Asset Management Decisions Research Articles

A reliability study of railway switch and crossing components

Within any railway network Switches and Crossings (S&C) are essential. They allow trains to change tracks, allowing different routes to be selected. Despite their necessity, they generally have a lower reliability than plain line track and are often subject to breakdowns due to the high number of interlinking electrical and mechanical components they contain. Due to their location such as station throats and major junctions, S&C breakdown is generally very disruptive to traffic causing significant delays. Ensuring that S&C units are maintained correctly and minimising their risk of failure, is therefore of critical importance to railway asset managers. This research uses maintenance and failure data to determine probability distributions for the degradation, failure, inspection and maintenance of nine critical components within S&C units. These distributions can then be used within an asset management framework to simulate the expected operational behaviour of an S&C unit under a given set of conditions, allowing more informed asset management decisions to be taken.

Creating an asset management plan for traffic signal structures through interactive explorations of wind induced fatigue damage

This paper presents an asset management framework for remote monitoring of traffic signal structures for fatigue damage under wind loads, using historical wind measurements from nearby weather stations. Estimate wind profiles developed from the wind data are used to calculate fatigue damage at welded connections for a wide variety of common traffic pole configurations. The analysis is embedded within a novel remote monitoring application that performs the fatigue life analysis in real time for a user defined location and traffic pole configuration. Such a framework is the first of its kind that allows operators to make real time asset management decisions though interactive explorations of the relationship between traffic pole configuration and fatigue life due to local wind conditions. Results are demonstrated using several traffic poles located around Logan, Utah, which has complex topography that creates drastic changes in wind conditions over short geographical distances. The results illustrate the ability of the remote monitoring application to detect small-scale differences in wind conditions that result in significant differences in the estimated fatigue life of traffic structures within communities. Such results highlight the need for, and feasibility of developing site-specific asset management frameworks at a much finer spatial resolution than previous attempts.

Monitoring of renewable energy systems by IoT‐aided SCADA system

AbstractWith the rapid increase of renewable energy generation worldwide, real‐time information has become essential to manage such assets, especially for systems installed offshore and in remote areas. To date, there is no cost‐effective condition monitoring technique that can assess the state of renewable energy sources in real‐time and provide suitable asset management decisions to optimize the utilization of such valuable assets and avoid any full or partial blackout due to unexpected faults. Based on the Internet of Things scheme, this paper represents a new application for the Supervisory Control and Data Acquisition (SCADA) system to monitor a hybrid system comprising photovoltaic, wind, and battery energy storage systems. Electrical parameters such as voltage, current, and power are monitored in real‐time via the ThingSpeak website. Network operators can control components of the hybrid power system remotely by the proposed SCADA system. The SCADA system is interfaced with the Matlab/Simulink software tool through KEPServerEX client. For cost‐effective design, low‐cost electronic components and Arduino Integrated Development Environment ATMega2560 remote terminal unit are employed to develop a hardware prototype for experimental analysis. Simulation and experimental results attest to the feasibility of the proposed system. Compared with other existing techniques, the developed system features advantages in terms of reliability and cost‐effectiveness.

Methodology Maps for Model-Based Sensor-Data Interpretation to Support Civil-Infrastructure Management

With increasing urbanization and depleting reserves of raw materials for construction, sustainable management of existing infrastructure will be an important challenge in this century. Structural sensing has the potential to increase knowledge of infrastructure behavior and improve engineering decision making for asset management. Model-based methodologies such as residual minimization (RM), Bayesian model updating (BMU) and error-domain model falsification (EDMF) have been proposed to interpret monitoring data and support asset management. Application of these methodologies requires approximations and assumptions related to model class, model complexity and uncertainty estimations, which ultimately affect the accuracy of data interpretation and subsequent decision making. This paper introduces methodology maps in order to provide guidance for appropriate use of these methodologies. The development of these maps is supported by in-house evaluations of nineteen full-scale cases since 2016 and a two-decade assessment of applications of model-based methodologies. Nineteen full-scale studies include structural identification, fatigue-life assessment, post-seismic risk assessment and geotechnical-excavation risk quantification. In some cases, much, previously unknown, reserve capacity has been quantified. RM and BMU may be useful for model-based data interpretation when uncertainty assumptions and computational constraints are satisfied. EDMF is a special implementation of BMU. It is more compatible with usual uncertainty characteristics, the nature of typically available engineering knowledge and infrastructure evaluation concepts than other methodologies. EDMF is most applicable to contexts of high magnitudes of uncertainties, including significant levels of model bias and other sources of systematic uncertainty. EDMF also provides additional practical advantages due to its ease of use and flexibility when information changes. In this paper, such observations have been leveraged to develop methodology maps. These maps guide users when selecting appropriate methodologies to interpret monitoring information through reference to uncertainty conditions and computational constraints. This improves asset-management decision making. These maps are thus expected to lead to lower maintenance costs and more sustainable infrastructure compared with current practice.

