production reports). The challenge for industrial companies is to get a structured overview of data sources, and data flows along the system life cycle. To address these challenges, a system life cycle data map has been developed utilizing design science. The data map illustrates the system life cycle phases, the related data sources and associated artifacts, and their interrelationships, i.e., the data flows between different artifacts. Applying the data map for a given system enhances data clarity and opens new opportunities for improving the system life cycle, its design, and potential for data-driven applications.

A Tensor-based t-SVD-LSTM Remaining Useful Life Prediction Model for Industrial Intelligence

The Industrial Internet of Things (IIoT) integrates smart sensors and actuators for the widespread digitization and enhancement of industrial and manufacturing processes. Smart equipment is used to improve the industrial intelligence and make industrial production more flexible, safer and more efficient. For complex equipment, product life-cycle management (PLM) including remaining useful life (RUL) is one of the essential issues for industrial intelligence. In this paper, a tensor-based remaining useful life prediction model is proposed to facilitate the life-cycle management, which combines features from time domain and frequency domain. For the characteristics of continuous generation of industrial data streaming, tensor singular value decomposition (t-SVD) is combined with long shortterm memory network (LSTM) method to predict the RUL of devices from high-order and high-noise time series data. Finally, experiments are carried out on three different data sets including the battery charge and discharge data set, the bearing acceleration life cycle data set, and the turbofan data set to measure the performance of the proposed model.

Life cycle assessment of a telematics box with ICT impact allocation and quantification – Application to precision agriculture technology and robotics

Telematics are increasingly promising and adopted to augment the productivity and efficiency of systems in various industrial sectors. Although the potential environmental benefits of deploying telematics solutions are often highlighted, the proper quantification and allocation of added information and communication technology-related (ICT) equipment is a challenge that still needs to be addressed. To help address that gap, a life cycle assessment (LCA) is done on a telematics box equipped on an autonomous robot performing precision agricultural tasks. The LCA is conducted using primary industrial data and state-of-the-art life cycle data libraries. Particular attention is paid to how to model, allocate, and quantify the impact related to telematics data exchanges, through mobile networks (like 4G or 5G) and Wi-Fi, as well as the infrastructures needed to operate the telematics box. By having a complete picture and quantitative understanding of such additional impacts, the LCA results help determine the minimum improvement level, e.g., in terms of resource efficiency or yield, that the ICT solution needs to bring to be ecologically beneficial.

Understanding the value of curation: A survey of US data repository curation practices and perceptions.

Data curators play an important role in assessing data quality and take actions that may ultimately lead to better, more valuable data products. This study explores the curation practices of data curators working within US-based data repositories. We performed a survey in January 2021 to benchmark the levels of curation performed by repositories and assess the perceived value and impact of curation on the data sharing process. Our analysis included 95 responses from 59 unique data repositories. Respondents primarily were professionals working within repositories and examined curation performed within a repository setting. A majority 72.6% of respondents reported that "data-level" curation was performed by their repository and around half reported their repository took steps to ensure interoperability and reproducibility of their repository's datasets. Curation actions most frequently reported include checking for duplicate files, reviewing documentation, reviewing metadata, minting persistent identifiers, and checking for corrupt/broken files. The most "value-add" curation action across generalist, institutional, and disciplinary repository respondents was related to reviewing and enhancing documentation. Respondents reported high perceived impact of curation by their repositories on specific data sharing outcomes including usability, findability, understandability, and accessibility of deposited datasets; respondents associated with disciplinary repositories tended to perceive higher impact on most outcomes. Most survey participants strongly agreed that data curation by the repository adds value to the data sharing process and that it outweighs the effort and cost. We found some differences between institutional and disciplinary repositories, both in the reported frequency of specific curation actions as well as the perceived impact of data curation. Interestingly, we also found variation in the perceptions of those working within the same repository regarding the level and frequency of curation actions performed, which exemplifies the complexity of a repository curation work. Our results suggest data curation may be better understood in terms of specific curation actions and outcomes than broadly defined curation levels and that more research is needed to understand the resource implications of performing these activities. We share these results to provide a more nuanced view of curation, and how curation impacts the broader data lifecycle and data sharing behaviors.

