Intelligent prognostics of machinery health utilising suspended condition monitoring data

The ability to forecast machinery failure is vital to reducing maintenance costs, operation downtime and safety hazards. Recent advances in condition monitoring technologies have given rise to a number of prognostic models for forecasting machinery health based on condition data. Although these models have aided the advancement of the discipline, they have made only a limited contribution to developing an effective machinery health prognostic system. The literature review indicates that there is not yet a prognostic model that directly models and fully utilises suspended condition histories (which are very common in practice since organisations rarely allow their assets to run to failure); that effectively integrates population characteristics into prognostics for longer-range prediction in a probabilistic sense; which deduces the non-linear relationship between measured condition data and actual asset health; and which involves minimal assumptions and requirements. This work presents a novel approach to addressing the above-mentioned challenges. The proposed model consists of a feed-forward neural network, the training targets of which are asset survival probabilities estimated using a variation of the Kaplan-Meier estimator and a degradation-based failure probability density estimator. The adapted Kaplan-Meier estimator is able to model the actual survival status of individual failed units and estimate the survival probability of individual suspended units. The degradation-based failure probability density estimator, on the other hand, extracts population characteristics and computes conditional reliability from available condition histories instead of from reliability data. The estimated survival probability and the relevant condition histories are respectively presented as “training target” and “training input” to the neural network. The trained network is capable of estimating the future survival curve of a unit when a series of condition indices are inputted. Although the concept proposed may be applied to the prognosis of various machine components, rolling element bearings were chosen as the research object because rolling element bearing failure is one of the foremost causes of machinery breakdowns. Computer simulated and industry case study data were used to compare the prognostic performance of the proposed model and four control models, namely: two feed-forward neural networks with the same training function and structure as the proposed model, but neglected suspended histories; a time series prediction recurrent neural network; and a traditional Weibull distribution model. The results support the assertion that the proposed model performs better than the other four models and that it produces adaptive prediction outputs with useful representation of survival probabilities. This work presents a compelling concept for non-parametric data-driven prognosis, and for utilising available asset condition information more fully and accurately. It demonstrates that machinery health can indeed be forecasted. The proposed prognostic technique, together with ongoing advances in sensors and data-fusion techniques, and increasingly comprehensive databases of asset condition data, holds the promise for increased asset availability, maintenance cost effectiveness, operational safety and – ultimately – organisation competitiveness.

Stability and convergence of an implicit numerical method for the non-linear fractional reaction-subdiffusion process

In this paper, we consider the following non-linear fractional reaction–subdiffusion process (NFR-SubDP): Formula where f(u, x, t) is a linear function of u, the function g(u, x, t) satisfies the Lipschitz condition and 0Dt1–{gamma} is the Riemann–Liouville time fractional partial derivative of order 1 – {gamma}. We propose a new computationally efficient numerical technique to simulate the process. Firstly, the NFR-SubDP is decoupled, which is equivalent to solving a non-linear fractional reaction–subdiffusion equation (NFR-SubDE). Secondly, we propose an implicit numerical method to approximate the NFR-SubDE. Thirdly, the stability and convergence of the method are discussed using a new energy method. Finally, some numerical examples are presented to show the application of the present technique. This method and supporting theoretical results can also be applied to fractional integrodifferential equations.

Characteristic analysis for multisampled digital implementation of fixed-switching-frequency closed-loop modulation of voltage-source inverter

In this paper, a fixed-switching-frequency closed-loop modulation of a voltage-source inverter (VSI), upon the digital implementation of the modulation process, is analyzed and characterized. The sampling frequency of the digital processor is considered as an integer multiple of the modulation switching frequency. An expression for the determination of the modulation design parameter is developed for smooth modulation at a fixed switching frequency. The variation of the sampling frequency, switching frequency, and modulation index has been analyzed for the determination of the switching condition under closed loop. It is shown that the switching condition determined based on the continuous-time analysis of the closed-loop modulation will ensure smooth modulation upon the digital implementation of the modulation process. However, the stability properties need to be tested prior to digital implementation as they get deteriorated at smaller sampling frequencies. The closed-loop modulation index needs to be considered maximum while determining the design parameters for smooth modulation. In particular, a detailed analysis has been carried out by varying the control gain in the sliding-mode control of a two-level VSI. The proposed analysis of the closed-loop modulation of the VSI has been verified for the operation of a distribution static compensator. The theoretical results are validated experimentally on both single- and three-phase systems.