Comparison of Machine Learning Techniques for Condition Assessment of Sewer Network

Assessment of the structural condition of sewer pipes is one of the critical steps in asset management and support investment decisions; therefore, structural condition models with high accuracy are important that can help the utility managers and other authorities correctly assess the current condition of the sewage network and effectively initiate maintenance and rehabilitation strategies. The main objective of this research is to assess the potential application of advanced machine learning (ML) for predicting the structural condition of sewer pipes with a case study in Ålesund city, Norway. Nine physical factors (i.e., age, diameter, depth, slope, length, pipe type, material, pipe form, and connection type) and ten environmental factors (i.e., rainfall, geology, landslide area, building area, population, land cover, groundwater level, traffic volume, distance from the road, and soil type) were used to assess the sewer structural condition employing seventeen ML models. After processing the sewer inspections, 1,159 of 1,449 individual pipelines were used to train the structural condition model. The performance of ML models was validated using the 290 remaining inspected sewer pipes. The area under the Receiver Operating Characteristic (AUC-ROC) curve and accuracy (ACC) showed that the Random Forest (AUC-ROC = 77.6% and ACC = 78.3%) is a sensitive model for predicting the condition of sewer pipes in the study area. Based on the RF model, maps of predicted conditions of sewers were generated that may be useful for utilities and water managers to establish future sewer system maintenance strategies.

Petri net-based reliability and availability analysis to support asset management: a CODOG propulsion system case study

It is common sense that reliability and availability analysis is an appropriate technique to support proper physical asset management. However, this task can become very complex in face of a dynamic demand during the operational course of the analyzed assets. That is the case when considering military missions since operational conditions are not as steady as in the manufacturing industry for instance, and different operational scenarios are expected in short periods. In these cases, advanced tools for reliability and availability analysis such as Petri nets are expected to be applied. Accordingly, this paper presents a Petri net-based reliability and availability analysis to support maintenance decision-making for asset management. It addresses a reliability and availability analysis in a dynamic demand scenario that is demonstrated through an application case study for a Combined Diesel Or Gas (CODOG) propulsion system that is used in military ships.

Development an Artificial Neural Network Model for Estimating Cost of R/C Building by Using Life‐Cycle Cost Function: Case Study of Mexico City

This paper addresses the importance of engineering asset management decisions and control. For this purpose, a Life‐Cycle Cost (LCC) analysis is conducted for typical reinforced concrete (R/C) buildings located in Mexico City. The objective of this study is to develop an artificial neural network (ANN) model that can estimate the total expected cost of R/C buildings by using LCC functions. The total cost includes the initial cost and the cost of the damage caused by future possible ground motions at the site of interest. The present value of the cost includes: initial cost, repair or reconstruction cost, cost of damage to the contents, costs associated with the loss of life or injuries and economic losses. The structural performance is evaluated using probabilistic models, artificial neural networks models are used to obtain the seismic response of the buildings. The methodology is applied to a set of reinforced concrete buildings with 4, 8, and 12 stories which are located at the soft soil of Mexico City. Finally, it is concluded that the life‐cycle cost is efficiently obtained using artificial neural network models for estimating the structural reliability of reinforced concrete buildings, in such a way that it can be used as an excellent planning tool that covers long spans of time.

