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.

Experimenting with an intrusion detection system for encrypted networks

Network-based Intrusion Detection Systems (NIDSs) analyse network traffic to detect instances of malicious activity. Typically, this is only possible when the network traffic is accessible for analysis. With the growing use of Virtual Private Networks (VPNs) that encrypt network traffic, the NIDS can no longer access this crucial audit data. In this paper, we present an implementation and evaluation of our approach proposed in Goh et al. (2009). It is based on Shamir's secret-sharing scheme and allows a NIDS to function normally in a VPN without any modifications and without compromising the confidentiality afforded by the VPN.

A psychosocial approach to understanding young Australians' mobile phone behaviour

This thesis by publication contributes to our knowledge of psychological factors underlying a modern day phenomenon, young people’s mobile phone behaviour. Specifically, the thesis reports a PhD program of research which adopted a social psychological approach to explore mobile phone behaviour among young Australians aged between 15 and 24 years. A particular focus of the research program was to explore both the cognitive and behavioural aspects of young people’s mobile phone behaviour which for the purposes of this thesis is defined as mobile phone involvement. The research program comprised three separate stages which were developmental in nature, in that, the findings of each stage of the research program informed the next. The overarching goal of the program of research was to improve our understanding of the psychosocial factors influencing young people’s mobile phone behaviour. To achieve this overall goal, there were a number of aims to the research program which reflect the developmental nature of this thesis. Given the limited research into the mobile phone behaviour in Australia, the first two aims of the research program were to explore patterns of mobile phone behaviour among Australian youth and explore the social psychological factors relating to their mobile phone behaviour. Following this exploration, the research program sought to develop a measure which captures the cognitive and behavioural aspects of mobile phone behaviour. Finally, the research program aimed to examine and differentiate the psychosocial predictors of young people’s frequency of mobile phone use and their level of involvement with their mobile phone. Both qualitative and quantitative methodologies were used throughout the program of research. Five papers prepared during the three stages of the research program form the bulk of this thesis. The first stage of the research program was a qualitative investigation of young people’s mobile phone behaviour. Thirty-two young Australians participated in a series of focus groups in which they discussed their mobile phone behaviour. Thematic data analysis explored patterns of mobile phone behaviour among young people, developed an understanding of psychological factors influencing their use of mobile phones, and identified that symptoms of addiction were emerging in young people’s mobile phone behaviour. Two papers (Papers 1 and 2) emanated from this first stage of the research program. Paper 1 explored patterns of mobile phone behaviour and revealed that mobile phones were perceived as being highly beneficial to young people’s lives, with the ability to remain in constant contact with others being particularly valued. The paper also identified that symptoms of behavioural addiction including withdrawal, cognitive and behavioural salience, and loss of control, emerged in participants’ descriptions of their mobile phone behaviour. Paper 2 explored how young people’s need to belong and their social identity (two constructs previously unexplored in the context of mobile phone behaviour) related to their mobile phone behaviour. It was revealed that young people use their mobile phones to facilitate social attachments. Additionally, friends and peers influenced young people’s mobile phone behaviour; for example, their choice of mobile phone carrier and their most frequent type of mobile phone use. These papers laid the foundation for the further investigation of addictive patterns of behaviour and the role of social psychological factors on young people’s mobile behaviour throughout the research program. Stage 2 of the research program focussed on developing a new parsimonious measure of mobile phone behaviour, the Mobile Phone Involvement Questionnaire (MPIQ), which captured the cognitive and behavioural aspects of mobile phone use. Additionally, the stage included a preliminary exploration of factors influencing young people’s mobile phone behaviour. Participants (N = 946) completed a questionnaire which included a pool of items assessing symptoms of behavioural addiction, the uses and gratifications relating to mobile phone use, and self-identity and validation from others in the context of mobile phone behaviour. Two papers (Papers 3 & 4) emanated from the second stage of the research program. Paper 3 provided an important link between the qualitative and quantitative components of the research program. Qualitative data from Stage 1 indicated the reasons young people use their mobile phones and identified addictive characteristics present in young people’s mobile phone behaviour. Results of the quantitative study conducted in Stage 2 of the research program revealed the uses and gratifications relating to young people’s mobile phone behaviour and the effect of these gratifications on young people’s frequency of mobile phone use and three indicators of addiction, withdrawal, salience, and loss of control. Three major uses and gratifications: self (such as feeling good or as a fashion item), social (such as contacting friends), and security (such as use in an emergency) were found to underlie much of young people’s mobile phone behaviour. Self and social gratifications predicted young people’s frequency of mobile phone use and the three indicators of addiction but security gratifications did not. These results provided an important foundation for the inclusion of more specific psychosocial predictors in the later stages of the research program. Paper 4 reported the development of the mobile phone involvement questionnaire and a preliminary exploration of the effect of self-identity and validation from others on young people’s mobile phone behaviour. The MPIQ assessed a unitary construct and was a reliable measure amongst this cohort. Results found that self-identity influenced the frequency of young people’s use whereas self-identity and validation from others influenced their level of mobile phone involvement. These findings provided an important indication that, in addition to self factors, other people have a strong influence on young people’s involvement with their mobile phone and that mobile phone involvement is conceptually different to frequency of mobile phone use. Stage 3 of the research program empirically examined the psychosocial predictors of young people’s mobile behaviour and one paper, Paper 5, emanated from this stage. Young people (N = 292) from throughout Australia completed an online survey assessing the role of self-identity, ingroup norm, the need to belong, and self-esteem on their frequency of mobile phone use and their mobile phone involvement. Self-identity was the only psychosocial predictor of young people’s frequency of mobile phone use. In contrast, self-identity, ingroup norm, and need to belong all influenced young people’s level of involvement with their mobile phone. Additionally, the effect of self-esteem on young people’s mobile phone involvement was mediated by their need to belong. These results indicate that young people who perceive their mobile phone to be an integral part of their self-identity, who perceive that mobile phone is common amongst friends and peers, and who have a strong need for attachment to others, in some cases driven by a desire to enhance their self-esteem, are most likely to become highly involved with their mobile phones. Overall, this PhD program of research has provided an important contribution to our understanding of young Australians’ mobile phone behaviour. Results of the program have broadened our knowledge of factors influencing mobile phone behaviour beyond the approaches used in previous research. The use of various social psychological theories combined with a behavioural addiction framework provided a novel examination of young people’s mobile behaviour. In particular, the development of a new measure of mobile phone behaviour in the research program facilitated the differentiation of the psychosocial factors influencing frequency of young people’s mobile phone behaviour and their level of involvement with their mobile phone. Results of the research program indicate the important role that mobile phone behaviour plays in young people’s social development and also signals the characteristics of those people who may become highly involved with their mobile phone. Future research could build on this thesis by exploring whether mobile phones are affecting traditional social psychological processes and whether the results in this research program are generalisable to other cohorts and other communication technologies.

