Lifecycle Management Research Articles

Realizing the Promise of Digital Engineering: Planning, Implementing, and Evolving the Ecosystem

ABSTRACTGaining benefits of digital engineering is not only about implementing digital technologies. An ecosystem for innovation is a system of systems in its own right, only partly engineered, subject to risks and challenges of evolving socio‐technical systems. This paper summarizes an aid to planning, analyzing, implementing, and improving innovation ecosystems. Represented as a configurable model‐based reference pattern used by collaborating INCOSE working groups, it was initially applied in targeted INCOSE case studies, and subsequently elaborated and applied to diverse commercial and defense ecosystems. Explicating the recurrent theme of consistency management underlying all historical engineering, it is revealing of digital engineering's special promise, and enhances understanding of historical as well as future engineering and life cycle management. It includes preparation of human and technical resources to effectively consume and exploit digital information assets, not just create them, capability enhancements over incremental release trains, and evolutionary steering using feedback and group learning.

Recycling impacts of renewable energy generation-related rare earth resources: A SWOT-based strategical analysis

As the global energy mix transforms and renewable energy technologies rapidly develop, there has been a significant increase in the demand for rare earth elements, which play a crucial role in renewable energy systems. By recycling rare earth resources from obsolete equipment, the reliance on newly mined rare earth resources can be reduced, thereby reducing the related environmental and energy footprints and increasing the stability of the supply chain. This study aims to systematically and strategically analyze and discuss the recycling impacts of renewable energy generation (REG)-related rare earth resources in terms of environmental, economic, social, and governance aspects. A SWOT strategical analysis framework integrating a management cycle and intelligent text data mining is built to address the above tasks from an ESG (Environment, Society, and Governance) perspective. The SWOT analysis of the rare earth recycling impacts focuses on strengths (e.g., reduced dependence on rare resources, reduced environmental and emission pressure), weaknesses (e.g., high cost, technology and management complexity), opportunities (e.g., policy support, technological innovations, diversification of the global supply chain), and threats (e.g., market volatility, the emergence of alternative materials). This analysis framework reveals textual topic changes and suggests cyclic and strategical improvement pathways to analyze the multi-dimensional recycling impacts. The text data analysis results indicate that the trend of rare earth recycling is gradually transformed from technologies, supply and demand, and secondary resources into resource and environment sustainable development. Beyond overcoming identified challenges, future pathways should focus more on the integration of advanced emerging technologies, life cycle management, and dynamic adjustment of recycling strategies. This study could provide policymakers and industry practitioners with recommendations and outlook on current development status and improvement strategies to promote REG-related rare earth recycling.

Towards Frugal Industrial AI: a framework for the development of scalable and robust machine learning models in the shop floor

AbstractArtificial intelligence (AI) among other digital technologies promise to deliver the next level of process efficiency of manufacturing systems. Although these solutions such as machine learning (ML) based condition monitoring and quality inspection are becoming popular, these work under very limited conditions. Solutions do not scale-up in the real environment, where there is a mix of manufacturing equipment, where the quality and quantity of data available changes from machine to machine, or where the process changes, changing the distribution of data (i.e. concept drift). This is particularly challenging in highly reconfigurable and flexible environments. Having to develop machine learning models from scratch every single time is not cost-effective, time-consuming, requires expert knowledge that is typically not available in the manufacturing environment as well as can be challenging when data is not available in high volumes. Model robustness, reusability, adaptability and life cycle management are the keys to scale-up this technology in the manufacturing industry. In this work, a conceptual framework to enable simple and robust ML model development for the shop floor is introduced. Referred here as Frugal Industrial AI, the approach takes advantage of existing models and their context to build more robust ones in a data-efficient manner. Using a semantic knowledge base of how to construct these models for different manufacturing applications and semi-automating the development or reuse of solutions through semantic similarity, it is demonstrated how models can be developed in a more streamlined way. In addition, it is demonstrated how capturing process context information is important for the effective reuse of existing models through continual learning. This is key to building more robust ML solutions that can deal with real changing manufacturing environments, avoiding retraining from scratch as well as enabling the non-expert to use AI effectively on the shop floor.

