Group maintenance scheduling : a case study for a pipeline network

This paper presents a group maintenance scheduling case study for a water distributed network. This water pipeline network presents the challenge of maintaining aging pipelines with the associated increases in annual maintenance costs. The case study focuses on developing an effective maintenance plan for the water utility. Current replacement planning is difficult as it needs to balance the replacement needs under limited budgets. A Maintenance Grouping Optimization (MGO) model based on a modified genetic algorithm was utilized to develop an optimum group maintenance schedule over a 20-year cycle. The adjacent geographical distribution of pipelines was used as a grouping criterion to control the searching space of the MGO model through a Judgment Matrix. Based on the optimum group maintenance schedule, the total cost was effectively reduced compared with the schedules without grouping maintenance jobs. This optimum result can be used as a guidance to optimize the current maintenance plan for the water utility.

Condition-based maintenance optimisation without a predetermined strategy structure for a two-component series system

Most existing research on maintenance optimisation for multi-component systems only considers the lifetime distribution of the components. When the condition-based maintenance (CBM) strategy is adopted for multi-component systems, the strategy structure becomes complex due to the large number of component states and their combinations. Consequently, some predetermined maintenance strategy structures are often assumed before the maintenance optimisation of a multi-component system in a CBM context. Developing these predetermined strategy structure needs expert experience and the optimality of these strategies is often not proofed. This paper proposed a maintenance optimisation method that does not require any predetermined strategy structure for a two-component series system. The proposed method is developed based on the semi-Markov decision process (SMDP). A simulation study shows that the proposed method can identify the optimal maintenance strategy adaptively for different maintenance costs and parameters of degradation processes. The optimal maintenance strategy structure is also investigated in the simulation study, which provides reference for further research in maintenance optimisation of multi-component systems.

Design, manufacturing and maintenance of sugar mill equipment

The ISSCT Engineering Workshop 2008 in Brazil was well attended with 62 participants including 39 overseas visitors from 15 countries. The workshop addressed the theme Design, manufacturing and maintenance of sugar mill equipment. From the technical sessions, the following conclusions were drawn: • Several speakers articulated a shared vision of the future of the Brazilian sugar industry. This shared vision gives considerable confidence that the vision can become a reality. • There is an increased focus on energy products. As a result, the reduction of factory energy consumption in order to maximise the energy available for products is also a focus. • New equipment and products are being developed with reduced power consumption, lower capital and maintenance costs, and better performance. • Methods presented for reducing maintenance costs included the use of a maintenance management system, condition monitoring and material selection. The workshop was held in conjunction with Piracicaba’s annual SIMTEC exhibition for the sugar and alcohol industries that provides a forum for technical presentations and discussion, and showcases products and services from manufacturers and service providers. In return for holding the workshop in conjunction with SIMTEC, SIMTEC provided sponsorship for the workshop, including paying travel and accommodation costs for two invited speakers, and organisation for the workshop. The ISSCT and SIMTEC technical programs were arranged so that their technical sessions did not clash, and the ISSCT program was extended a day to provide an opportunity for ISSCT participants to attend the SIMTEC exhibition. Informal feedback from workshop participants suggested that the arrangement between ISSCT and SIMTEC worked well. Site visits to two manufacturing facilities and two sugar mills were arranged as part of the workshop.

Optimizing preventative maintenance strategies for linear assets

Linear assets are engineering infrastructure, such as pipelines, railway lines, and electricity cables, which span long distances and can be divided into different segments. Optimal management of such assets is critical for asset owners as they normally involve significant capital investment. Currently, Time Based Preventive Maintenance (TBPM) strategies are commonly used in industry to improve the reliability of such assets, as they are easy to implement compared with reliability or risk-based preventive maintenance strategies. Linear assets are normally of large scale and thus their preventive maintenance is costly. Their owners and maintainers are always seeking to optimize their TBPM outcomes in terms of minimizing total expected costs over a long term involving multiple maintenance cycles. These costs include repair costs, preventive maintenance costs, and production losses. A TBPM strategy defines when Preventive Maintenance (PM) starts, how frequently the PM is conducted and which segments of a linear asset are operated on in each PM action. A number of factors such as required minimal mission time, customer satisfaction, human resources, and acceptable risk levels need to be considered when planning such a strategy. However, in current practice, TBPM decisions are often made based on decision makers’ expertise or industrial historical practice, and lack a systematic analysis of the effects of these factors. To address this issue, here we investigate the characteristics of TBPM of linear assets, and develop an effective multiple criteria decision making approach for determining an optimal TBPM strategy. We develop a recursive optimization equation which makes it possible to evaluate the effect of different maintenance options for linear assets, such as the best partitioning of the asset into segments and the maintenance cost per segment.

