scholarly journals PrimaVera: Synergising Predictive Maintenance

2020 ◽  
Vol 10 (23) ◽  
pp. 8348
Author(s):  
Bram Ton ◽  
Rob Basten ◽  
John Bolte ◽  
Jan Braaksma ◽  
Alessandro Di Bucchianico ◽  
...  

The full potential of predictive maintenance has not yet been utilised. Current solutions focus on individual steps of the predictive maintenance cycle and only work for very specific settings. The overarching challenge of predictive maintenance is to leverage these individual building blocks to obtain a framework that supports optimal maintenance and asset management. The PrimaVera project has identified four obstacles to tackle in order to utilise predictive maintenance at its full potential: lack of orchestration and automation of the predictive maintenance workflow, inaccurate or incomplete data and the role of human and organisational factors in data-driven decision support tools. Furthermore, an intuitive generic applicable predictive maintenance process model is presented in this paper to provide a structured way of deploying predictive maintenance solutions.

Author(s):  
Luminita Ciocoiu ◽  
Carys E Siemieniuch ◽  
Ella-Mae Hubbard

Introduction of new technology (technology implementation) within an organisation can have wide reaching implications, beyond the effectiveness and efficiency savings that are typically the aim of such an endeavour. The ‘Health and Prognostic Assessment of Railway Assets for Predictive Maintenance’ project developed a prognostic tool, which aimed to support enhancement of the London underground’s remote condition monitoring system to support change from reactive and preventative to predictive maintenance, in order to increase effectiveness and efficiency and reduce lost customer hours. This paper investigates the organisational challenges associated with the introduction of such a tool. The paper describes the approach adopted to model the extant maintenance processes (focusing on role mapping) and associated organisational structures which revealed issues such as unclear processes, poor communication and data sharing links and problems with delineation of responsibility for decision making. It also describes the development of a new maintenance process model that incorporates the additional functionality of the new prognostic tool, taking in to account changes of roles, responsibilities, organisational processes and activities.


Author(s):  
Alejandro Reyes ◽  
Otto Huisman

Workflows are the fundamental building blocks of business processes in any organization today. These workflows have attributes and outputs that make up various Operational, Management and Supporting processes, which in turn produce a specific outcome in the form of business value. Risk Assessment and Direct Assessment are examples of such processes; they define the individual tasks integrity engineers should carry out. According to ISO 55000, achieving excellence in Asset Management requires clearly defined objectives, transparent and consistent decision making, as well as a long-term strategic view. Specifically, it recommends well-defined policies and procedures (processes) to bring about performance and cost improvements, improved risk management, business growth and enhanced stakeholder confidence through compliance and improved reputation. In reality, such processes are interpreted differently all over the world, and the workflows that make up these processes are often defined by individual engineers and experts. An excellent example of this is Risk Assessment, where significant local variations in data sources, threat sources and other data elements, require the business to tailor its activities and models used. Successful risk management is about enabling transparent decision-making through clearly defined process-steps, but in practice it requires maintaining a degree of flexibility to tailor the process to the specific organizational needs. In this paper, we introduce common building blocks that have been identified to make up a Risk Assessment process and further examine how these blocks can be connected to fulfill the needs of multiple stakeholders, including data administrators, integrity engineers and regulators. Moving from a broader Business Process view to a more focused Integrity Management view, this paper will demonstrate how to formalize Risk Assessment processes by describing the activities, steps and deliverables of each using Business Process Model and Notation (BPMN) as the standard modeling technique and extending it with an integrity-specific notation we have called Integrity Modelling Language or IML. It is shown that flexible modelling of integrity processes based on existing standards and best practices is possible within a structured approach; one which guides users and provides a transparent and auditable process inside the organization and beyond, based on commonalities defined by best practice guidelines, such as ISO 55000.