New paradigms shift in buildings: experimental application of Digital Twin for safety and well-being

Abstract Enhancing durability and service life of buildings and components is pivotal for sustainable development. It constitutes an opportunity to reduce energy consumption, carbon emissions and life cycle impact of buildings in a climate neutral perspective. Issues strongly introduced and set as priorities by EU policies that highlight the urgent need to tackle them also through deeply heritage renovation and digital transformation in the building sector. Digitalization is a cornerstone of this transformation, instrumental in facilitating and enabling it sustainably. The Key Enabling Technologies (KETs) are directly associated with new technological potential and emerging technologies. Digital Twin (DT) approach appears alongside these. Its experimentation and widespread application are gaining prominence in innovating Life Cycle Management (LCM) and sustainability practices of buildings. In this scenario the paper explores the role and potentialities of DT approach presenting the experimental application of the DT4SEM. A digital infrastructure that, by integrating Internet of Things (IoT), synchronizes a physical building with its virtual counterpart. The two realities (physical and virtual) remain interconnected through the mutual exchange of data, both in real-time and asynchronously enabling proactive monitoring and analysis of seismic behaviour. The experimental setup involves simultaneously Big Data analytics, simulation tools and deploying sensors within the sample building. Data is then fed into the virtual DT model, allowing for continuous comparison and analysis to detect anomalies and predict potential risks. This approach facilitates enhanced decision-making, performance optimization and sustainability improvements across the building’s lifecycle: a new vision for built environment management that evolves from a process resulting in a sequence of operative phases into a complex digital infrastructure. The design of the DT4SEM and the current application for seismic monitoring in buildings, results from a collaborative effort involving the academic spinoff BIG srl, the startup Sysdev, the company Berna Engineering srl, and ACCA Software spa.

Research and Development of A Comprehensive Management Platform for Intelligent Production and Construction of the Entire Process of Sulige Gas Field

Abstract The Sulige gas field is the first fully installed atmospheric field in China with an annual production capacity exceeding 30 billion cubic meters. It has the characteristics of low permeability and density, rapid development decline, and large annual compensation decline workload. As a pilot project for the regional production and construction project system in Changqing Oilfield, Sulige Gas Field Development Branch is facing problems such as a wide range of project management points, limited personnel, difficulty in engineering data collection, and tight time connection between various professional businesses. By using digital and information technology, it has developed and created a closed-loop management platform for the digital production capacity construction of the entire business chain of production and construction project drilling, testing, and investment, and constructed a multi-dimensional visual control mechanism for the entire process of a single well, Realized digital management of the operation of a single account for the entire business chain production and construction project of drilling and trial investment; Relying on operator resources to quickly and low-cost expand the basic network, achieving real-time video monitoring of multi platform drilling and testing sites, collection of construction data, and online tracking; Establish a big data model for condition analysis and early warning, and innovate the formation of a “digital+safety dual prevention mechanism” linkage management and dynamic and static risk early warning push; Develop 16 APP functions such as supporting coordinates and well site handover to achieve online flow management of production and construction business, more efficiently guide production organization and close connection of processes, and significantly improve the efficiency of production and construction organization; Creating a dedicated digital and intelligent environment for production and construction, achieving deep integration of digital and intelligent business with Oil and gas fields production and construction, further enhancing project management and collaborative combat capabilities among various specialties. The innovative digital production capacity closed-loop management of Sulige Gas Field platform realizes the full lifecycle and multidisciplinary integrated management of wellbore engineering. Its efficient digital production organization model can provide a certain reference for the organization and management of similar projects in the upstream of exploration and development.