BOLD v4: A Centralized Bioinformatics Platform for DNA-Based Biodiversity Data.

BOLD, the Barcode of Life Data System, supports the acquisition, storage, validation, analysis, and publication of DNA barcodes, activities requiring the integration of molecular, morphological, and distributional data. Its pivotal role in curating the reference library of DNA barcodes, coupled with its data management and analysis capabilities, makes it a central resource for biodiversity science. It enables rapid, accurate identification of specimens and also reveals patterns of genetic diversity and evolutionary relationships among taxa.Launched in 2005, BOLD has become an increasingly powerful tool for advancing the understanding of planetary biodiversity. It currently hosts 17 million specimen records and 14 million barcodes that provide coverage for more than a million species from every continent and ocean. The platform has the long-term goal of providing a consistent, accurate system for identifying all species of eukaryotes.BOLD's integrated analytical tools, full data lifecycle support, and secure collaboration framework distinguish it from other biodiversity platforms. BOLD v4 brought enhanced data management and analysis capabilities as well as novel functionality for data dissemination and publication. Its next version will include features to strengthen its utility to the research community, governments, industry, and society-at-large.

Construction of a micro-application for full lifecycle data management based on a new-generation electricity consumption information collection system

Construction of a micro-application for full lifecycle data management based on a new-generation electricity consumption information collection system

The Circular Digital Cockpit: Towards an actionable framework for life cycle circularity assessment and decision

Digital solutions such as IoT, machine learning, big data, and simulation platforms are increasingly integrated into industrial practices but are not fully leveraged for enhancing circularity in the value chain. To address this, the Circular Digital Cockpit (CDC) framework is proposed to aid manufacturers and public authorities in transitioning towards circularity. The CDC is structured to analyze and improve organizational activities, incorporating Data Science, Digital Enterprise Architecture, and Circular Economy Strategies. It comprises two main parts. The first, "Measure, Understand, Prioritize", focuses on transforming lifecycle data into circularity indicators suitable for various organizational roles, like product and process managers. This helps in prioritizing improvement objectives both individually and organization-wide. The second part, “Act – Imagine, Simulate, Decide, Deploy –, Monitor”, guides each actor in developing and implementing actions aimed at circularity and sustainability goals. This involves assembling a global action plan and employing the Theory of Change methodology to assess and refine these actions based on their impact on circularity and sustainability. The CDC's adaptability and potential in enhancing circular practices across various industries and territorial levels are tested in diverse scenarios, including the remanufacturing of heavy vehicles, the maintenance of medical devices, and the management of bio-waste and construction waste.

A Continuous Remaining Useful Life Prediction Method With Multistage Attention Convolutional Neural Network and Knowledge Weight Constraint.

The rotating machinery is continuously monitored in practical application. However, the historical life-cycle data cannot be always preserved due to the limited storage resource; meanwhile, the on-site computing platform cannot process a large number of monitoring samples. It brings a great challenge for the remaining useful life (RUL) prediction. Thus, continuous learning (CL) is introduced into RUL prediction model for achieving its knowledge accumulation and dynamic update. To improve the performance of continuous RUL prediction, this article presents a new RUL prediction methodology with a multistage attention convolutional neural network (MSACNN) and knowledge weight constraint (KWC). First, an improved multihead full-channel sight self-attention (MFCSSA) mechanism is proposed to capture the global degradation information across all channels. MSACNN is then constructed by embedding MFCSSA, squeeze-and-excitation (SE) mechanism, and convolutional block attention module (CBAM) into different stages of feature extraction, which enables it to capture the global degradation information and refine the feature representations progressively. The KWC mechanism based on the importance of weight parameters and gradient information is proposed and integrated into MSACNN to achieve the continuous RUL prediction task. The proposed KWC can effectively alleviate catastrophic forgetting in CL. Finally, the experimental results on the life-cycle bearing and gear datasets demonstrate that MSACNN has a higher accuracy than the existing prediction methods. Moreover, the KWC mechanism performs better than typical CL methods in retaining the previously learned knowledge while acquiring the new task knowledge. Therefore, the proposed methodology can be better applied to the continuous RUL prediction tasks than the advanced methods of the same kind.