Application of the analytic hierarchy process (AHP) in multi-criteria analysis of the selection of intelligent building systems

The availability of innumerable intelligent building (IB) products, and the current dearth of inclusive building component selection methods suggest that decision makers might be confronted with the quandary of forming a particular combination of components to suit the needs of a specific IB project. Despite this problem, few empirical studies have so far been undertaken to analyse the selection of the IB systems, and to identify key selection criteria for major IB systems. This study is designed to fill these research gaps. Two surveys: a general survey and the analytic hierarchy process (AHP) survey are proposed to achieve these objectives. The first general survey aims to collect general views from IB experts and practitioners to identify the perceived critical selection criteria, while the AHP survey was conducted to prioritize and assign the important weightings for the perceived criteria in the general survey. Results generally suggest that each IB system was determined by a disparate set of selection criteria with different weightings. ‘Work efficiency’ is perceived to be most important core selection criterion for various IB systems, while ‘user comfort’, ‘safety’ and ‘cost effectiveness’ are also considered to be significant. Two sub-criteria, ‘reliability’ and ‘operating and maintenance costs’, are regarded as prime factors to be considered in selecting IB systems. The current study contributes to the industry and IB research in at least two aspects. First, it widens the understanding of the selection criteria, as well as their degree of importance, of the IB systems. It also adopts a multi-criteria AHP approach which is a new method to analyse and select the building systems in IB. Further research would investigate the inter-relationship amongst the selection criteria.

Living walls - a way to green the built environment

Actions Towards Sustainable Outcomes Environmental Issues/Principal Impacts The increasing urbanisation of cities brings with it several detrimental consequences, such as: • Significant energy use for heating and cooling many more buildings has led to urban heat islands and increased greenhouse gas emissions. • Increased amount of hard surfaces, which not only contributes to higher temperatures in cities, but also to increased stormwater runoff. • Degraded air quality and noise. • Health and general well-being of people is frequently compromised, by inadequate indoor air quality. • Reduced urban biodiversity. Basic Strategies In many design situations, boundaries and constraints limit the application of cutting EDGe actions. In these circumstances, designers should at least consider the following: • Living walls are an emerging technology, and many Australian examples function more as internal feature walls. However,as understanding of the benefits and construction of living walls develops this technology could be part of an exterior facade that enhances a building’s thermal performance. • Living walls should be designed to function with an irrigation system using non-potable water. Cutting EDGe Strategies • Living walls can be part of a design strategy that effectively improves the thermal performance of a building, thereby contributing to lower energy use and greenhouse gas emissions. • Including living walls in the initial stages of design would provide greater flexibility to the design, especially of the facade, structural supports, mechanical ventilation and watering systems, thus lowering costs. • Designing a building with an early understanding of living walls can greatly reduce maintenance costs. • Including plant species and planting media that would be able to remove air impurities could contribute to improved indoor air quality, workplace productivity and well-being. Synergies and References • Living walls are a key research topic at the Centre for Subtropical Design, Queensland University of Technology: http://www.subtropicaldesign.bee.qut.edu.au • BEDP Environment Design Guide: DES 53: Roof and Facade Gardens • BEDP Environment Design Guide: GEN 4: Positive Development – Designing for Net Positive Impacts (see green scaffolding and green space frame walls). • Green Roofs Australia: www.greenroofs.wordpress.com • Green Roofs for Healthy Cities USA: www.greenroofs.org

Downtime reduction analysis of the Australia Post flat mail optical character reader

Machine downtime, whether planned or unplanned, is intuitively costly to manufacturing organisations, but is often very difficult to quantify. The available literature showed that costing processes are rarely undertaken within manufacturing organisations. Where cost analyses have been undertaken, they generally have only valued a small proportion of the affected costs, leading to an overly conservative estimate. This thesis aimed to develop a cost of downtime model, with particular emphasis on the application of the model to Australia Post’s Flat Mail Optical Character Reader (FMOCR). The costing analysis determined a cost of downtime of $5,700,000 per annum, or an average cost of $138 per operational hour. The second section of this work focused on the use of the cost of downtime to objectively determine areas of opportunity for cost reduction on the FMOCR. This was the first time within Post that maintenance costs were considered along side of downtime for determining machine performance. Because of this, the results of the analysis revealed areas which have historically not been targeted for cost reduction. Further exploratory work was undertaken on the Flats Lift Module (FLM) and Auto Induction Station (AIS) Deceleration Belts through the comparison of the results against two additional FMOCR analysis programs. This research has demonstrated the development of a methodical and quantifiable cost of downtime for the FMOCR. This has been the first time that Post has endeavoured to examine the cost of downtime. It is also one of the very few methodologies for valuing downtime costs that has been proposed in literature. The work undertaken has also demonstrated how the cost of downtime can be incorporated into machine performance analysis with specific application to identifying high costs modules. The outcome of this report has both been the methodology for costing downtime, as well as a list of areas for cost reduction. In doing so, this thesis has outlined the two key deliverables presented at the outset of the research.