Smart City Aquaculture: AI-Driven Fry Sorting and Identification Model

The development of smart agriculture has become a critical issue for the future of smart cities, with large-scale management of aquaculture posing numerous challenges. Particularly in the fish farming industry, producing single-sex fingerlings (especially male fingerlings) is crucial for enhancing rearing efficiency and could even provide key support in addressing future global food demands. However, traditional methods of manually selecting the gender of broodfish rely heavily on experienced technicians, are labor-intensive and time-consuming, and present significant bottlenecks in improving production efficiency, thus limiting the capacity and sustainable development potential of fish farms. In response to this situation, this study has developed an intelligent identification system based on the You Only Look Once (YOLO) artificial intelligence (AI) model, specifically designed for analyzing secondary sexual characteristics and gender screening in farmed fish. Through this system, farmers can quickly photograph the fish’s cloaca using a mobile phone, and AI technology is then used to perform real-time gender identification. The study involved two phases of training with different sample sets: in the first phase, the AI model was trained on a single batch of images with varying parameter conditions. In the second phase, additional sample data were introduced to improve generalization. The results of the study show that the system achieved an identification accuracy of over 95% even in complex farming environments, significantly reducing the labor costs and physical strain associated with traditional screening operations and greatly improving the production efficiency of breeding facilities. This research not only has the potential to overcome existing technological bottlenecks but also may become an essential tool for smart aquaculture. As the system continues to be refined, it is expected to be applicable across the entire life cycle management of fish, including gender screening during the growth phase, thereby enabling a more efficient production and management model. This not only provides an opportunity for technological upgrades in the aquaculture industry but also promotes the sustainable development of aquaculture. The smart aquaculture solution proposed in this study demonstrates the immense potential of applying AI technology to the aquaculture industry and offers strong support for global food security and the construction of smart cities.

Risk Analysis of Intravascular Shockwave Catheter and Discussion of Critical Control Points throughout Life Cycle

This study summarizes the clinical application, technical characteristics, and production process of intravascular shockwave catheters. It collects statistics on adverse events of related products from the MAUDE database, analyzes the causes of adverse events and failure modes of intravascular shockwave catheters, and identifies risk points. Based on the whole life cycle management method of medical devices, combined with product risks and the requirements of Good Manufacturing Practice for Medical Devices and related appendices, the critical control points of such products in the design and development, manufacturing, sales, and use stages are discussed.

RESEARCH ON THE STANDARDIZATION AND VISUALIZATION PLATFORM FOR MONITORING AND TESTING DATA OF METRO CIVIL FACILITIES BASED ON BIG DATA

Introduction: With consideration for extensive standardized detection and monitoring data, multi-source data fusion analysis and comparison were implemented at cross-project and inter-regional levels to provide robust data support for the full life-cycle management of metro civil facilities. The purpose of the study was to integrate detection and monitoring data on metro bridges and tunnel structures and address the significant data isolation issue in existing systems and various units. Based on big data and Internet of Things technology, this paper investigates the standardized format of uploading, storage, processing, sharing, and other types of managing detection and monitoring data on metro bridges and tunnel structures. Additionally, a third-party data standardization and visualization platform for bridges and tunnel structures was developed, ensuring integration of detection and monitoring data fusion. In the course of the study, the following methods were used: theoretical analysis and software system development. As a result, the practicability and feasibility of the platform were verified through practical applications.

Digital twin-driven lifecycle management for motorized spindle

Digital twin-driven lifecycle management for motorized spindle

Product Lifecycle Management using AI

This paper also discussed into AI built into your (PLM) Product lifecycle Management processes. Transform your PLM with AI-driven insights, recommendations, and automation integrated into supply chain solutions from.