2021 ◽  
Vol 2 (4) ◽  
pp. 246-255
Author(s):  
Karrupusamy P

Predictive maintenance is the way to improve asset management in every manufacturing industry. While handling advance costlier machinery in the industry, the predictive maintenance knowledge will be essential to protect the machinery before gets degradation performance. Recently, the emergence of business in manufacturing industry deals with good systems, regular intervals maintenance process, predictive maintenance (PdM), machine learning (ML) approaches are extensively applied for handling the health standing of business instrumentation. Now the digital transformation towards I4.0, data techniques, processed management and communication networks; it’s doable to gather huge amounts of operational and processes conditions information generated type many items of kit and harvest information for creating an automatic fault detection and diagnosing with the aim to attenuate period of time and increase utilization rate of the parts and increase their remaining helpful lives. The predictive maintenance is inevitable for property good producing in I40. This paper aims to provide a comprehensive review of the recent advancements of metric capacity unit techniques wide applied to PdM for good producing in I4.0 by classifying the analysis consistent with metric capacity unit algorithms, ML class, machinery and instrumentation used device employed in information acquisition, classification of knowledge size and kind, and highlight the key contributions of the researchers and so offers pointers and foundation for additional analysis. In this research paper we constructed a Random Forest model to predict the failure of the various machine in manufacturing industry. It compares the prediction result with Decision Tree (DT) algorithm and proves its superiority in accuracy and precision.


2021 ◽  
Author(s):  
Xiangang Cao ◽  
Tianbo Xu ◽  
Youjun Zhao ◽  
Jiangbin Zhao ◽  
Yan Wang

Abstract In view of the problems of excessive maintenance and insufficient utilization of equipment service life caused by preventive maintenance of fully mechanized mining equipment with fixed cycle, a predictive maintenance method is proposed. Firstly, based on Weibull distribution function and evolution rules of equipment decay, the evolution model of equipment failure rate is established; Then, the single-objective decision-making models of equipment maintenance cost rate and maintenance downtime rate are established respectively. On this basis, the multi-objective predictive maintenance planning model of fully mechanized mining equipment with comprehensive cost and time factors is established, and the optimal predictive maintenance cycle planning sequence is obtained. Combined with the coal production continuation plan, this paper puts forward a method to determine the optimal maintenance time by making suitable choices between advance maintenance and delay maintenance. The result confirms the effectiveness and superiority of the proposed method.


2021 ◽  
Vol 11 (8) ◽  
pp. 3438
Author(s):  
Jorge Fernandes ◽  
João Reis ◽  
Nuno Melão ◽  
Leonor Teixeira ◽  
Marlene Amorim

This article addresses the evolution of Industry 4.0 (I4.0) in the automotive industry, exploring its contribution to a shift in the maintenance paradigm. To this end, we firstly present the concepts of predictive maintenance (PdM), condition-based maintenance (CBM), and their applications to increase awareness of why and how these concepts are revolutionizing the automotive industry. Then, we introduce the business process management (BPM) and business process model and notation (BPMN) methodologies, as well as their relationship with maintenance. Finally, we present the case study of the Renault Cacia, which is developing and implementing the concepts mentioned above.


2021 ◽  
Vol 6 (2) ◽  
pp. 18
Author(s):  
Alireza Sassani ◽  
Omar Smadi ◽  
Neal Hawkins

Pavement markings are essential elements of transportation infrastructure with critical impacts on safety and mobility. They provide road users with the necessary information to adjust driving behavior or make calculated decisions about commuting. The visibility of pavement markings for drivers can be the boundary between a safe trip and a disastrous accident. Consequently, transportation agencies at the local or national levels allocate sizeable budgets to upkeep the pavement markings under their jurisdiction. Infrastructure asset management systems (IAMS) are often biased toward high-capital-cost assets such as pavements and bridges, not providing structured asset management (AM) plans for low-cost assets such as pavement markings. However, recent advances in transportation asset management (TAM) have promoted an integrated approach involving the pavement marking management system (PMMS). A PMMS brings all data items and processes under a comprehensive AM plan and enables managing pavement markings more efficiently. Pavement marking operations depend on location, conditions, and AM policies, highly diversifying the pavement marking management practices among agencies and making it difficult to create a holistic image of the system. Most of the available resources for pavement marking management focus on practices instead of strategies. Therefore, there is a lack of comprehensive guidelines and model frameworks for developing PMMS. This study utilizes the existing body of knowledge to build a guideline for developing and implementing PMMS. First, by adapting the core AM concepts to pavement marking management, a model framework for PMMS is created, and the building blocks and elements of the framework are introduced. Then, the caveats and practical points in PMMS implementation are discussed based on the US transportation agencies’ experiences and the relevant literature. This guideline is aspired to facilitate PMMS development for the agencies and pave the way for future pavement marking management tools and databases.