Conceptual Modelling in 3D Virtual Worlds for Process Communication

Traditionally, conceptual modelling of business processes involves the use of visual grammars for the representation of, amongst other things, activities, choices and events. These grammars, while very useful for experts, are difficult to understand by naive stakeholders. Annotations of such process models have been developed to assist in understanding aspects of these grammars via map-based approaches, and further work has looked at forms of 3D conceptual models. However, no one has sought to embed the conceptual models into a fully featured 3D world, using the spatial annotations to explicate the underlying model clearly. In this paper, we present an approach to conceptual process model visualisation that enhances a 3D virtual world with annotations representing process constructs, facilitating insight into the developed model. We then present a prototype implementation of a 3D Virtual BPMN Editor that embeds BPMN process models into a 3D world. We show how this gives extra support for tasks performed by the conceptual modeller, providing better process model communication to stakeholders..

Internetalisation : the Internet's influence on international market growth in the firm's outward internationalisation process

It has been suggested that the Internet is the most significant driver of international trade in recent years to the extent that the term =internetalisation‘ has been coined (Bell, Deans, Ibbotson & Sinkovics, 2001; Buttriss & Wilkinson, 2003). This term is used to describe the Internet‘s affect on the internationalisation process of the firm. Consequently, researchers have argued that the internationalisation process of the firm has altered due to the Internet, hence is in need of further investigation. However, as there is limited research and understanding, ambiguity remains in how the Internet has influenced international market growth. Thus, the purpose of this study was to explore how the Internet influences firms‘ internationalisation process, specifically, international market growth. To this end, Internet marketing and international market growth theories are used to illuminate this ambiguity in the body of knowledge. Thus, the research problem =How and why does the Internet influence international market growth of the firm’ is justified for investigation. To explore the research question a two-stage approach is used. Firstly, twelve case studies were used to evaluate key concepts, generate hypotheses and to develop a model of Internetalisation for testing. The participants held key positions within their firm, so that rich data could be drawn from international market growth decision makers. Secondly, a quantitative confirmation process analysed the identified themes or constructs, using two hundred and twenty four valid responses. Constructs were evaluated through an exploratory factor analysis, confirmatory factor analysis and structural equation modelling process. Structural equation modelling was used to test the model of =internetalisation‘ to examine the interrelationships between the internationalisation process components: information availability, information usage, interaction communication, international mindset, business relationship usage, psychic distance, the Internet intensity of the firm and international market growth. This study found that the Internet intensity of the firm mediates information availability, information usage, international mindset, and business relationships when firms grow in international markets. Therefore, these results provide empirical evidence that the Internet has a positive influence on international information, knowledge, entrepreneurship and networks and these in turn influence international market growth. The theoretical contributions are three fold. Firstly, the study identifies a holistic model of the impact the Internet has had on the outward internationalisation of the firm. This contribution extends the body of knowledge pertaining to Internet international marketing by mapping and confirming interrelationships between the Internet, internationalisation and growth concepts. Secondly, the study highlights the broad scope and accelerated rate of international market growth of firms. Evidence that the Internet influences the traditional and virtual networks for the pursuit of international market growth extends the current understanding. Thirdly, this study confirms that international information, knowledge, entrepreneurship and network concepts are valid in a single model. Thus, these three contributions identify constructs, measure constructs in a multi-item capacity, map interrelationships and confirm single holistic model of ‗internetalisation‘. The main practical contribution is that the findings identified information, knowledge and entrepreneurial opportunities for firms wishing to maximise international market growth. To capitalise on these opportunities suggestions are offered to assist firms to develop greater Internet intensity and internationalisation capabilities. From a policy perspective, educational institutions and government bodies need to promote more applied programs for Internet international marketing. The study provides future researchers with a platform of identified constructs and interrelationships related to internetalisation, with which to investigate. However, a single study has limitations of generalisability; thus, future research should replicate this study. Such replication or cross validation will assist in the verification of scales used in this research and enhance the validity of causal predications. Furthermore, this study was undertaken in the Australian outward-bound context. Research in other nations, as well as research into inbound internationalisation would be fruitful.

