Industrial Assets Research Articles

Implementation of the Asset Management, Operational Reliability and Maintenance Survey in Recycled Beverage Container Manufacturing Lines

The effectiveness of a comprehensive maintenance and reliability management process can be assessed through an in-depth analysis of various factors that collectively represent the maintenance contribution to the operational and production processes of an industrial asset. There are no simple formulas for designing an integrated maintenance and reliability model within an asset management framework (in accordance with the ISO 55001 standard), nor are there fixed or universal rules that apply equally to all production assets over time. In light of this, the primary goal of this article is to provide an overview of the implementation project of the AMORMS (Asset Management, Operational Reliability and Maintenance Survey), based on the maintenance management model (MMM) developed by INGEMAN, at the SINEA PERU plant, a leading company in Latin America specializing in the industrial production of recycled plastic containers for commercial beverages (PET preforms—Polyethylene Terephthalate). Lastly, the article outlines the recommendations and high-impact action plans that will support SINEA PERU in strengthening the efficiency of its maintenance and reliability management processes while effectively optimizing the value of its industrial assets throughout its life cycle.

Data and transaction security as priority in operationalizing blockchain technology in ferry transportation: a literature review

Abstract Blockchain technology has a potential benefit of becoming valuable asset in ferry transportation industry to increase safety, transparency, efficiency and meet increasing market demands in meeting safety and sustainability standards. This study aims to find the important role of blockchain technology in the development of ferry transportation. Two main approaches in describing analytical techniques include bibliometrics to find derivatives of blockchain technology concept, and analytical hierarchy process to obtain alternative priorities in the development of ferry transportation. Journal publications within last ten years obtained through searches on Google Scholar are used as the main data in developing scientific literature analysis. The results found that several alternatives play an important role in the development of blockchain technology applied to ferry transportation include data and transaction security, supply chain tracking, identity and access management, operational efficiency, regulatory compliance, interoperability with external systems, transparency and accountability. Three alternatives in developing blockchain technology are main priority in this study and potential to have high novelty value, including data and transaction security, operational efficiency and supply chain tracking. The results of study recommend that the operationalization of ferry transportation really requires the development of blockchain technology to encourage increased safety and sustainability of ferry transportation.

In - Depth Research on User Profiles in Securities Companies Under the Digital Background

Based on systematically grasping the development, integration, and application of relevant technologies such as the industrial value - chain model in the securities industry, the securitization of real - industry assets and their flow patterns, and regulatory and risk - prevention - and - control models, this paper conducts in - depth research on user profiles centering on the main path of securities business and product innovation. Aiming at the modeling problems of business - behavior - level homomorphism consistency and business - network - isomorphy consistency in business network systems, the concept of business - network profiles is originally proposed. That is, taking the description image of user profiles as an index, equivalent classification analysis of the description image of business - network profiles is carried out. According to the behavior equivalence class and behavior - homomorphism consistency, the user - network associations and interaction relationships are identified, and the user - association - action - and - influence - network profile is established, thereby obtaining more accurate and scientific data analysis and business applications. Finally, from the perspectives of algorithms and modeling, the structural model frameworks of the correlation relationships, interaction relationships, and influence relationships between user profiles and business - network profiles are discussed one by one, and the extended user profiles, business - network profiles, and their application results are presented.

DECENTRALIZED BLOCKCHAIN TECHNOLOGIES FOR CREATING NEW TYPES OF INVESTMENT ASSETS IN THE CREATIVE INDUSTRY

This article examines the application of blockchain technology for creating and circulating new investment assets in the creative industry. The study analyzes the impact of decentralized technologies, particularly blockchain, on the formation of new types of investment assets in the creative sector of the economy and assesses their potential for industry development. The research employs a systematic approach, methods of analysis and synthesis, comparative and statistical analysis, and generalization of expert assessments. The current state and trends in the use of blockchain technologies in the creative industry have been investigated, with a focus on NFTs, intellectual property tokenization, and decentralized autonomous organizations (DAOs). The study concludes that blockchain technologies create fundamentally new opportunities for monetization and investment in creative assets, while also highlighting challenges such as regulatory uncertainty and technological limitations. Recommendations for maximizing the positive impact of blockchain on the creative economy are provided, along with suggestions for further research.

