Engineering Asset Research Articles

Mechanical behaviour of additively manufactured lunar regolith simulant components

Additive manufacturing and its related techniques have frequently been put forward as a promising candidate for planetary in-situ manufacturing, from building life-sustaining habitats on the Moon to fabricating various replacements parts, aiming to support future extra-terrestrial human activity. This paper investigates the mechanical behaviour of lunar regolith simulant material components, which is a potential future space engineering material, manufactured by a laser-based powder bed fusion additive manufacturing system. The influence of laser energy input during processing was associated with the evolution of component porosity, measured via optical and scanning electron microscopy in combination with gas expansion pycnometry. The compressive strength performance and Vickers micro-hardness of the components were analysed and related back to the processing history and resultant microstructure of the lunar regolith simulant build material. Fabricated structures exhibited a relative porosity of 44–49% and densities ranging from 1.76 to 2.3 g cm−3, with a maximum compressive strength of 4.2 ± 0.1 MPa and elastic modulus of 287.3 ± 6.6 MPa, the former is comparable to a typical masonry clay brick (3.5 MPa). The additive manufacturing parts also had an average hardness value of 657 ± 14 HV0.05/15, better than borosilicate glass (580 HV). This study has shed significant insight into realising the potential of a laser-based powder bed fusion additive manufacturing process to deliver functional engineering assets via in-situ and abundant material sources that can be potentially used for future engineering applications in aerospace and astronautics.

Architecture, Design & Engineering – Archiving Digital Assets: Past, Present and Future

For decades, design in the worlds of architecture, design and engineering have been digital and the software tools to support the work operate under a business model of rapid change and proprietary output. This paper reports on the outcome of a two day Summit held at the Library of Congress in November 2017 (Designing the Future Landscape: Digital Architecture, Design & Engineering Assets) bringing together creators, archivists, researchers, project managers, and standards and guidelines developers to illuminate the issues and challenges for preserving and accessing this work product, to explore new research possibilities created by design as data, and to identify initiatives contributing to addressing issues of preservation and access. Like the event itself, this paper hopes to increase awareness of the challenges and issues, and to share and encourage actions and collaborations for preserving this material. An in-depth consolidation of the themes and issues from the Summit can be found in the report written by Aliza Leventhal for the Library of Congress released in March 2018 entitled: Designing the Future Landscape: Digital Architecture, Design & Engineering Assets.

Derivation of Life Cycle Cost Model for Selecting Optimum Decisions in Engineering Asset Management

It is common that interdepartmental conflict may arise in an organisation to select which decision to be taken, especially in the area of engineering asset management. In this paper, an engineering asset management related case study is discussed and a derivation of a life cycle cost model is proposed to assist finding the optimum decision. Another challenge in this study is a situation that the data and information required to calculate the total cost is unavailable. To deal with this situation, a cost comparison approach is proposed as well as a sensitivity analysis in order to select the optimum decision if the uncontrolled variable in the system nature changes. The result shows that the total cost model derived from the life cycle cost model is capable to assist selecting the optimum decision. A simple spreadsheet based Monte Carlo model is also developed to represent the randomness of the time to failure during the five years of time horizon of the analysis.

Scalable prognostic models for large-scale condition monitoring applications

ABSTRACTHigh-value engineering assets are often embedded with numerous sensing technologies that monitor and track their performance. Capturing physical and performance degradation entails the use of various types of sensors that generate massive amounts of multivariate data. Building a prognostic model for such large-scale datasets, however, often presents two key challenges: how to effectively fuse the degradation signals from a large number of sensors and how to make the model scalable to the large data size. To address the two challenges, this article presents a scalable semi-parametric statistical framework specifically designed for synthesizing and combining multistream sensor signals using two signal fusion algorithms developed from functional principal component analysis. Using the algorithms, we identify fused signal features and predict (in near real-time) the remaining lifetime of partially degraded systems using an adaptive functional (log)-location-scale regression modeling framework. We validate the proposed multi-sensor prognostic methodology using numerical and data-driven case studies.

