Machine prognostics based on health state probability estimation

The ability to accurately predict the remaining useful life of machine components is critical for machine continuous operation and can also improve productivity and enhance system’s safety. In condition-based maintenance (CBM), maintenance is performed based on information collected through condition monitoring and assessment of the machine health. Effective diagnostics and prognostics are important aspects of CBM for maintenance engineers to schedule a repair and to acquire replacement components before the components actually fail. Although a variety of prognostic methodologies have been reported recently, their application in industry is still relatively new and mostly focused on the prediction of specific component degradations. Furthermore, they required significant and sufficient number of fault indicators to accurately prognose the component faults. Hence, sufficient usage of health indicators in prognostics for the effective interpretation of machine degradation process is still required. Major challenges for accurate longterm prediction of remaining useful life (RUL) still remain to be addressed. Therefore, continuous development and improvement of a machine health management system and accurate long-term prediction of machine remnant life is required in real industry application. This thesis presents an integrated diagnostics and prognostics framework based on health state probability estimation for accurate and long-term prediction of machine remnant life. In the proposed model, prior empirical (historical) knowledge is embedded in the integrated diagnostics and prognostics system for classification of impending faults in machine system and accurate probability estimation of discrete degradation stages (health states). The methodology assumes that machine degradation consists of a series of degraded states (health states) which effectively represent the dynamic and stochastic process of machine failure. The estimation of discrete health state probability for the prediction of machine remnant life is performed using the ability of classification algorithms. To employ the appropriate classifier for health state probability estimation in the proposed model, comparative intelligent diagnostic tests were conducted using five different classifiers applied to the progressive fault data of three different faults in a high pressure liquefied natural gas (HP-LNG) pump. As a result of this comparison study, SVMs were employed in heath state probability estimation for the prediction of machine failure in this research. The proposed prognostic methodology has been successfully tested and validated using a number of case studies from simulation tests to real industry applications. The results from two actual failure case studies using simulations and experiments indicate that accurate estimation of health states is achievable and the proposed method provides accurate long-term prediction of machine remnant life. In addition, the results of experimental tests show that the proposed model has the capability of providing early warning of abnormal machine operating conditions by identifying the transitional states of machine fault conditions. Finally, the proposed prognostic model is validated through two industrial case studies. The optimal number of health states which can minimise the model training error without significant decrease of prediction accuracy was also examined through several health states of bearing failure. The results were very encouraging and show that the proposed prognostic model based on health state probability estimation has the potential to be used as a generic and scalable asset health estimation tool in industrial machinery.

Knowledge-based urban development of multimedia super corridor, Malaysia : an overview

In recent years, with the impact of the global knowledge economy, a more comprehensive urban development approach, so called 'knowledge-based urban development', has gained significant popularity. This paper discusses the critical connections among knowledge-based urban development strategies, knowledge-intensive industries and information and communication technology infrastructures. In particular, the research focuses on investigating the application of the knowledge-based urban development concept by discussing one of the South East Asia's large scale knowledge-based urban development manifestations of Malaysia's Multimedia Super Corridor. The paper scrutinises Malaysia's experience in the development and evolution of the Multimedia Super Corridor from the angle of knowledge-based urban development policy implementation, infrastructural implications, and actors involved in its development and management. This paper provides a number of lessons learned from the Multimedia Super Corridor on the orchestration of knowledge-based development that is a necessity for cities seeking successful knowledge city and knowledge economy transformations.

Enhancing professional ethics : service-learning in teacher education

The ethical conduct of professionals has been the focus of increasing scrutiny over the past several decades as members of the public, the media, professional bodies, and legislative authorities have struggled to define ethical behaviour in times of governmental change, increasing internationalisation, globalised communications, threats of terrorism, and the challenges of developments in science and medicine (e.g., Demmke & Bossaert, 2004). National governments and transnational bodies have responded to these concerns about ethics and corruption through measures such as the United Nations Convention Against Corruption (United Nations Office on Drugs and Crime, 2004), Transparency International’s annual corruption index (2010) and Queensland’s Public Sector Ethics Act 1994 (Queensland Parliament 1994). Similarly, academic interest in ethics and its application across a range of domains(e.g., business, health care, social welfare, criminal justice, law, journalism, defence, environment, and media) has also increased. To illustrate, in 1993, a non-partisan, non-profit national umbrella organisation, the Australian Association for Professional and Applied Ethics, was formed following a conference concerned with the teaching of ethics (http://www.arts.unsw.edu.au./aapae/about_aapae/about_aapae.htm), while a recent review of the Excellence in Research for Australian rankings of national and international academic journals revealed that 16 journals related to ethics had received the top ratings of A* or A (Australian Research Council, 2009). In this chapter we examine professional ethics and argue, with specific reference to the context of pre-service teacher education, that Service-learning is one way of enhancing emerging professionals’ understanding of ethics.