Intelligent prognostics of machinery health utilising suspended condition monitoring data

The ability to forecast machinery failure is vital to reducing maintenance costs, operation downtime and safety hazards. Recent advances in condition monitoring technologies have given rise to a number of prognostic models for forecasting machinery health based on condition data. Although these models have aided the advancement of the discipline, they have made only a limited contribution to developing an effective machinery health prognostic system. The literature review indicates that there is not yet a prognostic model that directly models and fully utilises suspended condition histories (which are very common in practice since organisations rarely allow their assets to run to failure); that effectively integrates population characteristics into prognostics for longer-range prediction in a probabilistic sense; which deduces the non-linear relationship between measured condition data and actual asset health; and which involves minimal assumptions and requirements. This work presents a novel approach to addressing the above-mentioned challenges. The proposed model consists of a feed-forward neural network, the training targets of which are asset survival probabilities estimated using a variation of the Kaplan-Meier estimator and a degradation-based failure probability density estimator. The adapted Kaplan-Meier estimator is able to model the actual survival status of individual failed units and estimate the survival probability of individual suspended units. The degradation-based failure probability density estimator, on the other hand, extracts population characteristics and computes conditional reliability from available condition histories instead of from reliability data. The estimated survival probability and the relevant condition histories are respectively presented as “training target” and “training input” to the neural network. The trained network is capable of estimating the future survival curve of a unit when a series of condition indices are inputted. Although the concept proposed may be applied to the prognosis of various machine components, rolling element bearings were chosen as the research object because rolling element bearing failure is one of the foremost causes of machinery breakdowns. Computer simulated and industry case study data were used to compare the prognostic performance of the proposed model and four control models, namely: two feed-forward neural networks with the same training function and structure as the proposed model, but neglected suspended histories; a time series prediction recurrent neural network; and a traditional Weibull distribution model. The results support the assertion that the proposed model performs better than the other four models and that it produces adaptive prediction outputs with useful representation of survival probabilities. This work presents a compelling concept for non-parametric data-driven prognosis, and for utilising available asset condition information more fully and accurately. It demonstrates that machinery health can indeed be forecasted. The proposed prognostic technique, together with ongoing advances in sensors and data-fusion techniques, and increasingly comprehensive databases of asset condition data, holds the promise for increased asset availability, maintenance cost effectiveness, operational safety and – ultimately – organisation competitiveness.

An integrated rail track degradation model

There has been a worldwide trend to increase axle loads and train speeds. This means that railway track degradation will be accelerated, and track maintenance costs will be increased significantly. There is a need to investigate the consequences of increasing traffic load. The aim of the research is to develop a model for the analysis of physical degradation of railway tracks in response to changes in traffic parameters, especially increased axle loads and train speeds. This research has developed an integrated track degradation model (ITDM) by integrating several models into a comprehensive framework. Mechanistic relationships for track degradation hav~ ?een used wherever possible in each of the models contained in ITDM. This overcc:mes the deficiency of the traditional statistical track models which rely heavily on historical degradation data, which is generally not available in many railway systems. In addition statistical models lack the flexibility of incorporating future changes in traffic patterns or maintenance practices. The research starts with reviewing railway track related studies both in Australia and overseas to develop a comprehensive understanding of track performance under various traffic conditions. Existing railway related models are then examined for their suitability for track degradation analysis for Australian situations. The ITDM model is subsequently developed by modifying suitable existing models, and developing new models where necessary. The ITDM model contains four interrelated submodels for rails, sleepers, ballast and subgrade, and track modulus. The rail submodel is for rail wear analysis and is developed from a theoretical concept. The sleeper submodel is for timber sleepers damage prediction. The submodel is developed by modifying and extending an existing model developed elsewhere. The submodel has also incorporated an analysis for the likelihood of concrete sleeper cracking. The ballast and subgrade submodel is evolved from a concept developed in the USA. Substantial modifications and improvements have been made. The track modulus submodel is developed from a conceptual method. Corrections for more global track conditions have been made. The integration of these submodels into one comprehensive package has enabled the interaction between individual track components to be taken into account. This is done by calculating wheel load distribution with time and updating track conditions periodically in the process of track degradation simulation. A Windows-based computer program ~ssociated with ITDM has also been developed. The program enables the user to carry out analysis of degradation of individual track components and to investigate the inter relationships between these track components and their deterioration. The successful implementation of this research has provided essential information for prediction of increased maintenance as a consequence of railway trackdegradation. The model, having been presented at various conferences and seminars, has attracted wide interest. It is anticipated that the model will be put into practical use among Australian railways, enabling track maintenance planning to be optimized and potentially saving Australian railway systems millions of dollars in operating costs.