Analysis of alternative actions for import substitution policy implementation in machine engineering complex

This paper discusses the considerations for production modernization, replacement of imported spare parts manufactured in-house and redistribution of items manufactured spare parts between managed societies, with a goal to improve the quality and reliability of products, to reduce its cost, and work. Noted that the process of acquiring new foreign equipment and modern materials are complicated. There are examples of restrictive measures of the sanctions impact on industrial products and the dynamics of dependency on imports of industrial products. We used analytic and statistical research methods. Authors conducted a comparative analysis of the engineering assets of «Metalloinvest» holding that showed that all businesses have an impressive fleet of metal-working equipment on all groups of machines on the basis of an analysis of the needs for them to transport spare parts.

An intelligent and improved density and distance-based clustering approach for industrial survey data classification

Engineering Asset Management (EAM) emphasizes on achieving sustainable business outcomes and competitive advantages by applying systematic and risk-based processes to decisions concerning an organization's physical assets. Nowadays, there is no specific method to evaluate performance of EAM and lack of benchmark to rank performance. To fill this gap, an improved density and distance-based clustering approach is proposed. The proposed approach is intelligent and efficient. It has largely simplified the current evaluating method so that the commitment in resources for manual data analyzing and performance ranking can be significantly reduced. Moreover, the proposed approach provides a basis on benchmarking for measuring and ranking the performance in Engineering Asset Management (EAM). Additionally, by using the intelligent approach, companies can avoid to pay expensive consultant fees for inviting external consultancy company to provide the necessary EAM auditing and performance benchmarking.

The adaptive reuse of industrial structures

Purpose The purpose of this paper is to investigate the feasibility of the adaptive reuse of a particular case study, and the evaluation of it within the context of international research done on similar projects. It aims to highlight the reuse potential of industrial structures by private developers, and the financial, environmental and social advantages that they could hold. Design/methodology/approach A literature review that explores these themes is followed by the post-occupancy analysis of the case study. Data were gathered primarily through interviews with key role players in the project, site visits and an analysis of the relevant project documentation. Findings While there are a number of international examples of the reuse of power stations in particular, these are mostly on a large scale, dependent mostly on government funding, lottery funding and donations and generally stripped of most of their machinery. The case study is a smaller-scale example which demonstrates that a project of this nature can be entirely funded by a private developer, that it can be sustainable and that it can be done while keeping most of the original machinery in place. The case study confirms a number of findings that are revealed in current research in the field, and it also shows the relative advantages of adaptive reuse when compared to new-build projects. Research limitations/implications There is fairly limited information and published research about adaptive reuse, especially in South Africa, so the paper builds on international knowledge on the subject while exposing a suitable local example. It is hoped that the study will not only lead to further research and post-occupancy analysis of similar projects in South Africa in particular, but also support international research that indicates the feasibility of adaptive reuse. Originality/value The Thesen Islands power station (or the Turbine Hotel as it is now known) could potentially be used as a precedent for similar redevelopments, and it could shed some light on the opportunities and constraints related to the management of fixed engineering assets.

Novel Bayesian inference on optimal parameters of support vector machines and its application to industrial survey data classification

Engineering Asset Management (EAM) is a recently attractive discipline and it aims to address valuable contributions of asset management to organization׳s success. As of today, there is no specific method to evaluate performance of EAM standards. This paper aims to fill this gap and rank performance of asset management automatically after conducting survey, instead of evaluating questionnaires, analyzing results and ranking performances with a tedious process. Hence, it is necessary to develop intelligent data classification to simplify the whole procedure. Among many supervised learning methods, support vector machine attracts much attention for binary classification problems and its extension, namely multiple support vector machines, is able to solve multiclass classification problems. It is crucial to find optimal parameters of support vector machines prior to their use for prediction of unknown testing data sets. In this paper, novel Bayesian inference on optimal parameters of support vector machines is proposed. Firstly, a state space model is constructed to find the relationship between parameters of support vector machines and guess cross-validation accuracy. Here, the guess cross-validation accuracy aims to prevent support vector machines from overfitting. Secondly, particle filter is introduced to iteratively find posterior probability density functions of the parameters of support vector machines. Then, optimal parameters of support vector machines can be found from the posterior probability density functions. Ultimately, survey data collected from industry are used to validate the effectiveness of the proposed Bayesian inference method. Comparisons with some randomly selected parameters are conducted to highlight the superiority of the proposed method. The results show that the proposed Bayesian inference method can result in both high training and testing accuracies.