Information Quality for Effective Asset Management: A literature review

Information quality is critical to successful asset management decision-making. Substandard quality information will likely cause significant negative short- and long-term consequences. The ongoing digital transformation in the Architecture, Engineering, and Construction (AEC) industry has influenced ways to manage physical assets. Yet many asset owners lack a clear understanding of identifying indispensable quality dimensions that satisfy the business, system, and technical requirements. This paper aims to comprehensively analyse asset information quality management with a systematic literature review. The study reveals that the quality dimension of ‘accuracy’ alone cannot support various asset management functions. Additionally, quality deficiencies remain in Building Information Modelling (BIM)-based project delivery handover documents, establishing insufficient asset baselines for future planning. Moreover, the limited knowledge of the quality complications of information generated through technical solutions suggests additional work is required to gain insights into vital quality dimensions. The findings of this study underpin the basis for classifying quality dimensions to support essential asset management processes while pointing to future study areas.

Integrated approach for pipe failure prediction and condition scoring in water infrastructure systems

Pipe failures in water distribution infrastructure have significant economic, environmental, and public health impacts. To alleviate these impacts, pipe deterioration modeling has been increasingly implemented to characterize and predict pipe failure patterns with the aim of prioritizing repair and replacement decisions. Logistic regression has been recognized in recent literature as a strong candidate for failure prediction modeling. However, previous studies have often been limited to demonstrating the application of logistic regression for estimating failure probabilities. This study builds on previous efforts by proposing an approach for implementing logistic regression into a holistic framework for asset management decision-making. This framework incorporates logistic regression modeling, with a flexible time-interval, into a practical condition scoring methodology that accounts for the attitude of water utilities towards risk. The developed framework is demonstrated and tested on a 20-year pipe failure dataset of a large metropolitan US city. The logistic regression model displayed high accuracy in estimating the probability of failure within different time intervals, and the scoring method showed a reasonable ability to predict the criticality of repair decisions for pipes based on their condition.

Systematic Approaches and Analyses on Voltage Uprating of 132 kV Transmission Lines: A Case Study in Malaysia

With the ageing infrastructure and fast-growing demand faced by utilities worldwide, voltage uprating is one of the most effective solutions and strategic approaches in asset management decisions. In this study, a systematic voltage uprating method is used to propose the new voltage level, based on the existing transmission lines’ physical structure, by utilising appropriate techniques. Prior to work conducted, the existing 132 kV transmission line was first examined in terms of the clearances and the components attached, and a technical issue on the clearance under still air was found to be a significant concern, particularly on phase-to-earth arrangements. Next, the proposed techniques and processes, by increasing the insulation strength and improving air conductor clearances, were used for the individual existing 132 kV transmission tower. The proposed voltage uprating of the 132 kV transmission tower comprehensively examines phase-to-earth under normal and extreme wind conditions based on electrical clearance requirements for 275 kV transmission lines. The proposed uprating model was then simulated using the Finite Element Method (FEM)-based commercial software under several lightning conditions, i.e., various basic lightning insulation levels (BIL) and different flashover mechanisms, taking into consideration still air and wind conditions for the voltage profile and its insulation strength analyses. The study found that the voltage profile condition for the proposed voltage uprating model was able to withstand its BIL under all conditions and thus satisfied the electrical requirement needed.

Issues and Challenges in Voltage Uprating for Sustainable Power Operation: A Case Study of a 132 kV Transmission Line System in Malaysia

Ageing grid infrastructure is a critical issue that is currently faced by electrical utilities worldwide, resulting in crucial decisions that must be made for the replacement, repair, or refurbishment of assets under constrained budgetary conditions, as well as other factors. As one of the fastest-developing countries globally, Malaysia is steadily growing in terms of its cumulative population, large industries, and advanced transportation sector, leading to an increasing electricity demand and, consequently, putting stress on electric utility providers to meet these tremendous demands. Significant concerns in the new development of transmission in Malaysia are the environmental issues, which involve exploration in gazetted areas and forest reserves. This paper describes the issues and challenges in developing a new transmission line system in Malaysia. In recent years, uprating existing transmission line systems has been one of the best asset management decisions for electric utility operators in order to meet such a demand for capacity. This paper assesses the technical issues and conventional methods of uprating the voltage of an existing transmission line system. The initial study found that the phase-to-Earth clearance does not fulfil the electrical clearance envelope. The study on the existing 132 kV transmission line system further analysed these issues and proposed an appropriate technique for uprating to a 275 kV transmission line system. Finally, the results indicate that the voltage uprating of the 132 kV transmission line system to 275 kV is able to satisfy the electrical clearance requirement envelope.