User profiling based on folksonomy information in Web 2.0 for personalized recommender systems

Information overload has become a serious issue for web users. Personalisation can provide effective solutions to overcome this problem. Recommender systems are one popular personalisation tool to help users deal with this issue. As the base of personalisation, the accuracy and efficiency of web user profiling affects the performances of recommender systems and other personalisation systems greatly. In Web 2.0, the emerging user information provides new possible solutions to profile users. Folksonomy or tag information is a kind of typical Web 2.0 information. Folksonomy implies the users‘ topic interests and opinion information. It becomes another source of important user information to profile users and to make recommendations. However, since tags are arbitrary words given by users, folksonomy contains a lot of noise such as tag synonyms, semantic ambiguities and personal tags. Such noise makes it difficult to profile users accurately or to make quality recommendations. This thesis investigates the distinctive features and multiple relationships of folksonomy and explores novel approaches to solve the tag quality problem and profile users accurately. Harvesting the wisdom of crowds and experts, three new user profiling approaches are proposed: folksonomy based user profiling approach, taxonomy based user profiling approach, hybrid user profiling approach based on folksonomy and taxonomy. The proposed user profiling approaches are applied to recommender systems to improve their performances. Based on the generated user profiles, the user and item based collaborative filtering approaches, combined with the content filtering methods, are proposed to make recommendations. The proposed new user profiling and recommendation approaches have been evaluated through extensive experiments. The effectiveness evaluation experiments were conducted on two real world datasets collected from Amazon.com and CiteULike websites. The experimental results demonstrate that the proposed user profiling and recommendation approaches outperform those related state-of-the-art approaches. In addition, this thesis proposes a parallel, scalable user profiling implementation approach based on advanced cloud computing techniques such as Hadoop, MapReduce and Cascading. The scalability evaluation experiments were conducted on a large scaled dataset collected from Del.icio.us website. This thesis contributes to effectively use the wisdom of crowds and expert to help users solve information overload issues through providing more accurate, effective and efficient user profiling and recommendation approaches. It also contributes to better usages of taxonomy information given by experts and folksonomy information contributed by users in Web 2.0.

Personalized recommender system based on item taxonomy and folksonomy

Item folksonomy or tag information is popularly available on the web now. However, since tags are arbitrary words given by users, they contain a lot of noise such as tag synonyms, semantic ambiguities and personal tags. Such noise brings difficulties to improve the accuracy of item recommendations. In this paper, we propose to combine item taxonomy and folksonomy to reduce the noise of tags and make personalized item recommendations. The experiments conducted on the dataset collected from Amazon.com demonstrated the effectiveness of the proposed approaches. The results suggested that the recommendation accuracy can be further improved if we consider the viewpoints and the vocabularies of both experts and users.

Mining users’ opinions based on item folksonomy and taxonomy for personalized recommender systems

Item folksonomy or tag information is a kind of typical and prevalent web 2.0 information. Item folksonmy contains rich opinion information of users on item classifications and descriptions. It can be used as another important information source to conduct opinion mining. On the other hand, each item is associated with taxonomy information that reflects the viewpoints of experts. In this paper, we propose to mine for users’ opinions on items based on item taxonomy developed by experts and folksonomy contributed by users. In addition, we explore how to make personalized item recommendations based on users’ opinions. The experiments conducted on real word datasets collected from Amazon.com and CiteULike demonstrated the effectiveness of the proposed approaches.

