Adaptive decision support for suggesting a machine tool maintenance strategy: from reactive to preventative

Purpose -- To produce a decision support aid for machine tool owners to utilise while deciding upon a maintenance strategy. Furthermore, the decision support tool is adaptive and capable of suggesting different strategies by monitoring for any change in machine tool manufacturing accuracy. Design/methodology/approach -- A maintenance cost estimation model is utilised within the research and development of this decision support system. An empirical-based methodology is pursued and validated through case study analysis. Findings -- A case study is provided where a schedule of preventative maintenance actions is produced to reduce the need for the future occurrences of reactive maintenance actions based on historical machine tool accuracy information. In the case-study, a 28% reduction in predicted accuracy-related expenditure is presented, equating to a saving of £14k per machine over a five year period. Research limitations/implications -- The emphasis on improving machine tool accuracy and reducing production costs is increasing. The presented research is pioneering in the development of a software-based tool to help reduce the requirement on domain-specific expert knowledge. Originality/value -- The paper presents an adaptive decision support system to assist with maintenance strategy selection. This is the first of its kind and is able to suggest a preventative strategy for those undertaking only reactive maintenance. This is of value for both manufacturers and researchers alike. Manufacturers will benefit from reducing maintenance costs, and researchers will benefit from the development and application of a novel decision support technique

Planning of aircraft fleet maintenance teams

This paper addresses a support information system for the planning of aircraft maintenance teams, assisting maintenance managers in delivering aircraft on time. The developed planning of aircraft maintenance teams is a computer application based on a mathematical programming problem written as a minimization one. The initial decision variables are positive integer variables specifying the allocation of available technicians by skills to maintenance teams. The objective function is a nonlinear function balancing the time spent and costs incurred with aircraft fleet maintenance. The data involves the technicians’ skills, the hours of work to perform maintenance tasks, the costs related to facilities, and the aircraft downtime cost. The realism of this planning entails random possibilities associated with maintenance workload data, and inference by a procedure of Monte Carlo simulation provides a proper set of workloads instead of going through all the possibilities. The based formalization is a nonlinear integer programming problem, converted into an equivalent pure linear integer programming problem, using a transformation from initial positive integer variables to Boolean ones. A case study addresses the use of this support information system for planning a team for aircraft maintenance of three lines under the uncertainty of workloads, and a discussion of results shows the serviceableness of the proposed support information system

Data-driven machine criticality assessment – maintenance decision support for increased productivity

Data-driven decision support for maintenance management is necessary for modern digitalized production systems. The data-driven approach enables analyzing the dynamic production system in realtime. Common problems within maintenance management are that maintenance decisions are experience-driven, narrow-focussed and static. Specifically, machine criticality assessment is a tool that is used in manufacturing companies to plan and prioritize maintenance activities. The maintenance problems are well exemplified by this tool in industrial practice. The tool is not trustworthy, seldomupdated and focuses on individual machines. Therefore, this paper aims at the development and validation of a framework for a data-driven machine criticality assessment tool. The tool supports prioritization and planning of maintenance decisions with a clear goal of increasing productivity. Four empirical cases were studied by employing a multiple case study methodology. The framework provides guidelines for maintenance decision-making by combining the Manufacturing Execution System (MES) and Computerized Maintenance Management System (CMMS) data with a systems perspective. The results show that by employing data-driven decision support within the maintenance organization, it can truly enable modern digitalized production systems to achieve higher levels of productivity

Developing Online Help Desk for Politeknik Tuanku Sultanah Bahiyah

A help desk is an integral part of many organizations which provides the users with a single focal point where users can channel their problems and ask for help on various ICT or utility equipments. Currently, Politeknik Tuanku Sultanah Bahiyah (PTSB) is adopting the traditional method for help desk maintenance support. The method to raise a defect complaint for ICT and utility equipments is by using telephone, hard copy form or email. Traditional help desk for maintenance support is subjected to communication problems especially between staff or department and this result in unnecessary delay in handling the defect complaint. The objective of this project is to develop an online help desk maintenance support system to improve the efficiency of the process flow. The "Vaishnavi and Kuechler" general methodology is employed in this study and system prototype is developed using Rapid Application Development (RAD) method. The evaluation of the system requirement and benefit is validated by potential users of the system, in this case are head of department Maintenance Unit, head of Information System Unit, technician from Maintenance Unit, technician from Information System Unit and staff from Department of Mathematics and Computer Science. Test result was acknowledged by the users and they are satisfied with the prototype online help desk system

Planned maintenance systems with respect to modern manufacturing strategies

To remain competitive in todays's increasingly automated environment, manufacturing industry must take a more proactive and strategic attitude towards maintenance. This thesis applies these concepts, as a case study? to Philips Components Durham an advanced manufacturing unit for colour television tubes. Consideration is first given to modern manufacturing strategies and the business objectives which the maintenance strategy must support. Recent organisational changes are then discussed and analysis made of the maintenance information systems infrastructure. Having related the maintenance department functional requirements to proprietary equipment management packages, the area of machine breakdown data collection is further discussed. To address the need for improved feedback on machine performance, a shop floor data collection and analysis system (Equipment Utilisation Improvement system) has been developed and commissioned. This system now provides more accurate and detailed information than was previously available. A further success of this system is that, as a pilot project, the system has highlighted many organisational and technical issues. These must be addressed before a more comprehensive equipment management package could be successfully implemented. Based on the knowledge gained from the implementation of this system, recommendations are made on the responsibilities for maintenance tasks, appropriate training for maintenance personnel and the further development of information systems to support the maintenance function