London Underground – successful outcome of bridge assessment programme

London Underground (LU) has a large civil engineering asset portfolio of which bridges comprise approximately 8000 assets. The LU system, which was constructed by various private railway companies commencing in the early 1860s, continues to provide service today, with some bridges having therefore been in use for over 150 years. As a variety of railway companies were used for the bridge constructions, diverse designs exist across the LU network. Many record drawings have been destroyed over the years as part of space-saving initiatives and others lost during mergers and takeovers of the various railway companies. Furthermore, in the early 1980s LU lacked formal engineering standards and procedures for the effective asset management of its bridge stock. It was therefore decided to embark upon an assessment project to determine the extent, condition and load-carrying capacity of the company's bridges and structures. This paper will describe how pioneering inspection and assessment standards were devised. It will progress to outline the bridge assessment strategy, the formulation of a risk management system to address non-compliant bridges and how the final assessment results have enabled maintenance and capacity improvement works to be prioritised ( Figure 1 ) to ensure that the LU railway delivers a safe, efficient and reliable service.

A review of recent research advances on structural health monitoring in Western Australia

Structural Health Monitoring (SHM) has been attracting numerous research efforts around the world because it targets at monitoring structural conditions and performance to prevent catastrophic failure, and to provide quantitative data for engineers and infrastructure owners to design a reliable and economical asset management strategy. In the past decade, with supports from Australian Research Council (ARC), Cooperative Research Center for Infrastructure and Engineering Asset Management (CIEAM), CSIRO and industry partners, intensive research works have been conducted in the School of Civil, Environmental and Mining Engineering, University of Western Australia and Centre for Infrastructural Monitoring and Protection, Curtin University on various techniques of SHM. The researches include the development of hardware, software and various algorithms, such as various signal processing techniques for operational modal analysis, modal analysis toolbox, non-model based methods for assessing the shear connection in composite bridges and identifying the free spanning and supports conditions of pipelines, vibration based structural damage identification and model updating approaches considering uncertainty and noise effects, structural identification under moving loads, guided wave propagation technique for detecting debonding damage, and relative displacement sensors for SHM in composite and steel truss bridges. This paper aims at summarizing and reviewing the recent research advances on SHM of civil infrastructure in Western Australia.

THE ROLE OF INFORMAL AND FORMAL NETWORKS: HOW PROFESSIONALS CAN BE INNOVATIVE IN A CONSTRAINED FISCAL ENVIRONMENT

One way of circumventing issues associated with constricted resources is to use organisational social resources embedded within both formal and informal social networks to provide increased support for proactive and innovative employee behaviour. Social exchange theorists argue that informal working relationships with non-line management provides an additional power source alongside traditional management relationships to foster proactive and innovative behaviour even when resources are scarce. About 272 Australian engineers, asset managers and technical employees were surveyed from public and private sector organisations, and data was examined using structural equation modelling. The findings depict that formal and informal leader–employee, organisational–employee relationships explained almost 53% of resource adequacy and resource adequacy explained 60.5% of the proactive engineering asset management organisational culture and 28.1% of innovative behaviour. This paper contributes insight into another hierarchical workplace relationship that employees can use to gain access to resources, and be proactive and innovative in the workplace.