Development of a methodology for diagnosing faults in bearings operating under variable operating conditions based on self-supervised learning

Predictive maintenance plays a crucial role in ensuring the efficiency and availability of industrial assets. Bearings, essential components in rotating machinery, are subject to diverse and complex operating conditions, necessitating advanced fault diagnosis methods. Traditional diagnostic approaches often rely on supervised learning, which requires extensive labeled datasets, a process that is both costly and impractical under varying conditions. This work proposes a novel methodology for diagnosing bearing faults using self-supervised learning, which leverages unlabeled data to generate useful representations for fault detection. The proposed approach aims to develop an end-to-end system that processes raw vibration signals to accurately diagnose the current state of bearings, including fault detection, localization, and severity assessment. The methodology is validated using experimental data from a test rig simulating various fault conditions and will be further tested on real industrial machinery. This research contributes to the development of more efficient and generalizable diagnostic tools for rotating machinery, particularly under variable operational conditions.

New food ingredient via acid-tolerant Rhizopus oligorporus growth

As the global population grows, the consumption of unsustainably produced proteins increases. Researching sustainable food sources with a positive environmental impact is essential. This research provides valuable insights into the production and application of nutrient rich food sources. The potential of the biomass generated through fermentation with R. oligosporus was evaluated as a new protein source with low environmental impact. A fermentation method has been optimized to maximize the biomass production of R. oligosporus mycelium in solid-state and submerged-state fermentation, yielding 5.9 g dry weight/L and 5 g dry weight/L, respectively. The growth medium for these fungi was adapted to acid-tolerant (pH 4-3) conditions to create a food development environment that is more difficult for pathogens to thrive in, both in solid state and submerged state fermentation. The highest protein content obtained was 38.4 % wet weight by solid state fermentation. Fungal biomass exhibited techno-functional properties relevant to food product development tested both on pH 4 and pH 3, showing high water absorption and emulsifying activity, particularly in submerged fermentation at pH 3. In contrast, solid-state fermentation at pH 4 resulted in higher emulsion stability. This research demonstrates the potential of Rhizopus oligosporus as an acid-tolerant, high-quality protein ingredient with valuable functional properties. Its efficient production using minimal resources makes it a valuable asset for the food industry.

K-콘텐츠를 활용한 지역관광 활성화 방안 : 춘천 사례를 중심으로

By focusing on the case of Chuncheon, this study explores the local governments' strategies to revitalize local tourism by utilizing K-contents. The study examines Chuncheon’s geographical characteristics, natural resources, cultural resources, and industrial assets within the broader context of K-contents which also includes local content. In-depth interviews with experts and focus group interviews with consumers were conducted. The analysis utilized the 'Local Content-Based Tourism Industry Competitiveness Evaluation Model' which focuses on four key elements: cultural tourism content resources, K-content-based tourism market, strategy and business implementation system, and supporting environment (i.e., infrastructure). The results revealed that while Chuncheon possesses diverse natural and cultural resources, there is a need for improvement in linking and utilizing these assets. The study highlights the necessity of developing and promoting contents that align with current tourism trends and the preferences of the MZ generation. Additionally, it calls for an enhancement of infrastructure, such as accommodation and transportation, to better connect major tourist spots within the city. To foster the cultural and tourism content industry, the study also emphasizes the urgent need to establish governance to systematically support development efforts. The research proposes a five-theme strategy to revitalize tourism in Chuncheon: regional characteristics, culture and festivals, filming location spots, leisure sports, and food. This approach provides practical implications by offering a comprehensive perspective on the current situation, challenges, and revitalization measures for Chuncheon tourism.

Optimizing Organizational Efficiency: The Strategic Necessity of Modern Industrial Asset Hierarchy Management

Asset Hierarchy Management (AHM) is not just a theoretical concept but a practical tool for optimizing operations in modern businesses, particularly in sectors with intricate infrastructures such as manufacturing, utilities, and healthcare. This research paper explores the significance of AHM in standardizing and enhancing organizational efficiency. It traces AHM's evolution, delineates its benefits, and provides strategies for implementation. By emphasizing the tangible benefits of AHM, the paper aims to instill optimism in the audience about its potential impact. The paper outlines prerequisites and steps for developing a robust asset hierarchy, stressing transparent governance and integration with technology solutions like Enterprise Asset Management (EAM) software. Furthermore, it presents a detailed framework for implementing AHM in manufacturing plants, highlighting the importance of defining clear asset categories, hierarchical levels, and relationships. By adhering to best practices and leveraging advanced tools, organizations can effectively manage assets, drive operational excellence, and achieve sustainable growth.