Means of achieving Business Process Management success factors

Business Process Management (BPM) in recent years has become a highest priority area for most organizations. Since this concept is multidisciplinary, success in this endeavour requires considering different factors. A number of studies have been conducted to identify these factors; however, most are limited to the introduction of high-level factors or to the identification of the means of success within only a specific context. This paper presents a holistic framework of success factors as well as the associated means for achieving success. This framework introduces nine factors, namely culture, leadership, communication, Information Technology, strategic alignment, people, project management, performance measurement and methodology. Each of these factors are characterized further by defining some sub-constructs and under each sub construct the means for achieving success are also introduced. This framework including means for achieving success can be useful for BPM project stakeholders in adequately planning the initiative and checking the progress during the implementation.

User-designer collaboration during the early stage of the design process

As user involvement becomes a necessary part of the product development process, various ways of accessing users' latent needs have been developed and studied. Reviews of literatures in user involvement and product development have revealed that accessing users' latent needs and transferring them into design process could be facilitated by effectively implementing user-designer collaboration during the early stage of the design process. In this paper, various types of user-designer collaboration were observed and then distinct characteristics of user-designer collaboration were classified into three categories. 1) Passive objectivity, 2) workplace democratisation, and 3) shared contexts were observed as strategies for better user-designer collaboration, which have been employed in the area of user-centred design, user participatory design and design for experiencing. Based on the literature review, this paper proposed a basic collaboration mechanism between the users and the designers during the early stage of the design process and then discussed how its mechanism will help to describe the interactions between the users and the designers during the user involvement sessions.

Stochastic modelling of financial time series with memory and multifractal scaling

Financial processes may possess long memory and their probability densities may display heavy tails. Many models have been developed to deal with this tail behaviour, which reflects the jumps in the sample paths. On the other hand, the presence of long memory, which contradicts the efficient market hypothesis, is still an issue for further debates. These difficulties present challenges with the problems of memory detection and modelling the co-presence of long memory and heavy tails. This PhD project aims to respond to these challenges. The first part aims to detect memory in a large number of financial time series on stock prices and exchange rates using their scaling properties. Since financial time series often exhibit stochastic trends, a common form of nonstationarity, strong trends in the data can lead to false detection of memory. We will take advantage of a technique known as multifractal detrended fluctuation analysis (MF-DFA) that can systematically eliminate trends of different orders. This method is based on the identification of scaling of the q-th-order moments and is a generalisation of the standard detrended fluctuation analysis (DFA) which uses only the second moment; that is, q = 2. We also consider the rescaled range R/S analysis and the periodogram method to detect memory in financial time series and compare their results with the MF-DFA. An interesting finding is that short memory is detected for stock prices of the American Stock Exchange (AMEX) and long memory is found present in the time series of two exchange rates, namely the French franc and the Deutsche mark. Electricity price series of the five states of Australia are also found to possess long memory. For these electricity price series, heavy tails are also pronounced in their probability densities. The second part of the thesis develops models to represent short-memory and longmemory financial processes as detected in Part I. These models take the form of continuous-time AR(∞) -type equations whose kernel is the Laplace transform of a finite Borel measure. By imposing appropriate conditions on this measure, short memory or long memory in the dynamics of the solution will result. A specific form of the models, which has a good MA(∞) -type representation, is presented for the short memory case. Parameter estimation of this type of models is performed via least squares, and the models are applied to the stock prices in the AMEX, which have been established in Part I to possess short memory. By selecting the kernel in the continuous-time AR(∞) -type equations to have the form of Riemann-Liouville fractional derivative, we obtain a fractional stochastic differential equation driven by Brownian motion. This type of equations is used to represent financial processes with long memory, whose dynamics is described by the fractional derivative in the equation. These models are estimated via quasi-likelihood, namely via a continuoustime version of the Gauss-Whittle method. The models are applied to the exchange rates and the electricity prices of Part I with the aim of confirming their possible long-range dependence established by MF-DFA. The third part of the thesis provides an application of the results established in Parts I and II to characterise and classify financial markets. We will pay attention to the New York Stock Exchange (NYSE), the American Stock Exchange (AMEX), the NASDAQ Stock Exchange (NASDAQ) and the Toronto Stock Exchange (TSX). The parameters from MF-DFA and those of the short-memory AR(∞) -type models will be employed in this classification. We propose the Fisher discriminant algorithm to find a classifier in the two and three-dimensional spaces of data sets and then provide cross-validation to verify discriminant accuracies. This classification is useful for understanding and predicting the behaviour of different processes within the same market. The fourth part of the thesis investigates the heavy-tailed behaviour of financial processes which may also possess long memory. We consider fractional stochastic differential equations driven by stable noise to model financial processes such as electricity prices. The long memory of electricity prices is represented by a fractional derivative, while the stable noise input models their non-Gaussianity via the tails of their probability density. A method using the empirical densities and MF-DFA will be provided to estimate all the parameters of the model and simulate sample paths of the equation. The method is then applied to analyse daily spot prices for five states of Australia. Comparison with the results obtained from the R/S analysis, periodogram method and MF-DFA are provided. The results from fractional SDEs agree with those from MF-DFA, which are based on multifractal scaling, while those from the periodograms, which are based on the second order, seem to underestimate the long memory dynamics of the process. This highlights the need and usefulness of fractal methods in modelling non-Gaussian financial processes with long memory.

