Fuzzy analytical hierarchy process for the selection of maintenance policies within petroleum industry

The selection of the maintenance policies is considered to be a complex matter at the strategic level and a trade-off between the criteria that should be considered is required to achieve the optimum maintenance selection. The purpose of this study is to develop a Fuzzy-Analytic Hierarchy Process multi criteria decision making model for the selection of maintenance policies within the petroleum industry. The model enables practitioners to decompose the structure of the hierarchy which assists in identifying the main criteria, sub-criteria and alternatives that impact on the selection of maintenance activities. The proposed AHP model is validated and in addition a comparison between classic and fuzzy analytic hierarchy process is conducted in terms of different derivation methods. Moreover, a sensitivity analysis is performed to validate the response of each derivation method at different inconsistency ratio which proved that the proposed model can be considered as the most accurate presentation of the criteria, sub-criteria and alternatives that should be used to decide upon the strategic maintenance policies within petroleum industry

Knowledge-based support in Non-Destructive Testing for health monitoring of aircraft structures

Maintenance manuals include general methods and procedures for industrial maintenance and they contain information about principles of maintenance methods. Particularly, Non-Destructive Testing (NDT) methods are important for the detection of aeronautical defects and they can be used for various kinds of material and in different environments. Conventional non-destructive evaluation inspections are done at periodic maintenance checks. Usually, the list of tools used in a maintenance program is simply located in the introduction of manuals, without any precision as regards to their characteristics, except for a short description of the manufacturer and tasks in which they are employed. Improving the identification concepts of the maintenance tools is needed to manage the set of equipments and establish a system of equivalence: it is necessary to have a consistent maintenance conceptualization, flexible enough to fit all current equipment, but also all those likely to be added/used in the future. Our contribution is related to the formal specification of the system of functional equivalences that can facilitate the maintenance activities with means to determine whether a tool can be substituted for another by observing their key parameters in the identified characteristics. Reasoning mechanisms of conceptual graphs constitute the baseline elements to measure the fit or unfit between an equipment model and a maintenance activity model. Graph operations are used for processing answers to a query and this graph-based approach to the search method is in-line with the logical view of information retrieval. The methodology described supports knowledge formalization and capitalization of experienced NDT practitioners. As a result, it enables the selection of a NDT technique and outlines its capabilities with acceptable alternatives

Asset management of energy company based on risk-oriented strategy

Repairs and maintenance of energy assets can be carried out in a variety of ways, which are usually based on indicators of reliability and efficiency. Due to the transition to a digital energy paradigm and implementation of intelligent diagnostic tools for technical condition of the equipment, it is advisable to carry out asset management with additional tools to consider operational and economic risks, as well as to predict the integrated efficiency of energy objects. This paper presents an overview of progressive strategies for energy asset management currently used in global practice. The analysis of approaches to asset management developed by one of the largest Russian generating and grid utilities is carried out. The authors developed several methodological recommendations for the identification of priority objects for technical maintenance and repair, ranked based on the type of equipment, risk of failure, predictiveness of defects, the undersupply of energy in emergency situations, types and cost of remediation and reputation losses of the energy business. Proposals for energy companies to implement a risk-based assets management strategy in units operating energy facilities are formulated. © 2020 WIT Press.The work was supported by Act 211 of Government of the Russian Federation, contract No. 02.A03.21.0006

A survey of AI in operations management from 2005 to 2009

Purpose: the use of AI for operations management, with its ability to evolve solutions, handle uncertainty and perform optimisation continues to be a major field of research. The growing body of publications over the last two decades means that it can be difficult to keep track of what has been done previously, what has worked, and what really needs to be addressed. Hence this paper presents a survey of the use of AI in operations management aimed at presenting the key research themes, trends and directions of research. Design/methodology/approach: the paper builds upon our previous survey of this field which was carried out for the ten-year period 1995-2004. Like the previous survey, it uses Elsevier’s Science Direct database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus, the application categories adopted are: design; scheduling; process planning and control; and quality, maintenance and fault diagnosis. Research on utilising neural networks, case-based reasoning (CBR), fuzzy logic (FL), knowledge-Based systems (KBS), data mining, and hybrid AI in the four application areas are identified. Findings: the survey categorises over 1,400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: the trends for design and scheduling show a dramatic increase in the use of genetic algorithms since 2003 that reflect recognition of their success in these areas; there is a significant decline in research on use of KBS, reflecting their transition into practice; there is an increasing trend in the use of FL in quality, maintenance and fault diagnosis; and there are surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research. Design/methodology/approach: the paper builds upon our previous survey of this field which was carried out for the 10 year period 1995 to 2004 (Kobbacy et al. 2007). Like the previous survey, it uses the Elsevier’s ScienceDirect database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus the application categories adopted are: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Research on utilising neural networks, case based reasoning, fuzzy logic, knowledge based systems, data mining, and hybrid AI in the four application areas are identified. Findings: The survey categorises over 1400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: (a) The trends for Design and Scheduling show a dramatic increase in the use of GAs since 2003-04 that reflect recognition of their success in these areas, (b) A significant decline in research on use of KBS, reflecting their transition into practice, (c) an increasing trend in the use of fuzzy logic in Quality, Maintenance and Fault Diagnosis, (d) surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research. Originality/value: This is the largest and most comprehensive study to classify research on the use of AI in operations management to date. The survey and trends identified provide a useful reference point and directions for future research

