Supporting group maintenance through prognostics-enhanced dynamic dependability prediction

Condition-based maintenance strategies adapt maintenance planning through the integration of online condition monitoring of assets. The accuracy and cost-effectiveness of these strategies can be improved by integrating prognostics predictions and grouping maintenance actions respectively. In complex industrial systems, however, effective condition-based maintenance is intricate. Such systems are comprised of repairable assets which can fail in different ways, with various effects, and typically governed by dynamics which include time-dependent and conditional events. In this context, system reliability prediction is complex and effective maintenance planning is virtually impossible prior to system deployment and hard even in the case of condition-based maintenance. Addressing these issues, this paper presents an online system maintenance method that takes into account the system dynamics. The method employs an online predictive diagnosis algorithm to distinguish between critical and non-critical assets. A prognostics-updated method for predicting the system health is then employed to yield well-informed, more accurate, condition-based suggestions for the maintenance of critical assets and for the group-based reactive repair of non-critical assets. The cost-effectiveness of the approach is discussed in a case study from the power industry

A Decision Support System for Ship Maintenance Capacity Planning

In this paper, the basic framework and algorithms of a decision support system are discussed, which enhance process and capacity planning at a large repair shop. The research is strongly motivated by experiences in a project carried out at a dockyard, which performs repair, overhaul and modification programs for various classes of navy ships. We outline the basic requirements placed upon order acceptance, process planning and capacity scheduling for large maintenance projects. In subsequent sections a number of procedures and algorithms to deal with these requirements, in particular a procedure for workload-based capacity planning, a database system to support process planning are developed, as well as a resource-constrained project scheduling system to support work planning at a more detailed level. The system has been designed to support decision making at the Navy Dockyard in particular, however, we believe that, due to its generic structure, it is applicable to a wide range of project-based manufacturing and maintenance environments

Reengineering Production Systems: the Royal Netherlands Naval Dockyard

Reengineering production systems in an attempt to meet tight cost, quality and leadtime standards has received considerable attention in the last decade. In this paper, we discuss the reengineering process at the Royal Netherlands Naval Dockyard. The process starts with a characterisation and a careful analysis of the production system and the set of objectives to be pursued. Next, a new production management structure is defined after which supporting planning and control systems are designed and a number of organisational changes are carried through. In this way, the Dockyard may combine high technological capabilities with an excellent logistic performance

Firm productivity, profit and business goal satisfaction: an assessment of maintenance decision effects on small and medium scale enterprises (SME’s)