Analysis of authenticated encryption stream ciphers

Authenticated Encryption (AE) is the cryptographic process of providing simultaneous confidentiality and integrity protection to messages. AE is potentially more efficient than applying a two-step process of providing confidentiality for a message by encrypting the message and in a separate pass, providing integrity protection by generating a Message Authentication Code (MAC) tag. This paper presents results on the analysis of three AE stream ciphers submitted to the recently completed eSTREAM competition. We classify the ciphers based on the methods the ciphers use to provide authenticated encryption and discuss possible methods for mounting attacks on these ciphers.

CTCombine : rotating and combining CT data with an accurate detector model, to simulate radiotherapy portal imaging at non-zero beam angles

Established Monte Carlo user codes BEAMnrc and DOSXYZnrc permit the accurate and straightforward simulation of radiotherapy experiments and treatments delivered from multiple beam angles. However, when an electronic portal imaging detector (EPID) is included in these simulations, treatment delivery from non-zero beam angles becomes problematic. This study introduces CTCombine, a purpose-built code for rotating selected CT data volumes, converting CT numbers to mass densities, combining the results with model EPIDs and writing output in a form which can easily be read and used by the dose calculation code DOSXYZnrc. The geometric and dosimetric accuracy of CTCombine’s output has been assessed by simulating simple and complex treatments applied to a rotated planar phantom and a rotated humanoid phantom and comparing the resulting virtual EPID images with the images acquired using experimental measurements and independent simulations of equivalent phantoms. It is expected that CTCombine will be useful for Monte Carlo studies of EPID dosimetry as well as other EPID imaging applications.

Is the new age phenomenon connected to delusion-like experiences? Analysis of survey data from Australia

Recent studies have shown that delusion-like experiences (DLEs) are common among general populations. This study investigates whether the prevalence of these experiences are linked to the embracing of New Age thought. Logistic regression analyses were performed using data derived from a large community sample of young adults (N = 3777). Belief in a spiritual or higher power other than God was found to be significantly associated with endorsement of 16 of 19 items from Peters et al. (1999b) Delusional Inventory following adjustment for a range of potential confounders, while belief in God was associated with endorsement of four items. A New Age conception of the divine appears to be strongly associated with a wide range of DLEs. Further research is needed to determine a causal link between New Age philosophy and DLEs (e.g. thought disturbance, suspiciousness, and delusions of grandeur).

Eco-structure data forms : classification of data analysis using perceived design affordances for musical outcomes

This paper explores a method of comparative analysis and classification of data through perceived design affordances. Included is discussion about the musical potential of data forms that are derived through eco-structural analysis of musical features inherent in audio recordings of natural sounds. A system of classification of these forms is proposed based on their structural contours. The classifications include four primitive types; steady, iterative, unstable and impulse. The classification extends previous taxonomies used to describe the gestural morphology of sound. The methods presented are used to provide compositional support for eco-structuralism.

Elliptic curve algorithm integration in the secure shell transport layer

This document describes algorithms based on Elliptic Cryptography (ECC) for use within the Secure Shell (SSH) transport protocol. In particular, it specifies Elliptic Curve Diffie-Hellman (ECDH) key agreement, Elliptic Curve Menezes-Qu-Vanstone (ECMQV) key agreement, and Elliptic Curve Digital Signature Algorithm (ECDSA) for use in the SSH Transport Layer protocol.