An Overview of Maintenance Management Strategies for Corroded Steel Structures in Extreme Marine Environments

Maintenance is playing an important role in integrity management of marine assets such as ship structures, offshore renewable energy platforms and subsea oil and gas facilities. The service life of marine assets is heavily influenced by the involvement of numerous material degradation processes (such as fatigue cracking, corrosion and pitting) as well as environmental stresses that vary with geographic locations and climatic factors. The composition of seawater constituents (e.g. dissolved oxygen, salinity, temperature content, etc.) is one of the major influencing factors in degradation of marine assets. Improving the efficiency and effectiveness of maintenance management strategies can have a significant impact on operational availability and reliability of marine assets. Many research studies have been conducted over the past few decades to predict the degradation behaviour of marine structures operating under different environmental conditions. The utilisation of structural degradation data – particularly on marine corrosion – can be very useful in developing a reliable, risk-free and cost-effective maintenance strategy. This paper presents an overview of the state-of-the-art and future trends in asset maintenance management strategies applied to corroded steel structures in extreme marine environments. The corrosion prediction models as well as industry best practices on maintenance of marine steel structures are extensively reviewed and analysed. Furthermore, some applications of advanced technologies such as computerized maintenance management system (CMMS), artificial intelligence (AI) and Bayesian network (BN) are discussed. Our review reveals that there are significant variations in corrosion behaviour of marine steel structures and their industrial maintenance practices from one climatic condition to another. This has been found to be largely attributed to variation in seawater composition/characteristics and their complex mutual relationships

Penentuan Kebijakam Perawatan Mesin Menggunakan Metode Reliability Centered Maintenance (RCM) II di Departemen Produksi pada Perusahaan Karoseri

Untuk memperoleh produk yang berkualitas harus didukung dengan mesin yang mempunyai performansi terbaik. Apabila salah satu mesin mengalami kerusakan, dapat mengakibatkan terganggunya proses produksi. Menurut keterangan salah satu karyawan awalnya perusahaan sudah memiliki jadwal preventive untuk mesin dan fasilitas pendukung proses produksi. Namun pada 4 bulan terakhir tindakan preventive maintenance tidak dilaksanakan karena tidak adanya karyawan pada bagian maintenance. Ketiadaan resource untuk maintenance mengakibatkan hilangnya data terkait penjadwalan maintenance. Sehingga untuk saat ini, karyawan maintenance yang baru hanya melaksanakan corrective maintenance. Berdasarkan latar belakang, peneliti melakukan implementasi perancangan aktivitas pemeliharaan dengan menggunakan metode Reliability Centered Maintenance II (RCM II). RCM II dapat menghasilkan maintenance task yang tepat. Melalui RCM II Information Worksheet diketahui fungsi, kegagalan fungsi, penyebab kegagalan fungsi, serta efek yang ditimbulkan dari kegagalan mesin. Kegagalan fungsi yang sering terjadi pada mesin yang ada di departemen produksi merupakan kegagalan fungsi akibat pengaruh dari usia mesin, dan kegagalan karena kerusakan fungsional masing-masing mesin. Kemudian didapatkan maintenance task yang tepat melalui RCM II Decision Worksheet. Ada 5 jenis maintenance task yang terpilih, yaitu Maintenance task tersebut adalah Scheduled Discard Task, Scheduled Restoration Task, On Condition Task, Finding Failure Task. dan No Schedule Maintenance Task. Kemudian ditentukan juga interval untuk masing-masing maintenance task, kemudian diplotkan pada kalender perawatan dalam periode 1 tahun. =================================================================== To obtain a product with a good quality should be supported by the machine with the best performance.If one of the machine is damaged, it can lead to disruption of the production process. According to an employee of, the company already has a schedule of preventive maintenance for supporting facilities and machines in production process. In the last 4 months of preventive maintexnance cannot be done because there are no available employee in the maintenance department. Loss of maintenance scheduling maintenance data. Therefore, the new maintenance employees only carry out corrective maintenance. Based on the background, researchers will undertake the design implementation of maintenance activities by using Reliability Centered Maintenance II (RCM II). Designing RCM II resulting in an appropriate maintenance tasks Through the RCM II Information Worksheet known functionality, malfunction, the cause of malfunction, and effects resulting from machine failure. Functional failures that often occur in existing machines is a failure of function due to the influence of machine age, and failure due to malfunction of each machine. Then, get the proper maintenance task through RCM II Decision Worksheet. There are 5 types of maintenance tasks selected, namely Maintenance task is Scheduled Discard Task, Scheduled Restoration Task, On Condition Task, Finding Failure Task. And No Schedule Maintenance Task. Then also determined the interval for each maintenance task, and plotted the interval on the calendar of maintenance within a period of 1 year