Deep expert network: A unified method toward knowledge-informed fault diagnosis via fully interpretable neuro-symbolic AI

In recent years, intelligent fault diagnosis (IFD) based on Artificial Intelligence (AI) has gained significant attention and achieved remarkable breakthroughs. However, the black-box property of AI-enabled IFD may render it non-interpretable, which is essential for safety-critical industrial assets. In this paper, we propose a fully interpretable IFD approach that incorporates expert knowledge using neuro-symbolic AI. The proposed approach, named Deep Expert Network, defines neuro-symbolic node, including signal processing operators, statistical operators, and logical operators to establish a clear semantic space for the network. All operators are connected with trainable weights that decide the connections. End-to-end and gradient-based learning are utilized to optimize both the model structure weights and parameters to fit the fault signal and obtain a fully interpretable decision route. The transparency of model, generalization ability toward unseen working conditions, and robustness to noise attack are demonstrated through case study of rotating machinery, paving the way for future industrial applications.

The Fuse Platform: Integrating data from IoT and other Sensors into an Industrial Spatial Digital Twin

Abstract. Digital Twins as virtual representations of industrial assets are being used to assimilate varied sources of data for improved awareness and decision making in operations and process optimisation. This paper explores the integration of IoT sensors into a spatial digital twin called Fuse that Woodside Energy has been building for the assets it operates. We describe the Fuse platform and its knowledge graph data core that is used to organise and inter-relate data for presentation within 3D visualisations, domain-specific contexts and immersive augmented reality presentations. The key contribution here is the use of a knowledge graph to link diverse data sources so as to contextualise sensor data for actionable insights. One area of significant innovation has been the development and use of new Internet of Things (IoT) devices which have been enabled by advances in sensor technology, connectivity, and cloud computing. These new tailored data sources are complimenting existing plant and resource planning data for improved asset monitoring, predictive maintenance and process automation.

Robotic assembly – mobile platform for construction field

Construction sites often involve the manual transportation of heavy objects, posing risks to workers’ safety and efficiency. In this paper, it was proposed a design, development, and evaluation of a service robot specifically created for carrying heavy objects in the construction field. The robot is equipped with robust locomotion capabilities, intelligent perception systems, and efficient control mechanisms to navigate through complex environments and handle various types of loads. It was presented the technical design of the robot, including its hardware components, sensing modalities, and software architecture. Furthermore, it was discussed the experimental validation of the robot’s performance in real-world construction scenarios, highlighting its effectiveness in improving productivity, reducing labor costs, and enhancing workplace safety. Through this research, the aim is to demonstrate the potential of service robots as valuable assets in the construction industry, paving the way for future advancements in robotic assistance technologies.

An interpretable hybrid framework combining convolution latent vectors with transformer based attention mechanism for rolling element fault detection and classification

Failure of industrial assets can cause financial, operational and safety hazards across different industries. Monitoring their condition is crucial for successful and smooth operations. The colossal volume of sensory data generated and acquired throughout industrial operations supports real-time condition monitoring of these assets. Leveraging digital technologies to analyze acquired data creates an ideal environment for applying advanced data-driven machine learning techniques, such as convolutional neural networks (CNNs) and vision transformer (ViT) to detect faults and classify, enabling accurate prediction and timely maintenance of industrial assets. In this paper, we present a novel hybrid framework based on the local feature extraction ability of CNN with comprehensive understanding of transformer within a global context. The proposed method leverages the complex weight-sharing properties of CNNs and ability of transformers to understand the larger context of spatial relationships in large-scale patterns, making it applicable to datasets of varying sizes. Preprocessing methods such as data augmentation are used to train the model on the Case Western Reserve University (CWRU) dataset in order to increase generalization through computational efficiency. An average fault classification accuracy of 99.62% is accomplished over all three fault classes with an average time-to-fault detection of 38.4 ms. MFPT fault dataset is used to further validate the method with an accuracy of 99.17% for outer race and 99.26% for inner race. Moreover, the proposed framework can be modified to accommodate alternative convolutional models.