2020 ◽  
Vol 3 (1) ◽  
pp. 428-436
Author(s):  
H. Alegre ◽  
R. Amaral ◽  
R. S. Brito ◽  
J. M. Baptista

Abstract Urban water supply, wastewater and storm water services (globally, water services) are essential to society. The lack of permanent, safe, and respondent services has inevitable consequences on public health and the well-being of communities, on the economy, and on the environment. Goal 6 of the Sustainable Development Goals (SDGs) recognizes this; failing to meet it necessarily affects the accomplishment of many of the other SDGs. Water services’ provision depends on expensive and long-lasting physical assets. Managing them strategically (e.g., according to the international standards on asset management, series ISO 55x and to the IWA recommendations on infrastructure asset management) is, therefore, fundamental for sustainable societies. Countries need to have sound public policies that enable asset management of water infrastructure. Portugal is a paradigmatic case. This paper elaborates on key government goals, on why asset management is important to meet them, and on key building blocks that a coherent public policy should consider in order to enable asset management of water infrastructure. It also presents how Portugal has been implementing this process, addressing the challenges that need to be overcome.


2021 ◽  
Author(s):  
Giorgio Ferrario ◽  
Salvatore Grimaldi

Abstract Capitalization of lessons learned on Asset Integrity Management during Front End Loading phases of a green field Project Development, by defining plan for implementation of a diagnostic digital tool for reducing downtime and introduce predictive maintenance during Operation. Eni developed a platform of Digital applications for enhanced Operations management by implementing an Integrated Asset Management (IAM) system. Advanced Analytics tool is part of it and is designed for monitoring, foreseeing and preventing production upsets and anomalies; the tool is set up by verification of areas of interest and criticalities, with identification of main equipment data sets and by the implementation and validation of predictive models. Starting from historical data, data scientists supported by experts develop algorithms capable of finding interdependencies between a set of input variables and an output variable (phenomenon to be predicted/monitored), thus detecting anomalies and criticalities. Main areas of benefit are envisaged on Production continuity, capable of predicting problems on static and rotating equipment and giving information on the most impacting variables on the incipient problems. The tool will support technicians to help them preventing failures and out-of-specs events which may cause loss of production or asset integrity issues, with the activation of predictive maintenance and the aim to strive a continuous monitoring and improvement of plant operational performances. An Energy Efficiency predictive model will also be set up, capable of forecasting the future energy performances of the asset through the prediction of the Stationary Combustion of Carbon Dioxide (CO2) emission index (t CO2/kbbl) and providing the list of the main influencing equipment and variables. The plan for implementation of the tool from the Early phases of development help the organization on prioritizing the implementation of Digital tools as part of the execution and realization of the Asset to be delivered to the Operational personnel, by easing the transition and avoiding subsequent retrofitting carrying brownfield works and additional costs. The implementation of Advanced Analytics tool has been embedded in a new green field initiative of a Development Project since Front End Loading phases, thus fostering digital implementation and minimizing deployment costs by including those as part of the Investment Proposal presented to Joint Venture Partners and Authorities.


Author(s):  
Jean Vincent Fonou-Dombeu ◽  
Magda Huisman

The ultimate goal of e-Governance is to reach the stage of seamless service delivery in one-stop e-Government. This raises the engineering issues of integration, reusability, maintenance, and interoperability of autonomous e-Government systems of government departments and agencies. Therefore, appropriate methodologies that consistently address the aforementioned engineering issues throughout clearly defined e-Government development phases are needed. This chapter provides the design and specification, of a framework that amalgamates features from maturity models, software engineering and Semantic Web domains for semantic-enabled development of e-Government systems. Firstly, the methods and techniques used for the planning, design, and implementation of e-Government systems worldwide are investigated; a critical analysis is carried out to identify their advantages and disadvantages, as well as their contribution towards addressing the aforementioned engineering issues. Secondly, the proposed framework is drawn and specified. Finally, support tools including a business process model, an alignment matrix of stages and phases of development, and a weighting matrix of the intensity of semantic activities at various phases of development is drawn and described.


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