Multi-criteria decision making support tools for maintenance of marine machinery systems

PhD ThesisFor ship systems to remain reliable and safe they must be effectively maintained through a sound maintenance management system. The three major elements of maintenance management systems are; risk assessment, maintenance strategy selection and maintenance task interval determination. The implementation of these elements will generally determine the level of ship system safety and reliability. Reliability Centred Maintenance (RCM) is one method that can be used to optimise maintenance management systems. However the tools used within the framework of the RCM methodology have limitations which may compromise the efficiency of RCM in achieving the desired results. This research presents the development of tools to support the RCM methodology and improve its effectiveness in marine maintenance system applications. Each of the three elements of the maintenance management system has been considered in turn. With regard to risk assessment, two Multi-Criteria Decision Making techniques (MCDM); Vlsekriterijumska Optimizacija Ikompromisno Resenje, meaning: Multi-criteria Optimization and Compromise Solution (VIKOR) and Compromise Programming (CP) have been integrated into Failure Mode and Effects Analysis (FMEA) along with a novel averaging technique which allows the use of incomplete or imprecise failure data. Three hybrid MCDM techniques have then been compared for maintenance strategy selection; an integrated Delphi-Analytical Hierarchy Process (AHP) methodology, an integrated Delphi-AHP-PROMETHEE (Preference Ranking Organisation METHod for Enrichment Evaluation) methodology and an integrated Delphi-AHP-TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) methodology. Maintenance task interval determination has been implemented using a MCDM framework integrating a delay time model to determine the optimum inspection interval and using the age replacement model for the scheduled replacement tasks. A case study based on a marine Diesel engine has been developed with input from experts in the field to demonstrate the effectiveness of the proposed methodologies.Tertiary Education Trust Fund (TETFUND), a scholarship body of the Federal Republic of Nigeria for providing the fund for this research. My gratitude also goes to Federal University of Petroleum Resource, Effurun, Nigeria for giving me the opportunity to be a beneficiary of the scholarship

Let’s augment the future together!:Augmented reality troubleshooting support for IT/OT rolling stock failures

The railway industry is moving to a socio-technological system that relies on computer-controlled and human-machine interfaces. Opportunities arise for creating new services and commercial business cases by using technological innovations and traffic management systems. The convergence of Information Technology (IT) with Operational Technology (OT) is critical for cost-effective and reliable railway operations. However, this convergence introduces complexities, leading to more intricate rolling stock system failures. Hence, operators necessitate assistance in their troubleshooting and maintenance strategy to simplify the decision-making and action-taking processes. Augmented Reality (AR) emerges as a pivotal tool for troubleshooting within this context. AR enhances the operator’s ability to visualize, contextualize, and understand complex data by overlaying real-time and virtual information onto physical objects. AR supports the identification of IT/OT rolling stock system failures, offers troubleshooting directions, and streamlines maintenance procedures, ultimately enhancing decision-making and action-taking processes. This thesis investigates how AR can support operators in navigating troubleshooting and maintenance challenges posed by IT/OT rolling stock system failures in the railway industry

Establishment of a novel predictive reliability assessment strategy for ship machinery