Ground Rules in Team Projects: Findings from a Prototype System to Support Students

Student team project work in higher education is one of the best ways to develop team working skills at the same time as learning about the subject matter. As today’s students require the freedom to learn at times and places that better match their lifestyles, there is a need for any support for team project work to be also available online. Team working requires that the task roles as well as the maintenance roles are taken into consideration, in that social interactions are just as important as carrying out the tasks of the project. The literature indicates that groupware, whilst effective in supporting the task roles, provides limited support for the maintenance roles of team working in the work place. As groupware was not specifically designed for student team working, it provides limited support for maintenance roles in student team projects. Virtual learning environments similarly provide support for completing the task roles. Many researchers have found that students experience difficulties with their team project work that reduce the perceived benefits of working in a team. It is proposed that helping students to agree on ground rules at the start of a project will improve team cohesion. This paper describes the implementation and evaluation of a prototype system to help students to agree on ground rules as they start their team projects. The system was tested with teams of students carrying out information systems team projects, using an interpretive case study research approach. In this case the teams had the additional problem of being composed of students from across three years of their undergraduate degree programmes, so they did not always have prior knowledge of each other’s preferences. We were trying to establish how useful this software tool would be to these student teams, in starting their project work. The findings showed that some of the student teams did find the ground rules function useful, but the team leaders were the ones who most appreciated its potential. The students may use the outputs in very different ways, but even just looking at the ground rules appeared to get team members thinking about their expectations for team working. Student teams do not often start by thinking about norms, but this study shows a positive benefit of encouraging teams to agree on ground rules at the start of their projects

Assessment of maintenance strategies for railway vehicles using Petri-Nets

The density of railway traffic has been steadily increasing over past years and decades. The developments have implicated a growing need for efficient operation and maintenance of railway rolling stock systems. Also the increased operation of articulated trains has induced new challenges on maintenance organization and planning. Selecting optimal maintenance strategies for each component does not only influence the availability of the railway vehicles but also the operational performance and the profitability of the operator. Suitable tools to analyse, compare and optimize different maintenance strategies are therefore required. Petri nets are such a mathematical tool that and have been applied for maintenance modeling and simulations of different applications. Several types of Petri nets with different properties have been introduced. One of the recently proposed extensions of Petri nets are the Abridged Petri Nets (APN) which fulfill the specific requirements of railway rolling stock maintenance. In this paper, we propose the application of APN in combination with the Monte-Carlo simulation for railway rolling stock maintenance evaluation. In a first step, the applicability of the APN approach was demonstrated on a theoretical case study comprising a condition based maintenance strategy for a system. In a second case study, several real application case studies were modeled and compared based on the processes and real application field data of three railway vehicle components. The tool can be further extended by pre-defining selected strategies that be easily implemented within an overall decision support system

Prediction of Optimal Maintenance Alternative for Iraqi Pavement Management Based on Multi-Objective Optimization Technique and Constraint Genetic Algorithm

Pavement management systems (PMS) are widely used to assist the transportation agencies to support the decision makers to select the best maintenance alternatives. To maintain a pavement network under a performance-based efficiently and cost-effectively in a long-term horizon, the local related agencies such as SCRB, mayoralty of Baghdad and Ministry of Municipalities need to provide balance multiple objectives (e.g., cost minimum, performance maximum) which are often different from the requirements of the traditional asset preservation practices. Accordingly, the main objective of this research is to develop a multi-objective optimization model to support the multi-year decision making process of the Iraqi pavement maintenance management system. Two optimization objectives are considered; maintenance cost minimization and pavement condition maximization. This study selects the flexible pavement section (R4/B-Expressway No.1) as the study area. Different field measurements are carried out to estimate the pavement performance indicators (PPI) which included; Pavement Condition Index (PCI), International Friction Index (IFI), and International Roughness Index (IRI) to formulate multi-objective optimization models to select optimal maintenance alternative for the selected case study. Keywords: pavement management system, pavement maintenance, multi-objective optimization, genetic algorithm

Deployment of a smart and predictive maintenance system in an industrial case study

Industrial manufacturing environments are often characterized as being stochastic, dynamic and chaotic, being crucial the implementation of proper maintenance strategies to ensure the production efficiency, since the machines? breakdown leads to a degradation of the system performance, causing the loss of productivity and business opportunities. In this context, the use of emergent ICT technologies, such as Internet of Things (IoT), machine learning and augmented reality, allows to develop smart and predictive maintenance systems, contributing for the reduction of unplanned machines? downtime by predicting possible failures and recovering faster when they occur. This paper describes the deployment of a smart and predictive maintenance system in an industrial case study, that considers IoT and machine learning technologies to support the online and real-time data collection and analysis for the earlier detection of machine failures, allowing the visualization, monitoring and schedule of maintenance interventions to mitigate the occurrence of such failures. The deployed system also integrates machine learning and augmented reality technologies to support the technicians during the execution of maintenance interventions.2411-78B2-7CDB | Pedro Miguel MoreiraN/

Graph-based reasoning in collaborative knowledge management for industrial maintenance

Capitalization and sharing of lessons learned play an essential role in managing the activities of industrial systems. This is particularly the case for the maintenance management, especially for distributed systems often associated with collaborative decision-making systems. Our contribution focuses on the formalization of the expert knowledge required for maintenance actors that will easily engage support tools to accomplish their missions in collaborative frameworks. To do this, we use the conceptual graphs formalism with their reasoning operations for the comparison and integration of several conceptual graph rules corresponding to different viewpoint of experts. The proposed approach is applied to a case study focusing on the maintenance management of a rotary machinery system