An NLP-based framework for early identification of design reliability issues from heterogeneous automotive lifecycle data

Natural Language Processing is increasingly used in different areas of design and product development with varied objectives, from enhancing productivity to embedding resilience into systems. In this paper, we introduce a framework that draws on NLP algorithms and expert knowledge for the automotive engineering domain, to extract actionable insight for system reliability improvement from data available from the operational phase of the system. Specifically, we are looking at the systematic exploration and exploitation of automotive heterogeneous data sources, including both closed-source (such as warranty records) and open-source (e.g., social networks, chatrooms, recall systems) data, to extract and classify information about faults, with predictive capability for early detection of issues. We present a preliminary NLP-based framework for enhancing system knowledge representation to increase the effectiveness and robustness of information extraction from data, and discuss the temporal alignment of data sources and insight to improve prediction ability. We demonstrate the effectiveness of the proposed framework using real-world automotive data in a recall study for a vehicle lighting system and a particular manufacturer: four recall campaigns were identified leading to corrective actions by the warranty experts.

The ripple effect of dataset reuse: Contextualising the data lifecycle for machine learning data sets and social impact

Although there exists a rich literature on data lifecycle, a common framework for data lifecycle depicts reuse as the last stage. However, this framework fails to explain the complex lifecycle of machine learning (ML) data sets, which can have many different afterlives. Data sets for ML can be expanded to supplement previous research, and researchers can concatenate multiple data sets to develop new models. This study discusses ML dataset reuse through the lens of the data–information–knowledge–wisdom pyramid. In social science research, researchers might reuse data to analyse a new research question that is still in the context of the data domain. By contrast, research practices in ML, where researchers layer multiple data sets for training purposes, require us to ask whether the existing data lifecycle model, ending with ‘reuse’, is appropriate for explaining such an iterative and layered lifecycle. This study introduces one case of merging computer vision data set and natural language processing data set and two cases of applying ML models from outside of the ML community (hate speech detection and politeness detection) to justify a framework for a ML dataset lifecycle. Last but not least, this study proposes a ML dataset lifecycle and provides case examples to describe each stage.

Accelerated Life Testing Dataset for Lithium-Ion Batteries with Constant and Variable Loading Conditions

The development of new modes of transportation, such as electric vertical takeoff and landing (eVTOL) aircraft and the use of drones for package and medical delivery, has increased the demand for reliable and powerful electric batteries. The most common batteries in electric-powered vehicles use Lithium-ion (Li-ion). Because of their long cycle life, they are the preferred choice for battery packs deployed over a lifespan of many years. Thus, battery aging needs to be well understood to achieve safe and reliable operation, and life cycle experiments are a crucial tool to characterize the effect of degradation and failure. With the importance of battery durability in mind, we present an accelerated Li-ion battery life cycle data set, focused on a large range of load levels, for batteries composed of two 18650 cells. We tested 26 battery packs grouped by: (i) constant or random loading conditions, (ii) loading levels, and (iii) number of load level changes. Furthermore, we conducted load cycling on second-life batteries, where surviving cells from previously-aged packs were assembled to second-life packs. The goal is to provide the PHM community with an additional data set characterized by unique features. The aggressive load profiles create large temperature increases within the cells. Temperature effects becomes therefore important for prognosis. Some samples are subject to changes in amplitude and number of load levels, thus approaching the level of variability encountered in real operations. Reassembling of survival cells into new packs created additional data that can be used to evaluate the performance of recommissioned batteries. The data set can be leveraged to develop and test models for state-of-charge and state-of-health prognosis. This paper serves as a companion to the data set. It outlines the design of experiment, shows some exemplifying time-series voltage curves and aging data, describes the testbed design and capabilities, and also provides information about the outliers detected thus far. Upon acceptance, the data set will be made available on the NASA Ames Prognostics Center of Excellence Data Repository.