Optimizing Organizational Efficiency: Strategic Requirement of Modern Industrial Asset Level Management

Asset Hierarchy Management (AHM) is not just a theoretical concept but a practical tool for optimizing operations in modern businesses, particularly in sectors with intricate infrastructures such as manufacturing, utilities, and healthcare. This research paper explores the significance of AHM in standardizing and enhancing organizational efficiency. It traces AHM's evolution, delineates its benefits, and provides strategies for implementation. By emphasizing the tangible benefits of AHM, the paper aims to instill optimism in the audience about its potential impact. The paper outlines prerequisites and steps for developing a robust asset hierarchy, stressing transparent governance and integration with technology solutions like Enterprise Asset Management (EAM) software. Furthermore, it presents a detailed framework for implementing AHM in manufacturing plants, highlighting the importance of defining clear asset categories, hierarchical levels, and relationships. By adhering to best practices and leveraging advanced tools, organizations can effectively manage assets, drive operational excellence, and achieve sustainable growth.

Evaluating the Impact of Hotel Classification on Pool Water Consumption: A Case Study from Costa Brava (Spain)

Swimming pools are key assets in the hotel industry. With climate change and water stress, more sustainable pools are needed in tourist areas. The study examines the relationship between hotel categories and the consumption of water in swimming pools in a Mediterranean coastal region facing water scarcity. The study focuses on the Costa Brava, with a focus on Lloret de Mar, a popular tourist destination. The research employs a combination of data analysis and the utilisation of evaporation models in order to estimate the consumption of water by swimming pools. The findings indicate that hotels in the higher categories, particularly those with three or four stars, contribute a notable proportion of the total water consumption due to their larger pool sizes and higher guest numbers. The study underscores the necessity for the implementation of sustainable water management strategies, particularly in the context of climate change. It recommends the utilisation of pool water-saving technologies as potential solutions. Furthermore, the paper highlights the broader environmental impact of tourism infrastructure on water resources and suggests policy measures to mitigate these effects. The research aligns with global sustainability goals such as the European Green Deal and the 2030 Agenda.

Operational status effect on the seismic risk assessment of oil refineries

The operational status of an oil refinery (type and scale of operations that take place at any time instance) largely determines the amount of fuel produced, circulated within the facility, and stored in tanks. This status is affected by seasonality, periods of peak or low demand, as well as periods of routine maintenance. However, it is an aspect that is typically neglected even though it stands out among the factors that determine the seismic performance of several critical industrial assets, such as the storage tanks, as well as the consequences of any potential failure. An open-source refinery testbed is employed herein to demonstrate the effect of the refinery's operational status on the seismic risk estimates. Alternative realistic operational scenarios are developed following typical industry practices and are arranged over a time period between two refinery major maintenance shutdown events. The most probable damage state is selected for each asset to identify the most vulnerable ones. Based on the type and importance of the impacted assets, the potential consequences are determined at the facility level. Resulting estimates are very different if an earthquake strikes during a regular/high/low-demand period, or a maintenance period. The framework can be utilized to identify the locations within the refinery that may trigger cascading failures and secondary damages, should their assets be damaged by a seismic event. The outcomes can be exploited by stakeholders, risk engineers, and emergency action planners for developing customized and businesslike procedures to enhance the seismic resilience of the facility.

BibMon: An open source Python package for process monitoring, soft sensing, and fault diagnosis

This paper introduces BibMon, a Python package that provides predictive models for data-driven fault detection and diagnosis, soft sensing, and process condition monitoring. Key features include regression and reconstruction models, preprocessing pipelines, alarms, and visualization through control charts and diagnostic maps. BibMon also includes real and simulated datasets for benchmarking, comparative performance analysis of different models, and hyperparameter tuning. The package is designed to be highly extensible, allowing for easy integration of new models and methodologies through its object-oriented implementation. Currently, BibMon is in production at Petrobras, a major player in the energy industry, monitoring numerous industrial assets and enabling real-time detection and diagnosis of equipment and process faults. The software is open source and available at: https://github.com/petrobras/bibmon.

Estimating Direct and Indirect Damage to Industrial Facilities from Coastal Flooding: Case of Ulsan, Korea

As climate change accelerates, the damage caused by natural disasters to logistics and industrial facilities near coastal areas is expected to increase. This study estimates the economic damage caused by coastal inundation in Ulsan Metropolitan City in detail, based on the frequency of inundation and building types. An object-based analysis is conducted to calculate the damage by building type, and the results show that the ripple effect of the disaster accounted for the majority of the total damage. Ulsan's major industrial clusters are concentrated along the coastline, with factories accounting for 92% of the total direct damage. The results of an input-output analysis based on industry linkage tables showed that the indirect damages were 22.1, 8.6, and 3.8 times greater than the direct damages for petrochemicals, automobiles, and shipbuilding, respectively, which represent the main industries in Ulsan. The automobile industry has a large impact on employment, with ripple effects on both directly related industries and other industries. Unlike residential assets, industrial assets exhibit different damage characteristics, depending on whether coastal inundation affects a specific industry or the entire sector. Coastal areas are densely populated with multiple industries. Hence, the ripple effects on assets and employment are significant. Therefore, it is important to accurately identify industries that are highly interdependent with others in the development of effective disaster preparedness and recovery policies. The results of this study can be used as a basis for cost–benefit analyses of climate change disaster measures and policies in coastal cities.

