scholarly journals A digital twins concept model for integrated maintenance: a case study for crane operation

2020 ◽  
Author(s):  
Janusz Szpytko ◽  
Yorlandys Salgado Duarte
Keyword(s):  
Decision Making ◽  
Production Systems ◽  
Container Terminal ◽  
Maintenance Schedule ◽  
Concept Model ◽  
Digital Twins ◽  
Maintenance Decision ◽  
Using Data ◽  
Operation Risk

Abstract The paper presents an Integrated Maintenance Decision Making Model (IMDMM) concept for cranes under operation especially into the container type terminals. The target is to improve cranes operational efficiency through minimizing the risk of the Gantry Cranes Inefficiency (GCI) results based on the implementation of the Digital Twins concept for maintenance purposes. The proposed model makes a joint transportation process and crane maintenance scheduling, relevant to assure more robust performances in stochastic environments, as well as to assess and optimize performances at different levels, from components and transport device to production systems (container terminal). The crane operation risk is estimated with a sequential Markov chain Monte Carlo simulation model and the optimization model behind of IMDMM is supported through the Particle Swarm Optimization algorithms because the objective function a non-linear stochastics problem with bounded constrains. The developed model allows the container terminal operators (management process) to obtain a maintenance schedule that minimizes the GCI (holistic indicator), as well as establishing the desired level of risk. The paper demonstrates the effectiveness of the proposed maintenance decision making concept model for cranes under operation using data from of a real container terminal (case study).

scholarly journals STANDARDISING MAINTENANCE JOBS TO IMPROVE GROUPING DECISION MAKING

2021 ◽  
Vol 1 ◽  
pp. 2701-2710
Author(s):  
Julie Krogh Agergaard ◽  
Kristoffer Vandrup Sigsgaard ◽  
Niels Henrik Mortensen ◽  
Jingrui Ge ◽  
Kasper Barslund Hansen ◽  
...  
Keyword(s):  
Decision Making ◽  
Tacit Knowledge ◽  
True Variance ◽  
Complex Decision ◽  
Maintenance Decision ◽  
Maintenance Activities

AbstractMaintenance decision making is an important part of managing the costs, effectiveness and risk of maintenance. One way to improve maintenance efficiency without affecting the risk picture is to group maintenance jobs. Literature includes many examples of algorithms for the grouping of maintenance activities. However, the data is not always available, and with increasing plant complexity comes increasingly complex decision requirements, making it difficult to leave the decision making up to algorithms.This paper suggests a framework for the standardisation of maintenance data as an aid for maintenance experts to make decisions on maintenance grouping. The standardisation improves the basis for decisions, giving an overview of true variance within the available data. The goal of the framework is to make it simpler to apply tacit knowledge and make right decisions.Applying the framework in a case study showed that groups can be identified and reconfigured and potential savings easily estimated when maintenance jobs are standardised. The case study enabled an estimated 7%-9% saved on the number of hours spent on the investigated jobs.

Mathematical-Modelling Simulation Applied to Help in the Decision-Making Process on Environmental Impact Assessment of Agriculture

2011 ◽  
pp. 199-233
Author(s):  
Maria Pessoa ◽  
Elizabeth Fernandes ◽  
Sonia Nascimento de Queiroz ◽  
Vera Ferracini ◽  
Marco Gomes ◽  
...  
Keyword(s):  
Decision Making ◽  
Environmental Impact ◽  
Mathematical Modelling ◽  
Impact Assessment ◽  
Environmental Impact Assessment ◽  
Production Systems ◽  
Decision Making Process ◽  
Aquifer Recharge ◽  
Guarani Aquifer

The present chapter provides a brief explanation on some aspects involved in the development of models and mathematical-modelling simulations, to show their benefits to the decision-making process in the environmental impact assessment of agriculture. Aspects concerning the agroecosystems were also presented toward the sustainability of Brazilian agricultural production systems. Some applications which have been developed in Brazil were pointed out, as well as a specific case study conducted at the Guarani aquifer recharge area located in Ribeirão Preto, São Paulo state, in order to show the influence of input data on the results provided by CMLS94 simulator.

