A Study on Estimating the Next Failure Time of Compressor Equipment in an Offshore Plant

The offshore plant equipment usually has a long life cycle. During its O&M (Operation and Maintenance) phase, since the accidental occurrence of offshore plant equipment causes catastrophic damage, it is necessary to make more efforts for managing critical offshore equipment. Nowadays, due to the emerging ICTs (Information Communication Technologies), it is possible to send health monitoring information to administrator of an offshore plant, which leads to much concern on CBM (ConditionBased Maintenance). This study introduces three approaches for predicting the next failure time of offshore plant equipment (gas compressor) with case studies, which are based on finite state continuous time Markov model, linear regression method, and their hybrid model

A semantic-driven approach toward Industry 4.0

The current manufacturing trend is towards sustainability. Due to the degradation of natural resources and the accumulation of waste with an increase in the world population and in the consumption of goods, many countries attempt to switch from a linear to a circular economy. Accordingly, they legislate strict environmental regulations on resource consumption, so manufacturing companies require the adoption of the circular economy. Meanwhile, Industry 4.0 technologies facilitate efficient monitoring/forecast through big data and analytics, and thus, manufacturing companies can avoid potential risks and save their resources toward sustainable manufacturing. However, in spite of the requirements to achieve sustainable manufacturing toward the circular economy, companies are immature to exploit the advent of new technologies, even though research works have been very active on each application of the newly emphasizing technologies. For this reason, this dissertation is aiming at providing a comprehensive approach in the context of Industry 4.0 for manufacturing companies to achieve sustainable manufacturing. Guided by the technology aggregation issue, this dissertation provides a procedure and related methods consisting of three steps: 1) the comprehensive research in Industry 4.0 era, especially maintenance, 2) design of knowledge representation and 3) a decision-making methodology. Our method will be aligned with the strong commitment from different actors from all over the world for the validation and demonstration

A semantic-driven approach for Industry 4.0

Toward industry 4.0, modern manufacturing companies are aiming at building digital twins to manage physical assets, processes, people, and places. Since in this environment, massive amounts of data have been generated and collected, integration and management of various data sources is of paramount importance. In this context, cloud computing as a crucial part of Industry 4.0 facilitates distribution of computer resources without direct active management by users. Accordingly, an ontology enables efficient integration and management of data as a reference data model through representation of knowledge. Besides, data mining from massive data is very important to identify significant meaning of data, and to avoid unexpected errors through predictions from experiences described in data replica. This research addresses problems towards efficient integration and management of data for predictive maintenance

Heuristic algorithms for maximising the total profit of end-of-life computer remanufacturing

Recently the optimisation of end-of-life (EOL) computer remanufacturing has been highlighted since a big amount of used computers have been disposed of every year. Each part inspected after disassembling EOL computers can have various EOL options such as reuse, repair, reconditioning and so on. Depending on EOL options, recovered values and costs of parts will be different. Hence, in order to maximise the profit of remanufactured computers, it is important to develop the method as to how to decide the EOL options of computer parts. To this end, this study deals with a decision-making problem to select the best EOL option policy of the computer parts for maximising the total profit of computer remanufacturing considering its incurred costs and demand of remanufactured computers during multiple production periods. In particular, to maximise the total profit, the conditional repair option is newly proposed. To resolve the problem, a genetic search algorithm and an ant colony search algorithm have been developed. Computational experiments have carried out to evaluate the algorithms and the proposed conditional repair option

Ontology for Strategies and Predictive Maintenance models

As of today, to cope with traditional maintenance policies such as reactive and preventive maintenance, the manufacturing companies need the deployment of adaptive and responsive maintenance strategies. Meanwhile, the advent of Industry 4.0 leads the maintenance paradigm shift facilitated by the efficient monitoring of physical assets and forecasting of the potential risks. As the advanced maintenance policies benefit in terms of cost-efficiency, inventory management and reliability management, most of the manufacturing companies are trying to make their own advanced maintenance strategies and to elaborate on the development of an innovative platform for it. However, since advanced enabling technologies collect a huge amount of data from different data sources such as machine, component, document, process and so on, data federation should necessarily be achieved for further discussion, but manufacturing companies are immature to address this issue. H2020 EU project Z-BRE4K, i.e., Strategies and predictive maintenance models wrapped around physical systems for zero unexpected-breakdowns and increased operating life of factories, deploys semantic technologies to address this issue. This paper deals with the debate on how to efficiently federate various data formats with the support of the semantic technologies in the context of maintenance. In addition, it proposes a maintenance ontology validated and implemented with an actor from European industry. Copyright (C) 2020 The Authors

A Hybrid Machine Learning Approach for Predictive Maintenance in Smart Factories of the Future