Life-Cycle Performance Modeling for Sustainable and Resilient Structures under Structural Degradation: A Systematic Review

The performance of structures degrades during their service life due to deterioration and extreme events, compromising the social development and economic growth of structure and infrastructure systems. Buildings and bridges play a vital role in the socioeconomic development of the built environment. Hence, it is essential to understand existing tools and methodologies to efficiently model the performance of these structures during their life cycle. In this context, this paper aims to explore the existing literature on the life-cycle performance modeling, assessment, enhancement, and decision making of buildings and bridge infrastructure systems under deterioration and extreme events for a sustainable and resilient built environment. The main objectives are to (1) systematically review the existing literature on life-cycle performance modeling of buildings and bridges based on the PRISMA methodology, (2) provide a bibliometric analysis of the systematically assessed journal articles, (3) perform an analysis of the included articles based on the identified components of life-cycle performance modeling, and (4) provide a discussion on the utilized tools, techniques, methodologies, and frameworks for buildings and bridge infrastructure systems in the life-cycle context. The provided systematic literature review and subsequent discussions could provide an overview to the reader regarding the individual components and existing methodologies of life-cycle performance management under deterioration and extreme events.

Digital Twin Framework for Aircraft Lifecycle Management Based on Data-Driven Models

This paper presents a comprehensive framework for implementing digital twins in aircraft lifecycle management, with a focus on using data-driven models to enhance decision-making and operational efficiency. The proposed framework integrates cutting-edge technologies such as IoT sensors, big data analytics, machine learning, 6G communication, and cloud computing to create a robust digital twin ecosystem. This paper explores the key components of the framework, including lifecycle phases, new technologies, and models for digital twins. It discusses the challenges of creating accurate digital twins during aircraft operation and maintenance and proposes solutions using emerging technologies. The framework incorporates physics-based, data-driven, and hybrid models to simulate and predict aircraft behavior. Supporting components like data management, federated learning, and analytics tools enable seamless integration and operation. This paper also examines decision-making models, a knowledge-driven approach, limitations of current implementations, and future research directions. This holistic framework aims to transform fragmented aircraft data into comprehensive, real-time digital representations that can enhance safety, efficiency, and sustainability throughout the aircraft lifecycle.

Case Studies on Changes and Proposed Process Development Approaches reflecting applicability of PDA Technical Report No. 89 Strategies for Vaccine Development and Lifecycle Management.

Vaccines are complex and a very diverse group of products with relatively long product lifecycles. The manufacturing programs for these vaccines need to be continually updated to comply with evolving regulatory expectations. Members of the Parenteral Drug Association (PDA) Vaccines Interest Group (VIG) authored and published PDA Technical Report No. 89 Strategies for Vaccine Development and Lifecycle Management (TR 89) which seeks to provide context to vaccine developers and manufacturers regarding key aspects of new or legacy vaccines such as control strategy from process development to vaccine lifecycle management, comparability and lifecycle management including technical, validation, quality and regulatory perspectives. To further explain and illustrate the concepts and topics discussed, seven relevant situations were selected as either case studies associated with changes implemented or proposed process development strategies, which are discussed in this article. The situations described are: working cell bank, modification or update of externally supplied product contact components for vaccine manufacturing, raw material change, new product at an existing site, vaccine development acceleration by leveraging existing platforms, selection and implementation of potency method, and modeling for stability forecast prediction. For each situation, the applicable key concepts from TR#89 are discussed as follows: Control Strategy, Prior Knowledge, Relying on PQS, Classification of Parameters, Validation Approach, Use of a Risk-based Approach, Comparability, Use of ICHQ12 and Additional Regulatory Considerations.