There is no doubt that recent years, maritime industry is moving forward to novel and sophisticated inspection and maintenance practices. Nowadays maintenance is encountered as an operational method, which can be employed both as a profit generating process and a cost reduction budget centre through an enhanced Operation and Maintenance (O&M) strategy. In the first place, a flexible framework to be applicable on complex system level of machinery can be introduced towards ship maintenance scheduling of systems, subsystems and components.;This holistic inspection and maintenance notion should be implemented by integrating different strategies, methodologies, technologies and tools, suitably selected by fulfilling the requirements of the selected ship systems. In this thesis, an innovative maintenance strategy for ship machinery is proposed, namely the Probabilistic Machinery Reliability Assessment (PMRA) strategy focusing towards the reliability and safety enhancement of main systems, subsystems and maintainable units and components.;In this respect, the combination of a data mining method (k-means), the manufacturer safety aspects, the dynamic state modelling (Markov Chains), the probabilistic predictive reliability assessment (Bayesian Belief Networks) and the qualitative decision making (Failure Modes and Effects Analysis) is employed encompassing the benefits of qualitative and quantitative reliability assessment. PMRA has been clearly demonstrated in two case studies applied on offshore platform oil and gas and selected ship machinery.;The results are used to identify the most unreliability systems, subsystems and components, while advising suitable practical inspection and maintenance activities. The proposed PMRA strategy is also tested in a flexible sensitivity analysis scheme.There is no doubt that recent years, maritime industry is moving forward to novel and sophisticated inspection and maintenance practices. Nowadays maintenance is encountered as an operational method, which can be employed both as a profit generating process and a cost reduction budget centre through an enhanced Operation and Maintenance (O&M) strategy. In the first place, a flexible framework to be applicable on complex system level of machinery can be introduced towards ship maintenance scheduling of systems, subsystems and components.;This holistic inspection and maintenance notion should be implemented by integrating different strategies, methodologies, technologies and tools, suitably selected by fulfilling the requirements of the selected ship systems. In this thesis, an innovative maintenance strategy for ship machinery is proposed, namely the Probabilistic Machinery Reliability Assessment (PMRA) strategy focusing towards the reliability and safety enhancement of main systems, subsystems and maintainable units and components.;In this respect, the combination of a data mining method (k-means), the manufacturer safety aspects, the dynamic state modelling (Markov Chains), the probabilistic predictive reliability assessment (Bayesian Belief Networks) and the qualitative decision making (Failure Modes and Effects Analysis) is employed encompassing the benefits of qualitative and quantitative reliability assessment. PMRA has been clearly demonstrated in two case studies applied on offshore platform oil and gas and selected ship machinery.;The results are used to identify the most unreliability systems, subsystems and components, while advising suitable practical inspection and maintenance activities. The proposed PMRA strategy is also tested in a flexible sensitivity analysis scheme

Proposta de um modelo multicritério para determinação da criticidade na gestão da manutenção industrial

This work aims to propose a model for determining the criticality in industrial processes, using the multi-criteria analysis. Initially it is proposed a priority rating of the most critical elements of an industrial system and later, an ordering of the elements within their respective classes of criticality, thus obtaining the criticality of each. The model is based on outranking methods ELECTRE TRI and PROMETHEE II. An application of the model is carried out in this work, where the criteria are considered: security; environment; quality; operational impact; economic impacts; mean time between failures and mean time to repair. As a result, are presented comparisons of the proposed model with the traditional methods of determination of criticality, trying to observe subjective aspects and objectives jointly in order to provide a clear and systemic view of the problems implicit in determining the criticality in industrial systems. It was concluded that the use of multi-criteria analysis to determine criticality enables greater depth in the assessment and is a tool that helps in the management of maintenance and increased reliability for production systems.Esse trabalho tem como objetivo propor um modelo para determinação da criticidade em processos industriais, utilizando a análise multicritério. Inicialmente é proposta uma classificação por prioridade dos elementos mais críticos de um sistema industrial e posteriormente, uma ordenação desses elementos dentro das suas respectivas classes de criticidade, obtendo com isso a criticidade de cada um. O modelo baseia-se nos métodos de sobreclassificação ELECTRE TRI e PROMETHEE II. Uma aplicação do modelo é realizada nesse trabalho, onde são considerados os critérios: segurança; meio-ambiente; qualidade; impacto operacional; impactos econômicos; tempo médio entre falhas e tempo médio de reparo. Como resultado, são apresentadas comparações do modelo proposto com os métodos tradicionais de determinação de criticidade, procurando observar aspectos subjetivos e objetivos de forma conjunta, a fim de fornecer uma visão clara e sistêmica sobre os problemas implícitos na determinação da criticidade em sistemas industriais. É possível concluir que o uso da análise multicritério na determinação da criticidade possibilita maior profundidade na avaliação e constitui uma ferramenta que auxilia na gestão da manutenção e no aumento da confiabilidade para os sistemas produtivos