The Impact of Using Data Warehouse on Manpower Employment Decision Support System

2011 ◽  
Vol 383-390 ◽  
pp. 4653-4659
Author(s):  
Amro F. Alasta ◽  
Muftah A. Enaba
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support System ◽  
Data Warehouse ◽  
Support System ◽  
Employment Decision ◽  
Use Of Data ◽  
Using Data ◽  
The Impact

Since the use of computers in business world, data collection has become one of the most important issues due to the available knowledge in the data; such data has been stored in database. Database system was developed which led to the evolvement of hierarchical and relational database followed by Standard Query Language (SQL). As data size increases, the need for more control and information retrieval increase. These increases lead to the development of data mining systems and data warehouses. This paper focuses on the use of data warehouse as a supporting tool in decision making. We to study the effectiveness of data warehouse techniques in the sense of time and flexibility in our case study (Manpower Employment). The study will conclude with a comparison of traditional relational database and the use of data warehouse. The fundamental role of data warehouse is to provide data for supporting decision-making process. Data in data warehouse environment is multidimensional data store. We can simply say that data warehouse is a process not a product, for assembling and managing data from various sources for the purpose of gaining a single detailed view of part or all an establishment. The data warehouse concept has changed the nature of decision support system, by adding new benefits for improving and expanding the scope, accuracy, and accessibility of data. The warehouse is the link between the application and raw data, which is scattered in separate database but now is unified. The objectives of this work are to study the impact of using data warehouse on Manpower Employment Decision Support System, in the sense as far as the data quality concern. We will focus on the benefits gained from using data warehouse, and why it is more powerful than the use of traditional databases in decision making. The case study will be the Libyan national manpower employment agency. The data warehouse will collect database scattered from different sources in Libya in order to compare the performance and time.

Mathematical-Modelling Simulation Applied to Help in the Decision-Making Process on Environmental Impact Assessment of Agriculture

2011 ◽  
pp. 946-979
Author(s):  
Maria Pessoa ◽  
Elizabeth Fernandes ◽  
Sonia Nascimento de Queiroz ◽  
Vera Ferracini ◽  
Marco Gomes ◽  
...  
Keyword(s):  
Decision Making ◽  
Environmental Impact ◽  
Mathematical Modelling ◽  
Impact Assessment ◽  
Environmental Impact Assessment ◽  
Production Systems ◽  
Decision Making Process ◽  
Aquifer Recharge ◽  
Guarani Aquifer

The present chapter provides a brief explanation on some aspects involved in the development of models and mathematical-modelling simulations, to show their benefits to the decision-making process in the environmental impact assessment of agriculture. Aspects concerning the agroecosystems were also presented toward the sustainability of Brazilian agricultural production systems. Some applications which have been developed in Brazil were pointed out, as well as a specific case study conducted at the Guarani aquifer recharge area located in Ribeirão Preto, São Paulo state, in order to show the influence of input data on the results provided by CMLS94 simulator.

Key Techniques in Intelligent Predictive Maintenance (IPdM) – A Framework of Intelligent Faults Diagnosis and Prognosis System (IFDaPS)

2014 ◽  
Vol 1039 ◽  
pp. 490-505 ◽  
Author(s):  
Ke Sheng Wang
Keyword(s):  
Decision Making ◽  
Frequency Domain ◽  
Wavelet Packet ◽  
Remaining Useful Life ◽  
Predictive Maintenance ◽  
Ann Model ◽  
Diagnosis And Prognosis ◽  
Faults Diagnosis ◽  
Maintenance Decision