[EN] This study was carried out to identify which factors are most relevant to managers of SMEs in maintenance decision making, and to investigate how these factors influence the realization of business goals satisfactorily, using structural equation modelling, partial least square design (PLS-SEM) to establish significant relationships between manifest and latent variables. A study of maintenance cost vis a vis the number of maintenance works carried out and profits realized was conducted to ascertain correlations and identify which factors played key roles in profit maximization. Results showed that with increasing level of maintenance for SMEs, profit margins reduced significantly. Also, an R2 value of 0.83 showed that the latent variable, business goal satisfaction was explained to a high degree (83%) by the manifest variables. Rentals of equipment from third parties (0.27), halting production (0.11) and outsourcing (0.39) were less considered for business sustainability per correlation coefficients than funds (0.79), and the possibilities to carry out both corrective (0.64) and preventive (0.58) maintenance works.  F-square value greater than zero was realized (0.387) and this showed reliability of the both inner and outer models. These findings can be used in building a decision tool or framework that will best suit SMEs with high financial budget constraints.Owusu-Mensah, D.; Quaye, EK.; Brako, L. (2021). Firm productivity, profit and business goal satisfaction: an assessment of maintenance decision effects on small and medium scale enterprises (SME’s). Journal of Applied Research in Technology & Engineering. 2(1):23-31. https://doi.org/10.4995/jarte.2021.14615OJS233121Al-Tabbaa, O., Ankrah, S. (2016). Social capital to facilitate 'engineered'university-industry collaboration for technology transfer: A dynamic perspective. Technological Forecasting and Social Change, 104, 1-15. https://doi.org/10.1016/j.techfore.2015.11.027Alarcón, D., Sánchez, J.A., Pablo de Olavide, U. (2015). Assessing convergent and discriminant validity in the ADHD-R IV rating scale: User-written commands for Average Variance Extracted (AVE), Composite Reliability (CR), and HeterotraitMonotrait ratio of correlations (HTMT). In Spanish STATA Meeting (pp. 1-39). Universidad Pablo de Olavide.Barone, G., Frangopol, D.M. (2014). Life-cycle maintenance of deteriorating structures by multi-objective optimization involving reliability, risk, availability, hazard and cost. Structural Safety, 48, 40-50. https://doi.org/10.1016/j.strusafe.2014.02.002Bertolini, M., Bevilacqua, M. (2006). A combined goal programming-AHP approach to maintenance selection problem. Reliability Engineering & System Safety, 91(7), 839-848. https://doi.org/10.1016/j.ress.2005.08.006Hair, Jr, Joseph, F., Tomas, G., Hult, M., Ringle, C., Sarstedt, M. (2016). A primer on partial least squares structural equation modeling (PLS-SEM). Sage publications.Jiang, R., Murthy, D.N.P. (2008). Maintenance: Decision Models for Management. Science press, Beijing, China.Joo, S-J. (2009). Scheduling preventive maintenance for modular designed components: A dynamic approach. European Journal of Operational Research, 192(2), 512-520. https://doi.org/10.1016/j.ejor.2007.09.033Lee, H. (2005). A cost/benefit model for investments in inventory and preventive maintenance in an imperfect production system. Computers and Industrial Engineering, 48(1), 55-68. https://doi.org/10.1016/j.cie.2004.07.008Liu, X., Wang, W., Peng, R. (2015). An integrated production: inventory and preventive maintenance model for a multiproduct production system. Reliab Eng Syst Safety, 137(2), 76-86. https://doi.org/10.1016/j.ress.2015.01.002Liu, X., Zheng, J., Fu, J., Ji, J., Chen, G. (2017). Multi-level optimization of maintenance plan for natural gas pipeline systems subject to external corrosion. Journal of Natural Gas Science and Engineering, 50, 64-73. https://doi.org/10.1016/j.jngse.2017.11.021Ma, J., Cheng, L., Li, D. (2018). Road Maintenance Optimization Model Based on Dynamic Programming in Urban Traffic Network. Journal of Advanced Transportation. Article ID 4539324, 11 pages. https://doi.org/10.1155/2018/4539324Marquez, A.C., Gupta, J.N.D. (2006). Contemporary maintenance management: process, framework and supporting pillars. Omega, 34(3), 313-326. https://doi.org/10.1016/j.omega.2004.11.003Nourelfath, M., Nahas, N. & Ben-Daya, M. (2015). Integrated preventive maintenance and production decisions for imperfect processes. Reliab Eng Syst Safety, 148, 21-31. https://doi.org/10.1016/j.ress.2015.11.015Olivotti D., Passlick J., Dreyer S., Lebek B., Breitner M.H. (2018) Maintenance Planning Using Condition Monitoring Data. In: Kliewer N., Ehmke J., Borndörfer R.(eds) Operations Research Proceedings 2017. https://doi.org/10.1007/978-3-319-89920-6_72Pallant, J. (2007). SPSS survival manual, 3rd. Edition. McGrath Hill.Parida, A., Kumar, U. (2016). Applications and Case Studies. Maintenance performance measurement (MPM): issues and challenges. Journal of Quality in Maintenance Engineering, 12(3), 239-251. https://doi.org/10.1108/13552510610685084Qiu, Q., Cui, L., Shen, J., Yang, L. (2017). Optimal maintenance policy considering maintenance errors for systems operating under performance-based contracts. Comput Industr Eng., 112, 147-155. https://doi.org/10.1016/j.cie.2017.08.025Ruschel, E., Santos, E.A.P. & Loures, E.D.F.R. (2017). Industrial maintenance decision-making: a systematic literature review. J Manuf Syst., 45, 180-194. https://doi.org/10.1016/j.jmsy.2017.09.003Shayesteh, E., Yu, J., Hilber, P. (2018). Maintenance optimization of power systems with renewable energy sources integrated. Energy, 149, 577-586. https://doi.org/10.1016/j.energy.2018.02.066Shen, J., Zhu, K. (2017). An uncertain single machine scheduling problem with periodic maintenance. Knowledge-Based Systems, 144, 32-41. https://doi.org/10.1016/j.knosys.2017.12.021Stebbins, R. A. (2001). Exploratory research in the social sciences (Vol. 48). Sage.Van, P.D., Bérenguer, C. (2012). Condition-based maintenance with imperfect preventive repairs for a deteriorating production system. Qual Reliab Eng., 28(6), 624-633. https://doi.org/10.1002/qre.1431Verbert, K., Schutter, B.D., Babuska, R. (2017). Timely condition-based maintenance planning for multi-component systems. Reliab Eng Syst Safety, 159, 310-321. https://doi.org/10.1016/j.ress.2016.10.032Yang, L., Ma, X., Zhao, Y. (2017). A condition-based maintenance model for a three-state system subject to degradation and environmental shocks. Comput Industr Eng., 105, 210-222. https://doi.org/10.1016/j.cie.2017.01.01

Development and implementation of preventive-maintenance practices in Nigerian industries.