Investigating engineering asset management standards (PAS-55) adoption and performance evaluation in information management: case study in Hong Kong

Publicly available specification (PAS) 55, the new standard for asset management which was developed by the British Standards Institute (BSI). Although the standard showed the guidelines and doing lists, they did not specify how to evaluate their performance. In this research, it evaluated the results by selecting information management section in PAS-55, which investigated the standard implementation in asset information management in Hong Kong. It was to survey not only the real situation of asset management in information management adoption, but also analysed their effects and performance. Data were analysed by correlation, multiple regression and clustering analysis. We found that the effective asset information management system depended on four main elements, the last two factors had higher influence in significance level for standard application. Also the finding indicated information security was the most positive variable affected by the independent variable. Moreover, after clustering analysis, performance evaluation and ranking analysis were conducted.

Constitutive Modelling of Elastomeric Seal Material under Compressive Loading

Elastomers are used in numerous engineering applications such as sealing components, it is therefore important to devise a method that can accurately predict elastomers’ response to load. Many applications that employ the use of these materials subject them to a nonlinear large strain; therefore the simple Hooke’s law is not sufficient to describe their material behaviour. This paper presents an approach to obtain material properties of elastomer under compression loading, based on hyperelastic strain formulation, through experimental test and finite element modelling. The paper focuses on the isotropic incompressible behaviour exhibited by elastomers, and obtains strain energy functions that satisfy the characteristic properties of a hyperelastic model. Data obtained from compression test on a nitrile rubber (NBR) specimen were used as material input into ABAQUS®—a finite element analysis software. A least square fitting technique was used to determine the coefficients of various stable hyperelastic models, based on Drucker’s stability criteria within the software. The strain energy functions obtained concentrate on material parameters which are related to physical quantities of the material molecular network they are subjected to in practical application. The approach benefits from mathematical simplicity, and possesses the property of the deformation mode dependency. Furthermore, a model validation procedure using a step-by-step method for parameters estimation is explained. The work herein is a nonlinear finite element modelling process that leads to an optimal solution and can be employed not only for elastomeric seals, but also for similar engineering assets.

Preservation engineering assets developed from an oxidation predictive model

AbstractA previously developed model which effectively predicts the probability of olive oil reaching the end of its shelf-life within a certain time frame was tested for its response when the convective diffusion of oxygen through packaging material is taken in account. Darcy’s Law was used to correlate the packaging permeability with the oxygen flow through the packaging materials. Mass transport within the food-packaging system was considered transient and the relative one-dimensional differential equations along with appropriate initial and boundary conditions were numerically solved. When the Peclet (Pe) number was used to validate the significance of the oxygen transport mechanism through packaging, the model results confirmed the Arrhenius type dependency of diffusion, where the slope of the line per material actually indicated their –Ea/R. Furthermore, Pe could not be correlated to the hexanal produced in samples stored under light. Photo-oxidation has a significant role in the oxidative degradation of olive oil confirmed by the shelf-assessing test. The validity of our model for the oxygen diffusion driven systems, was also confirmed, for that reason the predictive boundaries were set. Results safely indicated the significance of applying a self-assessing process to confirm the packaging selection process for oxygen sensitive food via this model.

NPM and change management in asset management organisations

Purpose – Effective engineering asset management is essential in delivering public services safely whilst avoiding breakdowns and accidents. The purpose of this paper is to ensure asset safety and sustainability, public sector firms have to adopt new processes and practices. It is the role of supervisors to implement the changes, and as part of the new public management (NPM) public sector reforms, public sector asset managers have more discretionary power to implement further changes related to increased accountability. Design/methodology/approach – The paper explores the impact of management practices on supervisor-employee relationships and employees’ perception of autonomy, employees’ attitudes towards change and their perceptions of organisational culture within Australian public sector engineering asset management organisations, and in the context of NPM reforms and consequent changes in supervisory discretionary power. Social exchange theory provided the theoretical framework and a self-report surv...