Construction, application and validation of selection evaluation model (SEM) for intelligent HVAC control system

Design teams are confronted with the quandary of choosing apposite building control systems to suit the needs of particular intelligent building projects, due to the availability of innumerable ‘intelligent’ building products and a dearth of inclusive evaluation tools. This paper is organised to develop a model for facilitating the selection evaluation for intelligent HVAC control systems for commercial intelligent buildings. To achieve these objectives, systematic research activities have been conducted to first develop, test and refine the general conceptual model using consecutive surveys; then, to convert the developed conceptual framework into a practical model; and, finally, to evaluate the effectiveness of the model by means of expert validation. The results of the surveys are that ‘total energy use’ is perceived as the top selection criterion, followed by the‘system reliability and stability’, ‘operating and maintenance costs’, and ‘control of indoor humidity and temperature’. This research not only presents a systematic and structured approach to evaluate candidate intelligent HVAC control system against the critical selection criteria (CSC), but it also suggests a benchmark for the selection of one control system candidate against another.

EduBuilding: material selection from life cycle costing sensitivity

Life Cycle Cost Analysis provides a form of synopsis of the initial and consequential costs of building related decisions. These cost figures may be implemented to justify higher investments, for example, in the quality or flexibility of building solutions through a long term cost reduction. The emerging discipline of asset mnagement is a promising approach to this problem, because it can do things that techniques such as balanced scorecards and total quantity cannot. Decisions must be made about operating and maintaining infrastructure assets. An injudicious sensitivity of life cycle costing is that the longer something lasts, the less it costs over time. A life cycle cost analysis will be used as an economic evaluation tool and collaborate with various numbers of analyses. LCCA quantifies incurring costs commonly overlooked (by property and asset managers and designs) as replacement and maintenance costs. The purpose of this research is to examine the Life Cycle Cost Analysis on building floor materials. By implementing the life cycle cost analysis, the true cost of each material will be computed projecting 60 years as the building service life and 5.4% as the inflation rate percentage to classify and appreciate the different among the materials. The analysis results showed the high impact in selecting the floor materials according to the potential of service life cycle cost next.

Optimal voltage support mechanism in distribution networks

Optimal scheduling of voltage regulators (VRs), fixed and switched capacitors and voltage on customer side of transformer (VCT) along with the optimal allocaton of VRs and capacitors are performed using a hybrid optimisation method based on discrete particle swarm optimisation and genetic algorithm. Direct optimisation of the tap position is not appropriate since in general the high voltage (HV) side voltage is not known. Therefore, the tap setting can be determined give the optimal VCT once the HV side voltage is known. The objective function is composed of the distribution line loss cost, the peak power loss cost and capacitors' and VRs' capital, operation and maintenance costs. The constraints are limits on bus voltage and feeder current along with VR taps. The bus voltage should be maintained within the standard level and the feeder current should not exceed the feeder-rated current. The taps are to adjust the output voltage of VRs between 90 and 110% of their input voltages. For validation of the proposed method, the 18-bus IEEE system is used. The results are compared with prior publications to illustrate the benefit of the employed technique. The results also show that the lowest cost planning for voltage profile will be achieved if a combination of capacitors, VRs and VCTs is considered.

Smart Measures for Sustainable Outcomes: Integrated Considerations for Sustainable Refurbishment of Office Buildings

The increasing stock of aging office buildings will see a significant growth in retrofitting projects in Australian capital cities. Stakeholders of refitting works will also need to take on the sustainability challenge and realize tangible outcomes through project delivery. Traditionally, decision making for aged buildings, when facing the alternatives, is typically economically driven and on ad hoc basis. This leads to the tendency to either delay refitting for as long as possible thus causing building conditions to deteriorate, or simply demolish and rebuild with unjust financial burden. The technologies involved are often limited to typical strip-clean and repartition with dry walls and office cubicles. Changing business operational patterns, the efficiency of office space, and the demand on improved workplace environment, will need more innovative and intelligent approaches to refurbishing office buildings. For example, such projects may need to respond to political, social, environmental and financial implications. There is a need for the total consideration of buildings structural assessment, modeling of operating and maintenance costs, new architectural and engineering designs that maximise the utility of the existing structure and resulting productivity improvement, specific construction management procedures including procurement methods, work flow and scheduling and occupational health and safety. Recycling potential and conformance to codes may be other major issues. This paper introduces examples of Australian research projects which provided a more holistic approach to the decision making of refurbishing office space, using appropriate building technologies and products, assessment of residual service life, floor space optimisation and project procurement in order to bring about sustainable outcomes. The paper also discusses a specific case study on critical factors that influence key building components for these projects and issues for integrated decision support when dealing with the refurbishment, and indeed the “re-life”, of office buildings.