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.

Finding additional semantic entity information for search engines

Entity-oriented search has become an essential component of modern search engines. It focuses on retrieving a list of entities or information about the specific entities instead of documents. In this paper, we study the problem of finding entity related information, referred to as attribute-value pairs, that play a significant role in searching target entities. We propose a novel decomposition framework combining reduced relations and the discriminative model, Conditional Random Field (CRF), for automatically finding entity-related attribute-value pairs from free text documents. This decomposition framework allows us to locate potential text fragments and identify the hidden semantics, in the form of attribute-value pairs for user queries. Empirical analysis shows that the decomposition framework outperforms pattern-based approaches due to its capability of effective integration of syntactic and semantic features.

Competence in Intelligence Testing : A Training Model for Postgraduate Psychology Students

The assessment of intellectual ability is a core competency in psychology. The results of intelligence tests have many potential implications and are used frequently as the basis for decisions about educational placements, eligibility for various services, and admission to specific groups. Given the importance of intelligence test scores, accurate test administration and scoring are essential; yet there is evidence of unacceptably high rates of examiner error. This paper discusses competency and postgraduate training in intelligence testing and presents a training model for postgraduate psychology students. The model aims to achieve high levels of competency in intelligence testing through a structured method of training, practice and feedback that incorporates peer support, self-reflection and multiple methods for evaluating competency.

Training the public to collect oral histories of our community : the OHAA Queensland Chapter’s model

In a digital age, the skills required to undertake an oral history project have changed dramatically. For community groups, this shift can be new and exciting, but can also invoke feelings of anxiety when there is a gap in the skill set. Addressing this gap is one of Oral History Association of Australia, Queensland (OHAA Qld) main activities. This paper reports on the OHAA Qld chapter’s oral history workshop program, which was radically altered in 2011.

The QUT Training Model for Peer Leader Capacity Building

QUT has enacted a university-wide Peer Program’s Strategy which aims to improve student success and graduate outcomes. A component of this strategy is a training model providing relevant, quality-assured and timely training for all students who take on leadership roles. The training model is designed to meet the needs of the growing scale and variety of peer programs, and to recognise the multiple roles and programs in which students may be involved during their peer leader journey. The model builds peer leader capacity by offering centralised, beginning and ongoing training modules, delivered by in-house providers, covering topics which prepare students to perform their role safely, inclusively, accountably and skilfully. The model also provides efficiencies by differentiating between ‘core competency' and ‘program-specific’ modules, thus avoiding training duplication across multiple programs, and enabling training to be individually and flexibly formatted to suit the specific and unique needs of each program.

A sustainable model for supporting entrepreneurship in developing countries

Principal Topic Small and micro-enterprises are believed to play a significant part in economic growth and poverty allevition in developing countries. However, there are a range of issues that arise when looking at the support required for local enterprise development, the role of micro finance and sustainability. This paper explores the issues associated with the establishment and resourcing of micro-enterprise develoment and proposes a model of sustainable support of enterprise development in very poor developing economies, particularly in Africa. The purpose of this paper is to identify and address the range of issues raised by the literature and empirical research in Africa, regarding micro-finance and small business support, and to develop a model for sustainable support for enterprise development within a particular cultural and economic context. Micro-finance has become big business with a range of models - from those that operate on a strictly business basis to those that come from a philanthropic base. The models used grow from a range of philosophical and cultural perspectives. Entrepreneurship training is provided around the world. Success is often measured by the number involved and the repayment rates - which are very high, largely because of the lending models used. This paper will explore the range of options available and propose a model that can be implemented and evaluated in rapidly changing developing economies. Methodology/Key Propositions The research draws on entrepreneurial and micro-finance literature and empirical research undertaken in Mozambique, which lies along the Indian ocean sea border of Southern Africa. As a result of war and natural disasters over a prolonged period, there is little industry, primary industries are primitive and there is virtually no infrastructure. Mozambique is ranked as one of the poorest countries in the world. The conditions in Mozambique, though not identical, reflect conditions in many other parts of Africa. A numebr of key elements in the development of enterprises in poor countries are explored including: Impact of micro-finance Sustainable models of micro-finance Education and training Capacity building Support mechanisms Impact on poverty, families and the local economy Survival entrepreneurship versus growth entrepreneurship Transitions to the formal sector. Results and Implications The result of this study is the development of a model for providing intellectual and financial resources to micro-entrepreneurs in poor developing countries in a sustainable way. The model provides a base for ongoing research into the process of entrepreneurial growth in African developing economies. The research raises a numeber of issues regarding sustainability including the nature of the donor/recipient relationship, access to affordable resources, the impact of individual entrepreneurial activity on the local economny and the need for ongoing research to understand the whole process and its impact, intended and unintended.