Research on the influence of the digital economy on the development of China’s small household appliance industry and enhancement countermeasures

Purpose The implementation of the digital economy has had a considerable influence on the manufacturing industry, and this paper aims to address the important issues of how to capture the opportunities presented by digital innovation and promote the transformation and upgrading of the manufacturing industry, as well as the improvement of quality and efficiency. Design/methodology/approach Using panel data from 30 Chinese provinces and cities between 2010 and 2021, this study establishes the panel vector autoregression (PVAR) model and uses impulse response function analysis to evaluate the influence of the digital economy on the high-quality transformation and upgrading of China's small home appliance industry across five dimensions under the digital economy. Findings The development of digital infrastructure has not demonstrated a noteworthy capacity for advancing the transformation and upgrading of the small home appliance industry. Furthermore, digital industrialization has exerted a minimal restraining influence on this process. Nevertheless, digital governance has consistently exhibited a substantial impact on facilitating the transformation and upgrading of the small home appliance industry. While both industrial digitization and digital innovation hold significant potential for promoting the transformation and upgrading of the small home appliance industry, their sustainability remains limited. Practical implications The organization should logically join independent innovation and open innovation, construct an industrial ecosystem for the profound convergence of the digital economy and compact household appliances, use digital-wise science and technology to empower the establishment of brand effects, strengthen the portrayal of the digital standard framework for the intelligent compact household appliance industry, advance the development of a public stage for computerized administrations in the compact household appliance industry and develop a strategy ecosystem for computerized assets in the compact household appliance industry. Originality/value This study offers systematic evidence of the relationship between the digital economy and the development of the small home appliance industry. The results of this research contribute to the literature on the impact of the digital economy on the manufacturing sector and provide a logical explanation for the transformation and upgrading of the small home appliance industry within the context of the digital economy.

Inovasi Pengembangan Film Dokumenter Sinematografi Sebagai Upaya Akselerasi Aset Digital Marketing Industri Tape Desa Banjarsari

The development of innovations in the production of cinematographic documentaries aims to accelerate digital marketing assets for the tape industry in Banjarsari Village. Through a methodology that includes analyzing digital marketing needs, designing documentary concepts, and evaluating the effectiveness of marketing campaigns, the documentary is designed to showcase the tape-making process in depth and engaging, as well as promote local cultural values. A significant increase in brand awareness and consumer interest in tape products from Banjarsari Village was revealed from the results of this activity. Evaluations conducted through in-depth surveys and interviews confirm the effectiveness of this approach in supporting digital marketing strategies and expanding market reach. The innovations applied in this cinematographic documentary not only increase consumer interest but also play an important role in strengthening marketing strategies and expanding market reach through an engaging visual approach. Thus, an important contribution is made to the development of innovative digital marketing assets for the rural tape industry, demonstrating that this approach is effective in supporting digital marketing strategies as well as expanding the market and increasing brand awareness of local products.

Deep Koopman Operator-based degradation modelling

Developing reliable health indicators for industrial assets is essential for accurate condition monitoring, fault detection, and predicting the remaining useful lifetime. However, constructing such indicators is challenging, especially given the increasing complexity of industrial systems and the not well-understood degradation dynamics. Previously proposed autoencoder-based methods for unsupervised health indicator construction faced the difficulty of constraining the latent representation over the system’s lifetime to obtain trendable and prognosable health indicators. Koopman operator theory provides a natural solution to this challenge. In this work, we first demonstrate the successful application of the Deep Koopman Operator approach for learning the dynamics of industrial systems. This results in a latent representation that provides sufficient information for estimating the remaining useful life of the asset. Secondly, we propose a novel Koopman-Inspired Degradation Model for modelling the degradation of dynamical systems with control. The Koopman-based algorithms demonstrate superior or comparable performance with autoencoder-based approaches in predicting the remaining useful life of assets such as CNC milling machine cutters and Li-ion batteries.