Assessing Factors Affecting Fall Accidents among Hispanic Construction Workers: Integrating Safety Insights into BIM for Enhanced Life Cycle Management

Falls are the most common type of accident in the construction industry, and falls to a lower level are among the leading causes of fatalities. Work-related fatalities due to falls, slips, and trips have been increasing, with Hispanic workers among the highest fatalities. This study investigated the association between fall accidents and attributes such as age, musculoskeletal pain (MSPs), sleep hours, safety knowledge, use of personal protective equipment (PPE), and working hours among Hispanic construction workers involved in building construction. This study collected 220 valid responses and used nonparametric chi-square tests and binary logistic regression to analyze the data. This study found that the location of the fall, MSPs, and use of personal protective equipment have a significant effect on the likelihood of having fall accidents. The strongest predictor of fall accidents was “fall from a ladder”, followed by having two or three MSPs. The use of PPE had the highest decreasing ratio in odds of fall accidents, indicating the importance of wearing PPE properly. The results show the importance of integrating safety management strategies within construction projects’ broader life cycle management. The insights list how project engineers can incorporate these findings into Building Information Modeling (BIM) systems to enhance project planning and safety measures in reducing fall-related accidents and their severe consequences. This study highlights the importance of addressing MSPs, properly using PPE, and reducing falls from ladders in the construction industry to prevent fall accidents among Hispanic workers and minimize their severe consequences.

State-of-art review of resilience of bridges and bridge networks

In recent years, many attempts have been made to incorporate resilience frameworks into decision making, establish the best use of available resources and reduce the impact of hazards on bridges and bridge networks. However, scholarly research in this area is still in its early phases, with minimal exploration of research gaps and future prospects. A state-of-the-art review of bridge and bridge network resilience studies using a science mapping approach is presented here. Research on the topic was obtained from the Scopus literature database. The literature database was then analysed using VOSviewer and Nvivo tools to display the domain-specific body of knowledge. The study then focused on the most prolific researchers and disaster types, bridge resilience assessment approaches, current research trends, future directions and theoretical and practical implications. The research paradigm on the resilience of bridges and bridge networks is changing toward the usage of digital tools and technologies. In this context, a framework id proposed by integrating building information modelling/geographic information systems and digital twin models. The proposed framework can aid bridge owners in accumulating iterative data, creating multi-hazard disaster preparedness policies and designing and enhancing life-cycle management and maintenance tasks of bridges and bridge networks.

Discussion On Safety Management Of Urban Gas Non-Resident Users

with the rapid development of urbanization, the number of non-residential users of gas consumption continues to increase, and the corresponding security wind direction and problems are increasing. By combing the gas safety management process of non-residential users in large-scale urban gas enterprises, embedding the whole life cycle management process, improving the quality of gas safety management of non-residential users, eliminating some potential safety hazards and reducing the accident development rate, at the same time for other enterprises in the same industry to provide reference.

Editorial — Notes on sustainability and the lifecycle management of constructed assets

Editorial — Notes on sustainability and the lifecycle management of constructed assets

Real-world data to support post-market safety and performance of embolization coils: evidence generation from a medical device manufacturer and data institute partnership

BackgroundRecognizing the limitations of pre-market clinical data, regulatory authorities have embraced total product lifecycle management with post-market surveillance (PMS) data to assess medical device safety and performance. One method of proactive PMS involves the analysis of real-world data (RWD) through retrospective review of electronic health records (EHR). Because EHRs are patient-centered and focused on providing tools that clinicians use to determine care rather than collecting information on individual medical products, the process of transforming RWD into real-world evidence (RWE) can be laborious, particularly for medical devices with broad clinical use and extended clinical follow-up. This study describes a method to extract RWD from EHR to generate RWE on the safety and performance of embolization coils.MethodsThrough a partnership between a non-profit data institute and a medical device manufacturer, information on implantable embolization coils’ use was extracted, linked, and analyzed from clinical data housed in an electronic data warehouse from the state of Indiana’s largest health system. To evaluate the performance and safety of the embolization coils, technical success and safety were defined as per the Society of Interventional Radiology guidelines. A multi-prong strategy including electronic and manual review of unstructured (clinical chart notes) and structured data (International Classification of Disease codes), was developed to identify patients with relevant devices and extract data related to the endpoints.ResultsA total of 323 patients were identified as treated using Cook Medical Tornado, Nester, or MReye embolization coils between 1 January 2014 and 31 December 2018. Available clinical follow-up for these patients was 1127 ± 719 days. Indications for use, adverse events, and procedural success rates were identified via automated extraction of structured data along with review of available unstructured data. The overall technical success rate was 96.7%, and the safety events rate was 5.3% with 18 major adverse events in 17 patients. The calculated technical success and safety rates met pre-established performance goals (≥ 85% for technical success and ≤ 12% for safety), highlighting the relevance of this surveillance method.ConclusionsGenerating RWE from RWD requires careful planning and execution. The process described herein provided valuable longitudinal data for PMS of real-world device safety and performance. This cost-effective approach can be translated to other medical devices and similar RWD database systems.