How should a vocational education and training course be evaluated?

The course evaluation process used by a large VET provider was evaluated using guidelines suggested by the course evaluation literature and feedback obtained from multiple stakeholders. A modified model is presented as an exemplar for course evaluation in the VET sector.

Training young people as researchers to investigate engagement and disengagement in the middle years

This paper reports on the first stage of a study that used Young People as Researchers to investigate the phenomenon of middle-year student engagement and disengagement. The first stage of the study focused on a two-day workshop that provided training for students and teachers from four secondary schools in conducting research in their schools. An overview of the three stages is presented and the workshop procedures and example activities for Stage 1 of the Young People as Researchers model are described. Further to this, the paper reports on data collected in the workshop to address the research question: How do middleyear students describe engagement and disengagement?

Aligning policy with practice: an evaluation of vocational education and training in the library and information services sector

Vocational education and training for the library and information services (LIS) sector in Australia offers students the career pathway to become library technicians. Library technicians play a valuable role in drawing on sound practical knowledge and skills to support the delivery of library and information services that meet client needs. Over the past forty years, the Australian Library and Information Association (ALIA) has monitored the quality of library technician courses. Since 2005, ALIA has run national professional development days for library technician educators with the goal of establishing an alternative model for course recognition focusing on the process of peer review to benchmark good practice and stimulate continuous improvement in library technician education. This initial developmental work has culminated in 2009 with site visits to all library technician courses in Australia. The paper presents a whole-of-industry case study to critically review the work undertaken to date.

Decision support system for contractor pre-qualification : artificial neural network model

The selection criteria for contractor pre-qualification are characterized by the co-existence of both quantitative and qualitative data. The qualitative data is non-linear, uncertain and imprecise. An ideal decision support system for contractor pre-qualification should have the ability of handling both quantitative and qualitative data, and of mapping the complicated nonlinear relationship of the selection criteria, such that rational and consistent decisions can be made. In this research paper, an artificial neural network model was developed to assist public clients identifying suitable contractors for tendering. The pre-qualification criteria (variables) were identified for the model. One hundred and twelve real pre-qualification cases were collected from civil engineering projects in Hong Kong, and eighty-eight hypothetical pre-qualification cases were also generated according to the “If-then” rules used by professionals in the pre-qualification process. The results of the analysis totally comply with current practice (public developers in Hong Kong). Each pre-qualification case consisted of input ratings for candidate contractors’ attributes and their corresponding pre-qualification decisions. The training of the neural network model was accomplished by using the developed program, in which a conjugate gradient descent algorithm was incorporated for improving the learning performance of the network. Cross-validation was applied to estimate the generalization errors based on the “re-sampling” of training pairs. The case studies show that the artificial neural network model is suitable for mapping the complicated nonlinear relationship between contractors’ attributes and their corresponding pre-qualification (disqualification) decisions. The artificial neural network model can be concluded as an ideal alternative for performing the contractor pre-qualification task.

An effective approach to verbose queries using a limited dependencies language model

Intuitively, any `bag of words' approach in IR should benefit from taking term dependencies into account. Unfortunately, for years the results of exploiting such dependencies have been mixed or inconclusive. To improve the situation, this paper shows how the natural language properties of the target documents can be used to transform and enrich the term dependencies to more useful statistics. This is done in three steps. The term co-occurrence statistics of queries and documents are each represented by a Markov chain. The paper proves that such a chain is ergodic, and therefore its asymptotic behavior is unique, stationary, and independent of the initial state. Next, the stationary distribution is taken to model queries and documents, rather than their initial distri- butions. Finally, ranking is achieved following the customary language modeling paradigm. The main contribution of this paper is to argue why the asymptotic behavior of the document model is a better representation then just the document's initial distribution. A secondary contribution is to investigate the practical application of this representation in case the queries become increasingly verbose. In the experiments (based on Lemur's search engine substrate) the default query model was replaced by the stable distribution of the query. Just modeling the query this way already resulted in significant improvements over a standard language model baseline. The results were on a par or better than more sophisticated algorithms that use fine-tuned parameters or extensive training. Moreover, the more verbose the query, the more effective the approach seems to become.

Attitudes to Formal Business Training and Learning amongst Entrepreneurs in the Cultural Industries: Situated Business Learning through "Doing with Others."

Presents arguments supporting a social model of learning linked to situated learning and cultural capital. Critiques training methods used in cultural industries (arts, publishing, broadcasting, design, fashion, restaurants). Uses case study evidence to demonstrates inadequacies of formal training in this sector. (Contains 49 references.)