The development of an evidentiary on-board massmonitoring application for heavy vehicles

This paper reports on the development of specifications for an on-board mass monitoring (OBM) application for regulatory requirements in Australia. An earlier paper reported on feasibility study and pilot testing program prior to the specification development [1]. Learnings from the pilot were used to refine this testing process and a full scale testing program was conducted from July to October 2008. The results from the full scale test and evidentiary implications are presented in this report. The draft specification for an evidentiary on-board mass monitoring application is currently under development.

Structural health monitoring of bridges using acoustic emission technology

Bridges are an important part of society's infrastructure and reliable methods are necessary to monitor them and ensure their safety and efficiency. Bridges deteriorate with age and early detection of damage helps in prolonging the lives and prevent catastrophic failures. Most bridges still in used today were built decades ago and are now subjected to changes in load patterns, which can cause localized distress and if not corrected can result in bridge failure. In the past, monitoring of structures was usually done by means of visual inspection and tapping of the structures using a small hammer. Recent advancements of sensors and information technologies have resulted in new ways of monitoring the performance of structures. This paper briefly describes the current technologies used in bridge structures condition monitoring with its prime focus in the application of acoustic emission (AE) technology in the monitoring of bridge structures and its challenges.

Concentrations of polybrominated diphenyl ethers (PBDEs) in matched samples of human milk, dust and indoor air

Polybrominated diphenyl ethers (PBDEs) are lipophilic, persistent pollutants found worldwide in environmental and human samples. Exposure pathways for PBDEs remain unclear but may include food, air and dust. The aim of this study was to conduct an integrated assessment of PBDE exposure and human body burden using 10 matched samples of human milk, indoor air and dust collected in 2007–2008 in Brisbane, Australia. In addition, temporal analysis was investigated comparing the results of the current study with PBDE concentrations in human milk collected in 2002–2003 from the same region. PBDEs were detected in all matrices and the median concentrations of BDEs -47 and -209 in human milk, air and dust were: 4.2 and 0.3 ng/g lipid; 25 and 7.8 pg/m3; and 56 and 291 ng/g dust, respectively. Significant correlations were observed between the concentrations of BDE-99 in air and human milk (r = 0.661, p = 0.038) and BDE-153 in dust and BDE-183 in human milk (r = 0.697, p = 0.025). These correlations do not suggest causal relationships — there is no hypothesis that can be offered to explain why BDE-153 in dust and BDE-183 in milk are correlated. The fact that so few correlations were found in the data could be a function of the small sample size, or because additional factors, such as sources of exposure not considered or measured in the study, might be important in explaining exposure to PBDEs. There was a slight decrease in PBDE concentrations from 2002–2003 to 2007–2008 but this may be due to sampling and analytical differences. Overall, average PBDE concentrations from these individual samples were similar to results from pooled human milk collected in Brisbane in 2002–2003 indicating that pooling may be an efficient, cost-effective strategy of assessing PBDE concentrations on a population basis. The results of this study were used to estimate an infant's daily PBDE intake via inhalation, dust ingestion and human milk consumption. Differences in PBDE intake of individual congeners from the different matrices were observed. Specifically, as the level of bromination increased, the contribution of PBDE intake decreased via human milk and increased via dust. As the impacts of the ban of the lower brominated (penta- and octa-BDE) products become evident, an increased use of the higher brominated deca-BDE product may result in dust making a greater contribution to infant exposure than it does currently. To better understand human body burden, further research is required into the sources and exposure pathways of PBDEs and metabolic differences influencing an individual's response to exposure. In addition, temporal trend analysis is necessary with continued monitoring of PBDEs in the human population as well as in the suggested exposure matrices of food, dust and air.