How a hydrogen start-up can contribute to the energy transition through the emerging hydrogen economy

How a hydrogen start-up can contribute to the energy transition through the emerging hydrogen economy

Battery asset management with cycle life prognosis

Battery Asset Management problem determines the minimum cost replacement schedules for each individual asset in a group of battery assets that operate in parallel. Battery cycle life varies under different operating conditions including temperature, depth of discharge (DOD), charge rate, etc., and a battery deteriorates due to usage, which cannot be handled by current asset management models. This paper presents a new battery asset management methodology where battery cycle life prognosis is integrated with parallel asset management to reduce lifecycle cost of the Battery Energy Storage Systems (BESS). For the battery failure time prognosis, a nonlinear physics-based battery capacity fade model is developed and incorporated in parallel asset management model to update battery capacity over time. Experiment results have shown that the developed battery asset management methodology can be conveniently used to facilitate BESS asset management decision making thereby decreasing asset lifecycle costs.

GeoBIM for built environment condition assessment supporting asset management decision making

The digital transformation in management of the built environment is more and more evident. While the benefits of location data, from Building Information Modelling or Geographical Information Systems, have been explored separately, their combination - GeoBIM - in asset management has never been explored. Data collection for condition assessment is challenging due to quantity, types, frequency and quality of data. We first describe the opportunities and challenges of GeoBIM for condition assessment. The theoretical approach is then validated developing an integrated GeoBIM model of the digital built environment, for a neighbourhood in Milan, Italy. Data are collected, linked, processed and analysed, through multiple software platforms, providing relevant information for asset management decision making. Good results are achieved in rapid massive data collection, improved visualisation, and analysis. While further testing and development is required, the case study outcomes demonstrated the innovation and the mid-term service-oriented potential of the proposed approach.

Practical Demonstration of a Hybrid Model for Optimising the Reliability, Risk, and Maintenance of Rolling Stock Subsystem

Railway transport system (RTS) failures exert enormous strain on end-users and operators owing to in-service reliability failure. Despite the extensive research on improving the reliability of RTS, such as signalling, tracks, and infrastructure, few attempts have been made to develop an effective optimisation model for improving the reliability, and maintenance of rolling stock subsystems. In this paper, a new hybrid model that integrates reliability, risk, and maintenance techniques is proposed to facilitate engineering failure and asset management decision analysis. The upstream segment of the model consists of risk and reliability techniques for bottom-up and top-down failure analysis using failure mode effects and criticality analysis and fault tree analysis, respectively. The downstream segment consists of a (1) decision-making grid (DMG) for the appropriate allocation of maintenance strategies using a decision map and (2) group decision-making analysis for selecting appropriate improvement options for subsystems allocated to the worst region of the DMG map using the multi-criteria pairwise comparison features of the analytical hierarchy process. The hybrid model was illustrated through a case study for replacing an unreliable pneumatic brake unit (PBU) using operational data from a UK-based train operator where the frequency of failures and delay minutes exceeded the operator’s original target by 300% and 900%, respectively. The results indicate that the novel hybrid model can effectively analyse and identify a new PBU subsystem that meets the operator’s reliability, risk, and maintenance requirements.

Designing a Sustainability Assessment Framework for Selecting Sustainable Wastewater Treatment Technologies in Corporate Asset Decisions

There is a growing demand for an integrated assessment to identify and select asset management options based on sustainability in the wastewater industry. However, water companies are often not equipped with a rigorous methodology and sufficient resources to perform sustainability assessments. Although many frameworks and tools for sustainability assessment have been developed in academia, practical challenges such as feasibility and usability remain when implementing sustainability assessment methods to support corporate decision-making. This study developed a Multi-Criteria Analysis based framework to evaluate wastewater treatment processes from a sustainability perspective. This study firstly explored the decision and organizational context of a water company with preliminary interviews and then applied the Analytical Hierarchy Process (AHP) with composite scores to evaluate wastewater technologies at a sewage treatment works. The preliminary interviews with stakeholders highlighted that the existing investment decisions were primarily driven by financial cost and compliance whilst calling for a wider consideration of other criteria. A selection of assessment criteria and indicators were then proposed to compare seven treatment technologies at a sewage treatment works. The results of composite scores indicated that the baseline activated sludge process (ASP) was the best option for this study. Experience from the development process highlighted usability, stakeholder engagement and the organizational context should all be considered as part of the design and implementation of the sustainability assessment. The insights from this study provide a valuable practical foundation for applying a multi-criteria approach to perform sustainability assessments and inform asset management decisions in the water company.