A methodology for the development of PM using the modern approaches of FMEA, root-cause analysis, and fault-tree analysis is presented. Applying PM leads to a cost reduction in maintenance and less overall energy expenditure. Implementation of PM is preferable to the present reactive maintenance procedures (still prevalent in Nigeria

Life-cycle of fatigue sensitive structures under uncertainty

Fatigue is the one of the main contributors to problems related to structural safety of civil and marine structures. Life-cycle management (LCM) techniques considering various uncertainties can be used to predict the safe service life of fatigue sensitive structures, plan for their future inspections and support the decision making process regarding maintenance and repair actions. This paper provides a brief overview of the LCM of fatigue sensitive civil and marine structures under uncertainty. Probabilistic performance prediction, inspection scheduling and maintenance optimization for such structures are discussed

Maintenance Knowledge Management with Fusion of CMMS and CM

Abstract- Maintenance can be considered as an information, knowledge processing and management system. The management of knowledge resources in maintenance is a relatively new issue compared to Computerized Maintenance Management Systems (CMMS) and Condition Monitoring (CM) approaches and systems. Information Communication technologies (ICT) systems including CMMS, CM and enterprise administrative systems amongst others are effective in supplying data and in some cases information. In order to be effective the availability of high-quality knowledge, skills and expertise are needed for effective analysis and decision-making based on the supplied information and data. Information and data are not by themselves enough, knowledge, experience and skills are the key factors when maximizing the usability of the collected data and information. Thus, effective knowledge management (KM) is growing in importance, especially in advanced processes and management of advanced and expensive assets. Therefore efforts to successfully integrate maintenance knowledge management processes with accurate information from CMMSs and CM systems will be vital due to the increasing complexities of the overall systems. Low maintenance effectiveness costs money and resources since normal and stable production cannot be upheld and maintained over time, lowered maintenance effectiveness can have a substantial impact on the organizations ability to obtain stable flows of income and control costs in the overall process. Ineffective maintenance is often dependent on faulty decisions, mistakes due to lack of experience and lack of functional systems for effective information exchange [10]. Thus, access to knowledge, experience and skills resources in combination with functional collaboration structures can be regarded as vital components for a high maintenance effectiveness solution. Maintenance effectiveness depends in part on the quality, timeliness, accuracy and completeness of information related to machine degradation state, based on which decisions are made. Maintenance effectiveness, to a large extent, also depends on the quality of the knowledge of the managers and maintenance operators and the effectiveness of the internal & external collaborative environments. With emergence of intelligent sensors to measure and monitor the health state of the component and gradual implementation of ICT) in organizations, the conceptualization and implementation of E-Maintenance is turning into a reality. Unfortunately, even though knowledge management aspects are important in maintenance, the integration of KM aspects has still to find its place in E-Maintenance and in the overall information flows of larger-scale maintenance solutions. Nowadays, two main systems are implemented in most maintenance departments: Firstly, Computer Maintenance Management Systems (CMMS), the core of traditional maintenance record-keeping practices that often facilitate the usage of textual descriptions of faults and actions performed on an asset. Secondly, condition monitoring systems (CMS). Recently developed (CMS) are capable of directly monitoring asset components parameters; however, attempts to link observed CMMS events to CM sensor measurements have been limited in their approach and scalability. In this article we present one approach for addressing this challenge. We argue that understanding the requirements and constraints in conjunction - from maintenance, knowledge management and ICT perspectives - is necessary. We identify the issues that need be addressed for achieving successful integration of such disparate data types and processes (also integrating knowledge management into the “data types” and processes)

Generic Continuity of Operations/Continuity of Government Plan for State-Level Transportation Agencies, Research Report 11-01

The Homeland Security Presidential Directive 20 (HSPD-20) requires all local, state, tribal and territorial government agencies, and private sector owners of critical infrastructure and key resources (CI/KR) to create a Continuity of Operations/Continuity of Government Plan (COOP/COG). There is planning and training guidance for generic transportation agency COOP/COG work, and the Transportation Research Board has offered guidance for transportation organizations. However, the special concerns of the state-level transportation agency’s (State DOT’s) plan development are not included, notably the responsibilities for the entire State Highway System and the responsibility to support specific essential functions related to the State DOT Director’s role in the Governor’s cabinet. There is also no guidance on where the COOP/COG planning and organizing fits into the National Incident Management System (NIMS) at the local or state-level department or agency. This report covers the research conducted to determine how to integrate COOP/COG into the overall NIMS approach to emergency management, including a connection between the emergency operations center (EOC) and the COOP/COG activity. The first section is a presentation of the research and its findings and analysis. The second section provides training for the EOC staff of a state-level transportation agency, using a hybrid model of FEMA’s ICS and ESF approaches, including a complete set of EOC position checklists, and other training support material. The third section provides training for the COOP/COG Branch staff of a state-level transportation agency, including a set of personnel position descriptions for the COOP/COG Branch members

Synthesis of the Gambia-un Country Team Development Forum 2003

This compendium highlights the activities of the Gambia-United Nations Country Team Development Forum in sharing experiences and examinations of the complexities of the development process. It advocates for volunteerism and CSR as key drivers of sustainable development