Parallel user profiling based on folksonomy for Large Scaled Recommender Systems : an implimentation of Cascading MapReduce

The Large scaled emerging user created information in web 2.0 such as tags, reviews, comments and blogs can be used to profile users’ interests and preferences to make personalized recommendations. To solve the scalability problem of the current user profiling and recommender systems, this paper proposes a parallel user profiling approach and a scalable recommender system. The current advanced cloud computing techniques including Hadoop, MapReduce and Cascading are employed to implement the proposed approaches. The experiments were conducted on Amazon EC2 Elastic MapReduce and S3 with a real world large scaled dataset from Del.icio.us website.

A hybrid recommender systems based on weighted tags

Social tags in web 2.0 are becoming another important information source to describe the content of items as well as to profile users’ topic preferences. However, as arbitrary words given by users, tags contains a lot of noise such as tag synonym and semantic ambiguity a large number personal tags that only used by one user, which brings challenges to effectively use tags to make item recommendations. To solve these problems, this paper proposes to use a set of related tags along with their weights to represent semantic meaning of each tag for each user individually. A hybrid recommendation generation approaches that based on the weighted tags are proposed. We have conducted experiments using the real world dataset obtained from Amazon.com. The experimental results show that the proposed approaches outperform the other state of the art approaches.

Development and pilot test of a peer-support based Cardiac-Diabetes Self-Management program : a study protocol

Background: People with cardiac disease and type 2 diabetes have higher hospital readmission rates (22%)compared to those without diabetes (6%). Self-management is an effective approach to achieve better health outcomes; however there is a lack of specifically designed programs for patients with these dual conditions. This project aims to extend the development and pilot test of a Cardiac-Diabetes Self-Management Program incorporating user-friendly technologies and the preparation of lay personnel to provide follow-up support. Methods/Design: A randomised controlled trial will be used to explore the feasibility and acceptability of the Cardiac-Diabetes Self-Management Program incorporating DVD case studies and trained peers to provide follow-up support by telephone and text-messaging. A total of 30 cardiac patients with type 2 diabetes will be randomised, either to the usual care group, or to the intervention group. Participants in the intervention group will received the Cardiac-Diabetes Self-Management Program in addition to their usual care. The intervention consists of three faceto- face sessions as well as telephone and text-messaging follow up. The face-to-face sessions will be provided by a trained Research Nurse, commencing in the Coronary Care Unit, and continuing after discharge by trained peers. Peers will follow up patients for up to one month after discharge using text messages and telephone support. Data collection will be conducted at baseline (Time 1) and at one month (Time 2). The primary outcomes include self-efficacy, self-care behaviour and knowledge, measured by well established reliable tools. Discussion: This paper presents the study protocol of a randomised controlled trial to pilot evaluates a Cardiac- Diabetes Self-Management program, and the feasibility of incorporating peers in the follow-ups. Results of this study will provide directions for using such mode in delivering a self-management program for patients with both cardiac condition and diabetes. Furthermore, it will provide valuable information of refinement of the intervention program.

Ethical dilemmas : a model to understand teacher practice

Over recent decades, the field of ethics has been the focus of increasing attention in teaching. This is not surprising given that teaching is a moral activity that is heavily values-laden. Because of this, teachers face ethical dilemmas in the course of their daily work. This paper presents an ethical decision-making model that helps to explain the decision-making processes that individuals or groups are likely to experience when confronted by an ethical dilemma. In order to make sense of the model, we put forward three short ethical dilemma scenarios facing teachers and apply the model to interpret them. Here we identify the critical incident, the forces at play that help to illuminate the incident, the choices confronting the individual and the implications of these choices for the individual, organization and community. Based on our analysis and the wider literature we identify several strategies that may help to minimize the impact of ethical dilemmas. These include the importance of sharing dilemmas with trusted others; having institutional structures in schools that lessen the emergence of harmful actions occurring; the necessity for individual teachers to articulate their own personal and professional ethics; acknowledging that dilemmas have multiple forces at play; the need to educate colleagues about specific issues; and the necessity of appropriate preparation and support for teachers. Of these strategies, providing support for teachers via professional development is explored more fully.

