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.

Emerging issues for road construction and maintenance – A futuristic view

In the coming decades the design, construction and maintenance of roads will face a range of new issues and as such will require a number of new approaches. In particular, road authorities will be required to consider and respond to a range of issues related to climate change, and associated extreme weather events, such as the extensive flooding in January 2011 in Queensland, Australia Figure 1). Coupled with diminishing access to road construction supplies (such as aggregate), water scarcity, and the potential for increases in oil and electricity prices, this range of challenges bear little resemblance to those previously faced. In Australia, state and federal authorities face further pressures given the variety of needs resulting from the country's geographical and population diversity, expansive road networks, road freight requirements and relatively small population base.

Contributing to computing services to benchmarking asset management knowledge management

Asset management has broadened from a focus on maintenance management to whole of life cycle asset management requiring a suite of new competencies from asset procurement to management and disposal. Well developed skills and competencies as well as practical experience are a prerequisite to maintain capability, to manage demand as well to plan and set priorities and ensure on-going asset sustainability. This paper has as its focus to establish critical understandings of data, information and knowledge for asset management along with the way in which benchmarking these attributes through computer-aided design may aid a strategic approach to asset management. The paper provides suggestions to improve sharing, integration and creation of asset-related knowledge through the application of codification and personalization approaches.

A data fusion approach of multiple maintenance data sources for real-world reliability modelling

A central tenet in the theory of reliability modelling is the quantification of the probability of asset failure. In general, reliability depends on asset age and the maintenance policy applied. Usually, failure and maintenance times are the primary inputs to reliability models. However, for many organisations, different aspects of these data are often recorded in different databases (e.g. work order notifications, event logs, condition monitoring data, and process control data). These recorded data cannot be interpreted individually, since they typically do not have all the information necessary to ascertain failure and preventive maintenance times. This paper presents a methodology for the extraction of failure and preventive maintenance times using commonly-available, real-world data sources. A text-mining approach is employed to extract keywords indicative of the source of the maintenance event. Using these keywords, a Naïve Bayes classifier is then applied to attribute each machine stoppage to one of two classes: failure or preventive. The accuracy of the algorithm is assessed and the classified failure time data are then presented. The applicability of the methodology is demonstrated on a maintenance data set from an Australian electricity company.

Towards measuring the impact of information availability on maintenance decisions

Decisions concerning maintenance have become increasingly important and requires a diverse set of information as systems become more complex. The availability of information has an impact on the effectiveness of these decisions, and thus on the performance of the asset. This paper highlights the importance of quantifying the value of information on maintenance decisions and asset performance. In particular, we emphasise the need to focus on measuring value as opposed to cost of maintenance, which is the current practice. In this direction, we propose a measure - Value of Ownership (VOO) - to assess the value of information and performance of maintenance decisions throughout an assets lifecycle. © 2009 IFAC.