Intelligent predictive maintenance (IPdM) is a maintenance strategy that makes maintenance decisions automatically and dynamically based on Artificial Intelligence and Data mining techniques through condition monitoring of machines, equipment and production processes. IPdM system consists of the following main modules: sensor and data acquisition, signal and data processing, feature extractions, maintenance decision-making, key performance indicators, maintenance scheduling optimization and feedback control and compensation. Among them, the most important part of IPdM is maintenance decision-making, which includes diagnostics and prognostics. This paper proposes a framework of intelligent faults diagnosis and prognosis system (IFDaPS) and discuss some key technologies for implement IPdM policy in manufacturing and industries. A case study focus on the vibration signals collected from the sensors mounted on a pressure blower for critical components monitoring. We decompose the pre-processed signals into several signals using Wavelet Packet Decomposition (WPD), and then the signals are transformed to frequency domain using Fast Fourier Transform (FFT). The features extracted from frequency domain are used to train Artificial Neural Network (ANN). Trained ANN model is able to identify the fault of the components and predict its Remaining Useful Life (RUL). The case study demonstrates how to implement the proposed framework and intelligent technologies for IPdM and the result indicates its higher efficiency and effectiveness comparing to traditional methods.

scholarly journals Decision-making approaches in process innovations: an explorative case study

2019 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Erik Flores-Garcia ◽  
Jessica Bruch ◽  
Magnus Wiktorsson ◽  
Mats Jackson
Keyword(s):  
Decision Making ◽  
Production Systems ◽  
Current Understanding ◽  
Research Approach ◽  
Manufacturing Companies ◽  
Content Type ◽  
Process Innovations ◽  
Combined Use ◽  
Selection Of

Purpose The purpose of this paper is to explore the selection of decision-making approaches at manufacturing companies when implementing process innovations. Design/methodology/approach This study reviews the current understanding of decision structuredness for determining a decision-making approach and conducts a case study based on an interactive research approach at a global manufacturer. Findings The findings show the correspondence of intuitive, normative and combined intuitive and normative decision-making approaches in relation to varying degrees of equivocality and analyzability. Accordingly, the conditions for determining a decision-making choice when implementing process innovations are revealed. Research limitations/implications This study contributes to increased understanding of the combined use of intuitive and normative decision making in production system design. Practical implications Empirical data are drawn from two projects in the heavy-vehicle industry. The study describes decisions, from start to finish, and the corresponding decision-making approaches when implementing process innovations. These findings are of value to staff responsible for the design of production systems. Originality/value Unlike prior conceptual studies, this study considers normative, intuitive and combined intuitive and normative decision making. In addition, this study extends the current understanding of decision structuredness and discloses the correspondence of decision-making approaches to varying degrees of equivocality and analyzability.

scholarly journals Data-Driven Digital Twins for Technical Building Services Operation in Factories: A Cooling Tower Case Study

2020 ◽  
Vol 4 (4) ◽  
pp. 97 ◽  
Author(s):  
Christine Blume ◽  
Stefan Blume ◽  
Sebastian Thiede ◽  
Christoph Herrmann
Keyword(s):  
Manufacturing Systems ◽  
Polynomial Regression ◽  
Production Systems ◽  
Selection Procedure ◽  
Cooling Capacity ◽  
Data Driven ◽  
Digital Twins ◽  
Operational Management ◽  
Whole Life Cycle

Cyber-physical production systems (CPPS) and digital twins (DT) with a data-driven core enable retrospective analyses of acquired data to achieve a pervasive system understanding and can further support prospective operational management in production systems. Cost pressure and environmental compliances sensitize facility operators for energy and resource efficiency within the whole life cycle while achieving reliability requirements. In manufacturing systems, technical building services (TBS) such as cooling towers (CT) are drivers of resource demands while they fulfil a vital mission to keep the production running. Data-driven approaches, such as data mining (DM), help to support operators in their daily business. Within this paper the development of a data-driven DT for TBS operation is presented and applied on an industrial CT case study located in Germany. It aims to improve system understanding and performance prediction as essentials for a successful operational management. The approach comprises seven consecutive steps in a broadly applicable workflow based on the CRISP-DM paradigm. Step by step, the workflow is explained including a tailored data pre-processing, transformation and aggregation as well as feature selection procedure. The graphical presentation of interim results in portfolio diagrams, heat maps and Sankey diagrams amongst others to enhance the intuitive understanding of the procedure. The comparative evaluation of selected DM algorithms confirms a high prediction accuracy for cooling capacity (R2 = 0.96) by using polynomial regression and electric power demand (R2 = 0.99) by linear regression. The results are evaluated graphically and the transfer into industrial practice is discussed conclusively.

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