Precision prevention in occupational health: a conceptual analysis and development of a unified understanding and an integrative framework.

Precision prevention implements highly precise, tailored health interventions for individuals by directly addressing personal and environmental determinants of health. However, precision prevention does not yet appear to be fully established in occupational health. There are numerous understandings and conceptual approaches, but these have not yet been systematically presented or synthesized. Therefore, this conceptual analysis aims to propose a unified understanding and develop an integrative conceptual framework for precision prevention in occupational health. Firstly, to systematically present definitions and frameworks of precision prevention in occupational health, six international databases were searched for studies published between January 2010 and January 2024 that used the term precision prevention or its synonyms in the context of occupational health. Secondly, a qualitative content analysis was conducted to analyze the existing definitions and propose a unified understanding. Thirdly, based on the identified frameworks, a multi-stage exploratory development process was applied to develop and propose an integrative conceptual framework for precision prevention in occupational health. After screening 3,681 articles, 154 publications were reviewed, wherein 29 definitions of precision prevention and 64 different frameworks were found, which can be summarized in eight higher-order categories. The qualitative content analysis revealed seven themes and illustrated many different wordings. The proposed unified understanding of precision prevention in occupational health takes up the identified themes. It includes, among other things, a contrast to a "one-size-fits-all approach" with a risk- and resource-oriented data collection and innovative data analytics with profiling to provide and improve tailored interventions. The developed and proposed integrative conceptual framework comprises three overarching stages: (1) data generation, (2) data management lifecycle and (3) interventions (development, implementation and adaptation). Although there are already numerous studies on precision prevention in occupational health, this conceptual analysis offers, for the first time, a proposal for a unified understanding and an integrative conceptual framework. However, the proposed unified understanding and the developed integrative conceptual framework should only be seen as an initial proposal that should be critically discussed and further developed to expand and strengthen both research on precision prevention in occupational health and its practical application in the workplace.

Membrane‐Free Electrocatalytic Co‐Conversions of PBS Waste Plastics and Maleic Acid into High‐Purity Succinic Acid Solid

AbstractPlastic pollution, an increasingly serious global problem, can be addressed through the full lifecycle management of plastics, including plastics recycling as one of the most promising approaches. System design, catalyst development, and product separation are the keys in improving the economics of electrocatalytic plastics recycling. Here, a membrane‐free co‐production system was devised to produce succinic acid (SA) at both anode and cathode respectively by the co‐electrolysis of polybutylene succinate (PBS) waste plastics and biomass‐derived maleic acid (MA) for the first time. To this end, Cr3+‐Ni(OH)2 electrocatalyst featuring much enhanced 1,4‐butanediol (BDO) oxidation reaction (BOR) activity has been synthesized and the role of doped Cr has been revealed as an “electron puller” to accelerate the rate‐determining step (RDS) in the Ni2+/Ni3+ cycling. Impressively, an extra‐high SA production rate of 3.02 g h−1 and ultra‐high apparent Faraday efficiency towards SA (FEapparent=181.5 %) have been obtained. A carbon dioxide‐assisted sequential precipitation approach has been developed to produce high‐purity SA and byproduct NaHCO3 solids. Preliminary techno‐economic analysis demonstrates that the reported system is economically profitable and promising for future industrial applications.