Special Issue: 2023 Research Data Access and Preservation (RDAP) Summit

The 2023 Research Data Access and Preservation (RDAP) Summit, Building on Experience: Centering Communities in Data Creation and Access, focused on engagement with and building communities within data environments, including how data is being made more accessible for a wider range of communities. The 2023 RDAP Summit was a natural extension from the prior year’s theme of Envisioning an Inclusive Data Future, which highlighted the ways data service providers tailor their services to address specific needs. A selection of presentations from this year’s Summit were expanded into articles for this special issue covering topics on developing and maintaining communities that address aspects across the research data life cycle.

Operational Efficiency and Environmental Impacts of Food Service Establishments in Phuket, Thailand

The expansion of global tourism development has led to an increase in environmental burdens. This study aimed to assess the operational performance and the environmental impacts associated with food service establishments in Phuket, an international tourist island in Thailand. A joint application of life cycle assessment and data envelopment analysis was employed to evaluate environmental burdens and calculate the efficiency scores encompassing several inputs and outputs of each food service establishment. There are several characteristics of food service establishments that affect their operational and environmental performance. The results showed that location, opening hours, and surrounding conditions are key factors driving the performance of air-conditioned food service establishments, while the performance of non-air-conditioned ones is mainly decided by opening hours. However, these factors are not significant for the performance of street food service establishments. It is advised to carefully consider the characteristics of the included food service establishments based on their products and services to enhance their performance. As an effort to achieve a balanced connection between environmental quality and economic growth, it is essential to evaluate both operational and environmental performance. Attaining maximum operational efficiency does not necessarily ensure the same for eco-efficiency.

RESTRICTED DATA MANAGEMENT: THE CURRENT PRACTICE AND THE FUTURE.

Many organizations across the world that manage restricted data have adapted the Five Safes framework (safe data, safe projects, safe people, safe setting, safe output) for their management of restricted and confidential data. While the Five Safes have been well integrated throughout the data life cycle, organizations encounter several challenges with regard to safe data management. In this paper, we review current practices for restricted data management, and discuss challenges and future directions. We focus on data use agreements, disclosure risk review, and training. In the future, organizations managing restricted data may need proactively to take into consideration reducing inequalities in access to scientific data, preventing unethical use of data, and managing various types of data.

Protecting Sensitive Data Early in the Research Data Lifecycle

How do researchers in fieldwork-intensive disciplines protect sensitive data in the field, how do they assess their own practices, and how do they arrive at them? This article reports the results of a qualitative study with 36 semi-structured interviews with qualitative and multi-method researchers in political science and humanitarian aid/migration studies. We find that researchers frequently feel ill-prepared to handle the management of sensitive data in the field and find that formal institutions provide little support. Instead, they use a patchwork of sources to devise strategies for protecting their informants and their data. We argue that this carries substantial risks for the security of the data as well as their potential for later sharing and re-use. We conclude with some suggestions for effectively supporting data management in fieldwork-intensive research without unduly adding to the burden on researchers conducting it.

Attention features selection oversampling technique (AFS-O) for rolling bearing fault diagnosis with class imbalance

When using data-driven methods for fault diagnosis of mechanical rotating components such as gears and bearings, there is a problem of class imbalance in the lifecycle data collected by sensors. The most commonly used method to address this issue is the synthetic minority over-sampling technique, which synthesizes samples in the feature space, but its blind synthesis may lead to redundant features in the synthetic samples. To avoid this deficiency, this paper proposes a feature-weighted oversampling method called AFS-O (Attention Features Selection Oversampling Technique). First, time–frequency domain features are extracted from the full lifecycle data of bearings to construct an initial subset of features, which serves the input to AFS. Then, AFS is then used to obtain the distribution of feature selection patterns and generate feature weights to determine the inclusion or exclusion of each feature, thereby constructing an optimal subset of features. Finally, the optimal feature subset is synthetically oversampled to achieve class-balanced data, which is then fed into a classifier. AFS-O is applied to the rolling bearing accelerated lifetime dataset from Xi’an Jiaotong University. Experimental results demonstrate that AFS-O outperforms other state-of-the-art synthetic oversampling algorithms in terms of Gmean, , and Recall, confirming the effectiveness of the proposed method.