Part 5: Industry 4.0 - Digital TwinInternational audienceAdvanced technologies based on Internet of Things (IOT) are blazing a trail to effective and efficient management of an overall plant. In this context, manufacturing companies require an innovative strategy to survive in a competitive business environment, utilizing those technologies. Guided by these requirements, the so-called predictive maintenance is of paramount importance and offers a significant potential for innovation to overcome the limitations of traditional maintenance policies. However, real shop-floors often have obstacles in providing insights to facilitate the effective management of assets in smart factories. Even if a significant amount of machine and process data is available, one of the common problems of these data is the lack of annotations describing the machine status or maintenance history. For this reason, companies have limited options to analyse manufacturing data, despite the capability of advanced machine learning techniques in supporting the identification of failure symptoms in order to optimize scheduling of maintenance operations. Moreover, each machine generates highly heterogeneous data, making it difficult to integrate all the information to provide data-driven decision support for predictive maintenance. Inspired by these challenges, this research provides a hybrid machine learning approach combining unsupervised learning and semi-supervised learning. The approach and result in this article are based on the development and implementation in a large collaborative EU-funded H2020 research project entitled BOOST 4.0 i.e. Big Data Value Spaces for COmpetitiveness of European COnnected Smart FacTories

A Study on Estimating the Next Failure Time of Compressor Equipment in an Offshore Plant

The offshore plant equipment usually has a long life cycle. During its O&M (Operation and Maintenance) phase, since the accidental occurrence of offshore plant equipment causes catastrophic damage, it is necessary to make more efforts for managing critical offshore equipment. Nowadays, due to the emerging ICTs (Information Communication Technologies), it is possible to send health monitoring information to administrator of an offshore plant, which leads to much concern on CBM (Condition-Based Maintenance). This study introduces three approaches for predicting the next failure time of offshore plant equipment (gas compressor) with case studies, which are based on finite state continuous time-Markov model, linear regression method, and their hybrid model

Predictive Maintenance Platform Based on Integrated Strategies for Increased Operating Life of Factories

Part 5: Industry 4.0 - Digital TwinInternational audienceProcess output and profitability of the operations are mainly determined by how the equipment is being used. The production planning, operations and machine maintenance influence the overall equipment effectiveness (OEE) of the machinery, resulting in more ‘good parts’ at the end of the day. The target of the predictive maintenance approaches in this respect is to increase efficiency and effectiveness by optimizing the way machines are being used and to decrease the costs of unplanned interventions for the customer. To this end, development of ad-hoc strategies and their seamless integration into predictive maintenance systems is envisaged to bring substantial advantages in terms of productivity and competitiveness enhancement for manufacturing systems, representing a leap towards the real implementation of the Industry 4.0 vision. Inspired by this challenge, the study provides an approach to develop a novel predictive maintenance platform capable of preventing unexpected-breakdowns based on integrated strategies for extending the operating life span of production systems. The approach and result in this article are based on the development and implementation in a large collaborative EU-funded H2020 research project entitled Z-Bre4k, i.e. Strategies and predictive maintenance models wrapped around physical systems for zero-unexpected-breakdowns and increased operating life of factories

A Hybrid Machine Learning Approach for Predictive Maintenance in Smart Factories of the Future

Advanced technologies based on Internet of Things (IOT) are blazing a trail to effective and efficient management of an overall plant. In this context, manufacturing companies require an innovative strategy to survive in a competitive business environment, utilizing those technologies. Guided by these requirements, the so-called predictive maintenance is of paramount importance and offers a significant potential for innovation to overcome the limitations of traditional maintenance policies. However, real shop-floors often have obstacles in providing insights to facilitate the effective management of assets in smart factories. Even if a significant amount of machine and process data is available, one of the common problems of these data is the lack of annotations describing the machine status or maintenance history. For this reason, companies have limited options to analyse manufacturing data, despite the capability of advanced machine learning techniques in supporting the identification of failure symptoms in order to optimize scheduling of maintenance operations. Moreover, each machine generates highly heterogeneous data, making it difficult to integrate all the information to provide data-driven decision support for predictive maintenance. Inspired by these challenges, this research provides a hybrid machine learning approach combining unsupervised learning and semi-supervised learning. The approach and result in this article are based on the development and implementation in a large collaborative EU-funded H2020 research project entitled BOOST 4.0 i.e. Big Data Value Spaces for COmpetitiveness of European COnnected Smart FacTories

Predictive Maintenance Platform Based on Integrated Strategies for Increased Operating Life of Factories

Process output and profitability of the operations are mainly determined by how the equipment is being used. The production planning, operations and machine maintenance influence the overall equipment effectiveness (OEE) of the machinery, resulting in more 'good parts' at the end of the day. The target of the predictive maintenance approaches in this respect is to increase efficiency and effectiveness by optimizing the way machines are being used and to decrease the costs of unplanned interventions for the customer. To this end, development of ad-hoc strategies and their seamless integration into predictive maintenance systems is envisaged to bring substantial advantages in terms of productivity and competitiveness enhancement for manufacturing systems, representing a leap towards the real implementation of the Industry 4.0 vision. Inspired by this challenge, the study provides an approach to develop a novel predictive maintenance platform capable of preventing unexpected-breakdowns based on integrated strategies for extending the operating life span of production systems. The approach and result in this article are based on the development and implementation in a large collaborative EU-funded H2020 research project entitled Z-Bre4k, i.e. Strategies and predictive maintenance models wrapped around physical systems for zero-unexpected-breakdowns and increased operating life of factories