Raman spectroscopy of gallium- and zinc-based hydrotalcites

Insight into the unique structure of hydrotalcites (HTs) has been obtained using Raman spectroscopy. Gallium-contg. HTs of formula Zn4 Ga2(CO3)(OH)12 · xH2O (2:1 ZnGa-HT), Zn6 Ga2(CO3)(OH)16 · xH2O (3:1 ZnGa-HT) and Zn8 Ga2(CO3)(OH)18 · xH2O (4:1 ZnGa-HT) have been successfully synthesized and characterised by X-ray diffraction (XRD) and Raman spectroscopy. The d(003) spacing varies from 7.62 Å for the 2:1 ZnGa-HT to 7.64 Å for the 3:1 ZnGa-HT. The 4:1 ZnGa-HT showed a decrease in the d(003) spacing, compared to the 2:1 and 3:1 compds. Raman spectroscopy complemented with selected IR data has been used to characterize the synthesized gallium-contg. HTs. Raman bands obsd. at around 1050, 1060 and 1067 cm-1 are attributed to the sym. stretching modes of the (CO32-) units. Multiple ν3 (CO32-) antisym. stretching modes are found between 1350 and 1520 cm-1, confirming multiple carbonate species in the HT structure. The splitting of this mode indicates that the carbonate anion is in a perturbed state. Raman bands obsd. at 710 and 717 cm-1 and assigned to the ν4 (CO32-) modes support the concept of multiple carbonate species in the interlayer.

Synthesis and Raman spectroscopic characterisation of hydrotalcites based on the formula Ca6Al2(CO3)(OH)16· 4H2O

We have successfully synthesized hydrotalcites (HTs) contg. calcium, which are naturally occurring minerals. Insight into the unique structure of HTs has been obtained using a combination of X-ray diffraction (XRD) as well as IR and Raman spectroscopies. Calcium-contg. hydrotalcites (Ca-HTs) of the formula Ca4Al2(CO3)(OH)12·4H2O (2:1 Ca-HT) to Ca8Al2(CO3)(OH)20· 4H2O (4:1 Ca-HT) have been successfully synthesized and characterised by XRD and Raman spectroscopy. XRD has shown that 3:1 calcium HTs have the largest interlayer distance. Raman spectroscopy complemented with selected IR data has been used to characterize the synthesized Ca-HTs. The Raman bands obsd. at around 1086 and 1077 cm-1 were attributed to the ν1 sym. stretching modes of the (CO32-) units of calcite and carbonate intercalated into the HT interlayer. The corresponding ν3 CO32- antisym. stretching modes are found at around 1410 and 1475 cm-1.

An obsession with storytelling : conducting oral history interviews for creative writing

Anna Hirsch and Clare Dixon (2008, 190) state that creative writers’ ‘obsession with storytelling…might serve as an interdisciplinary tool for evaluating oral histories.’ This paper enters a dialogue with Hirsch and Dixon’s statement by documenting an interview methodology for a practice-led PhD project, The Artful Life Story: Oral History and Fiction, which investigates the fictionalising of oral history. ----- ----- Alistair Thomson (2007, 62) notes the interdisciplinary nature of oral history scholarship from the 1980s onwards. As a result, oral histories are being used and understood in a variety of arts-based settings. In such contexts, oral histories are not valued so much for their factual content but as sources that are at once dynamic, emotionally authentic and open to a multiplicity of interpretations. How can creative writers design and conduct interviews that reflect this emphasis? ----- ----- The paper briefly maps the growing trend of using oral histories in fiction and ethnographic novels, in order to establish the need to design interviews for arts-based contexts. I describe how I initially designed the interviews to suit the aims of my practice. Once in the field, however, I found that my original methods did not account for my experiences. I conclude with the resulting reflection and understanding that emerged from these problematic encounters, focusing on the technique of steered monologue (Scagliola 2010), sometimes referred to as the Biographic Narrative Interpretative Method (Wengraf 2001, Jones 2006).

HIFiRE re-entry observation using an image-based visual servoing system

This paper presents an image based visual servoing system that is intended to be used for tracking and obtaining scientific observations of the HIFiRE vehicles. The primary aim of this tracking platform is to acquire and track the thermal signature emitted from the surface of the vehicle during the re-entry phase of the mission using an infra-red camera. The implemented visual servoing scheme uses a classical image based approach to identify and track the target using visual kinematic control. The paper utilizes simulation and experimental results to show the tracking performance of the system using visual feedback. Discussions on current implementation and control techniques to further improve the performance of the system are also explored.