Real time detectionof airborne fungal spores and investigations into their dynamics in indoor air

Concern regarding the health effects of indoor air quality has grown in recent years, due to the increased prevalence of many diseases, as well as the fact that many people now spend most of their time indoors. While numerous studies have reported on the dynamics of aerosols indoors, the dynamics of bioaerosols in indoor environments are still poorly understood and very few studies have focused on fungal spore dynamics in indoor environments. Consequently, this work investigated the dynamics of fungal spores in indoor air, including fungal spore release and deposition, as well as investigating the mechanisms involved in the fungal spore fragmentation process. In relation to the investigation of fungal spore dynamics, it was found that the deposition rates of the bioaerosols (fungal propagules) were in the same range as the deposition rates of nonbiological particles and that they were a function of their aerodynamic diameters. It was also found that fungal particle deposition rates increased with increasing ventilation rates. These results (which are reported for the first time) are important for developing an understanding of the dynamics of fungal spores in the air. In relation to the process of fungal spore fragmentation, important information was generated concerning the airborne dynamics of the spores, as well as the part/s of the fungi which undergo fragmentation. The results obtained from these investigations into the dynamics of fungal propagules in indoor air significantly advance knowledge about the fate of fungal propagules in indoor air, as well as their deposition in the respiratory tract. The need to develop an advanced, real-time method for monitoring bioaerosols has become increasingly important in recent years, particularly as a result of the increased threat from biological weapons and bioterrorism. However, to date, the Ultraviolet Aerodynamic Particle Sizer (UVAPS, Model 3312, TSI, St Paul, MN) is the only commercially available instrument capable of monitoring and measuring viable airborne micro-organisms in real-time. Therefore (for the first time), this work also investigated the ability of the UVAPS to measure and characterise fungal spores in indoor air. The UVAPS was found to be sufficiently sensitive for detecting and measuring fungal propagules. Based on fungal spore size distributions, together with fluorescent percentages and intensities, it was also found to be capable of discriminating between two fungal spore species, under controlled laboratory conditions. In the field, however, it would not be possible to use the UVAPS to differentiate between different fungal spore species because the different micro-organisms present in the air may not only vary in age, but may have also been subjected to different environmental conditions. In addition, while the real-time UVAPS was found to be a good tool for the investigation of fungal particles under controlled conditions, it was not found to be selective for bioaerosols only (as per design specifications). In conclusion, the UVAPS is not recommended for use in the direct measurement of airborne viable bioaerosols in the field, including fungal particles, and further investigations into the nature of the micro-organisms, the UVAPS itself and/or its use in conjunction with other conventional biosamplers, are necessary in order to obtain more realistic results. Overall, the results obtained from this work on airborne fungal particle dynamics will contribute towards improving the detection capabilities of the UVAPS, so that it is capable of selectively monitoring and measuring bioaerosols, for which it was originally designed. This work will assist in finding and/or improving other technologies capable of the real-time monitoring of bioaerosols. The knowledge obtained from this work will also be of benefit in various other bioaerosol applications, such as understanding the transport of bioaerosols indoors.

Quality-aware business process management

Business Process Management (BPM) has emerged as a popular management approach in both Information Technology (IT) and management practice. While there has been much research on business process modelling and the BPM life cycle, there has been little attention given to managing the quality of a business process during its life cycle. This study addresses this gap by providing a framework for organisations to manage the quality of business processes during different phases of the BPM life cycle. This study employs a multi-method research design which is based on the design science approach and the action research methodology. During the design science phase, the artifacts to model a quality-aware business process were developed. These artifacts were then evaluated through three cycles of action research which were conducted within three large Australian-based organisations. This study contributes to the body of BPM knowledge in a number of ways. Firstly, it presents a quality-aware BPM life cycle that provides a framework on how quality can be incorporated into a business process and subsequently managed during the BPM life cycle. Secondly, it provides a framework to capture and model quality requirements of a business process as a set of measurable elements that can be incorporated into the business process model. Finally, it proposes a novel root cause analysis technique for determining the causes of quality issues within business processes.