An alternative approach to railway asset management value analysis: application to a UK railway corridor

Railway networks are complex systems, and the management of such systems is a challenging task for railway asset managers. It is their responsibility to ensure that the network delivers the highest level of performance for all stakeholders while adhering to strict safety regulations and financial constraints. Historically, reliability, availability, maintainability and safety (RAMS) analysis has been used to assess the performance and safety of railway networks. Nonetheless, there is a lack of consistency in approaches across the industry, with analysis often influenced by the key stakeholders at the time. This research demonstrates an application of an extended RAMS (ExRAMS) framework on the UK railway network. The ExRAMS framework aims to consolidate various extensions to the traditional RAMS approach into a single universal approach, which is beneficial to all stakeholders. This paper explores the data currently available within the rail industry and how these can be used to assess the ten metrics within the framework. The final part of the paper explores how the parameters within the ExRAMS framework can be used as the bases of a value analysis, which can then be used to assist with asset-management decisions.

Dealing With Data Uncertainty for Transformer Insulation System Health Index

Health index has been widely accepted as a powerful tool for monitoring the condition of power transformer insulation system based on various diagnostic parameters. While this approach has been extensively discussed in the literature, not much attention was given to provide effective solutions to the uncertainty in the used data. According to CIGRE 761, data quality issues may arise due to measurement accuracy as well as incompleteness and unavailability of the required data. Therefore, this article presents the implementation and evaluation of a certainty level model for transformer insulation system health index to deal with data uncertainty. The impact of data unavailability on the health index results is also investigated. Certainty level of the health index is determined by the criticality level of available data, and is reported along with the health index result. A method to handle unavailable data by predicting the oil interfacial tension (IFT) using Random Forest approach is also presented. The proposed certainty level model is designed to accommodate the predicted value of missed data into the health index model while considering its prediction accuracy. The robustness of the developed model is validated through its application in assessing the health condition of six in-service power transformers. The results indicate that by including the proposed certainty level and the prediction approach to eliminate the issue of uncertain and missed diagnostic data, an asset management decision can be taken on operating power transformer fleets with high level of confidence.

Designing a hybrid methodology for the Life Cycle Valuation of capital goods

Decision-makers are increasingly required to assess the value created by complex physical systems over their entire life cycle. The commonly applied Life Cycle Costing approach fails to fully capture value, as it is primarily aimed at costs, takes a reductionist approach, and does not account for continuously changing industrial environments. To address these shortcomings, the Life Cycle Valuation methodology is proposed, designed as a hybrid of LCC and Life Cycle Assessment. LCV facilitates the assessment of costs and benefits from multiple complementary perspectives and can be tailored to specific decision contexts, as demonstrated by applying LCV during Asset Management decision-making.

A Strategic Business Intelligence Framework for Sustainable Asset Management in Finance

Amidst the evolving landscape of finance, integrating sustainability principles into asset management stands as a pivotal pursuit for fostering long-term value creation. This research addresses the symbiotic relationship between business intelligence methodologies and sustainable asset management within the domain of finance. Leveraging advanced machine learning techniques including logistic regression, XGBoost, and CatBoost, this study delves into the exploration of sustainable finance practices and their implications for optimized asset management strategies. The study analyzes and models Asset data, aiming to understand the multifaceted dynamics and interdependencies shaping sustainable asset management decisions. Logistic regression serves as a foundation to model the relationships between variables, while XGBoost and CatBoost handle the complexities of categorical attributes, predicting outcomes related to sustainability metrics and financial performance indicators within the asset portfolio. Through comprehensive analyses and visualizations, this research illuminates critical insights into the influential factors driving sustainable asset management decisions. The findings underscore the significance of leveraging data-driven methodologies to optimize asset management strategies aligned with environmental, social, and governance considerations.