Definition of Engineering Asset Management: A Review

The terminology of "Asset Management" (AM) has been widely used in many organizations. It has been generally accepted and defined as a cost effective approach for asset operation, maintenance, upgrade, and disposal. It covers many scopes and topics spanning from financial, IT, human resources, and also physical engineered assets. Engineering asset management (EAM) is the management of engineering assets and it provides guidelines on the effective usage of all the physical engineered assets within the organization. It is deemed essential to understand the concept of effective EAM and practices in an organization. This paper provides a review on the definition and concepts of AM and EAM with the aim of providing good insights and understanding on this topics.

What Constitutes a Clinical Engineering Asset?

What Constitutes a Clinical Engineering Asset?

Guiding principles for infrastructure climate change risk and adaptation studies

As global climate mitigation actions increasingly appear to be unable to curb global emissions, there has been a corresponding increase in climate adaptation planning undertaken by governments and communities. Along with an increasing number of adaptation studies, there has been an increasing complexity of adaptation studies as practitioners attempt to plan the adaptation of whole communities, cities and in some cases nations. These studies are commonly underpinned by increasingly complex climate change vulnerability studies that also attempt to encompass concepts such as resilience and adaptive capacity. Owners and operators of infrastructure assets and networks also need to consider climate change. This need has been met by an increasing number of climate change risk and adaptation studies of major infrastructure. The approaches used for these assessments are commonly derived from assessments of whole communities and policy development studies, and apply terminology that is often inconsistent with established engineering asset management methods and approaches. As a result the uptake of these studies has been less than desirable in many cases. To this end, the work presented here proposes a set of principles for undertaking the assessment of climate changes impacts on assets and infrastructure.

Criticality Analysis for Maintenance Purposes: A Study for Complex In-service Engineering Assets

The purpose of this paper is to establish a basis for a criticality analysis, considered here as a prerequisite, a first required step to review the current maintenance programs, of complex in-service engineering assets. Review is understood as a reality check, a testing of whether the current maintenance activities are well aligned to actual business objectives and needs. This paper describes an efficient and rational working process and a model resulting in a hierarchy of assets, based on risk analysis and cost–benefit principles, which will be ranked according to their importance for the business to meet specific goals. Starting from a multicriteria analysis, the proposed model converts relevant criteria impacting equipment criticality into a single score presenting the criticality level. Although detailed implementation of techniques like Root Cause Failure Analysis and Reliability Centered Maintenance will be recommended for further optimization of the maintenance activities, the reasons why criticality analysis deserves the attention of engineers and maintenance and reliability managers are precisely explained here. A case study is presented to help the reader understand the process and to operationalize the model. Copyright © 2015 John Wiley & Sons, Ltd.

Intelligent engineering asset management system for power transformer maintenance decision supports under various operating conditions

Large sized transformers are an important part of global power systems and industrial infrastructures. An unexpected failure of a power transformer can cause severe production damage and significant loss throughput the power grid. In order to prevent power facilities from malfunctions and breakdowns, the development of real-time monitoring and fault prediction tools are of great interests to both researches and practitioners. This research develops an intelligent engineering asset management system for power transformer maintenance. The system performs real-time monitoring of key parameters and uses data mining and fault prediction models to detect transformers’ potential failure under various operating conditions. Principal component analysis (PCA) and a back-propagation artificial neural network (BP-ANN) are the algorithms adopted for the prediction model. Historical industrial power transformer data from Taiwan and Australia are used to train and test the failure prediction models and to verify the proposed general methodology as comparative case studies. The PCA algorithm reduces the number of the primary dissolved gasses as the key factor values for BP-ANN prediction modeling inputs. The system yields effective predictions when verified using various operating condition data from Australia and Taiwan power companies. The accuracy rates are much higher when compared to the fault prediction results without using PCA. The intelligent system combining PCA and BP-ANN algorithms, developed in this research, can be adopted by asset managers in different regions to develop suitable maintenance and repair strategies for transformer failure preventions.