Bayesian latent trait modeling of migraine symptom data

Definition of disease phenotype is a necessary preliminary to research into genetic causes of a complex disease. Clinical diagnosis of migraine is currently based on diagnostic criteria developed by the International Headache Society. Previously, we examined the natural clustering of these diagnostic symptoms using latent class analysis (LCA) and found that a four-class model was preferred. However, the classes can be ordered such that all symptoms progressively intensify, suggesting that a single continuous variable representing disease severity may provide a better model. Here, we compare two models: item response theory and LCA, each constructed within a Bayesian context. A deviance information criterion is used to assess model fit. We phenotyped our population sample using these models, estimated heritability and conducted genome-wide linkage analysis using Merlin-qtl. LCA with four classes was again preferred. After transformation, phenotypic trait values derived from both models are highly correlated (correlation = 0.99) and consequently results from subsequent genetic analyses were similar. Heritability was estimated at 0.37, while multipoint linkage analysis produced genome-wide significant linkage to chromosome 7q31-q33 and suggestive linkage to chromosomes 1 and 2. We argue that such continuous measures are a powerful tool for identifying genes contributing to migraine susceptibility.

Linkage and heritability analysis of migraine symptom groupings: A comparison of three different clustering methods on twin data

Migraine is a painful disorder for which the etiology remains obscure. Diagnosis is largely based on International Headache Society criteria. However, no feature occurs in all patients who meet these criteria, and no single symptom is required for diagnosis. Consequently, this definition may not accurately reflect the phenotypic heterogeneity or genetic basis of the disorder. Such phenotypic uncertainty is typical for complex genetic disorders and has encouraged interest in multivariate statistical methods for classifying disease phenotypes. We applied three popular statistical phenotyping methods—latent class analysis, grade of membership and grade of membership “fuzzy” clustering (Fanny)—to migraine symptom data, and compared heritability and genome-wide linkage results obtained using each approach. Our results demonstrate that different methodologies produce different clustering structures and non-negligible differences in subsequent analyses. We therefore urge caution in the use of any single approach and suggest that multiple phenotyping methods be used.

Multi-factor password-authenticated key exchange

We consider a new form of authenticated key exchange which we call multi-factor password-authenticated key exchange, where session establishment depends on successful authentication of multiple short secrets that are complementary in nature, such as a long-term password and a one-time response, allowing the client and server to be mutually assured of each other's identity without directly disclosing private information to the other party. Multi-factor authentication can provide an enhanced level of assurance in higher-security scenarios such as online banking, virtual private network access, and physical access because a multi-factor protocol is designed to remain secure even if all but one of the factors has been compromised. We introduce a security model for multi-factor password-authenticated key exchange protocols, propose an efficient and secure protocol called MFPAK, and provide a security argument to show that our protocol is secure in this model. Our security model is an extension of the Bellare-Pointcheval-Rogaway security model for password-authenticated key exchange and accommodates an arbitrary number of symmetric and asymmetric authentication factors.

Copyright and Data

ANDS Guides http://ands.org.au/guides/index.html These guides provide information about ANDS services and some fundamental issues in data-intensive research and research data management. These are not rules, prescriptions or proscriptions. They are guidelines and checklists to inform and broaden the range of possibilities for researchers, data managers, and research organisations.

Creative Commons & Data

This guide is relevant to anyone who owns copyright in data compilations or databases and wants to share their data openly, or to anyone who wants to use data under an open content licence. ANDS Guides are available at http://ands.org.au/guides/index.html.

Detecting and characterising malicious executable payloads

Buffer overflow vulnerabilities continue to prevail and the sophistication of attacks targeting these vulnerabilities is continuously increasing. As a successful attack of this type has the potential to completely compromise the integrity of the targeted host, early detection is vital. This thesis examines generic approaches for detecting executable payload attacks, without prior knowledge of the implementation of the attack, in such a way that new and previously unseen attacks are detectable. Executable payloads are analysed in detail for attacks targeting the Linux and Windows operating systems executing on an Intel IA-32 architecture. The execution flow of attack payloads are analysed and a generic model of execution is examined. A novel classification scheme for executable attack payloads is presented which allows for characterisation of executable payloads and facilitates vulnerability and threat assessments, and intrusion detection capability assessments for intrusion detection systems. An intrusion detection capability assessment may be utilised to determine whether or not a deployed system is able to detect a specific attack and to identify requirements for intrusion detection functionality for the development of new detection methods. Two novel detection methods are presented capable of detecting new and previously unseen executable attack payloads. The detection methods are capable of identifying and enumerating the executable payload’s interactions with the operating system on the targeted host at the time of compromise. The detection methods are further validated using real world data including executable payload attacks.

Modeling corneal surfaces with rational functions for high-speed videokeratoscopy data compression

High-speed videokeratoscopy is an emerging technique that enables study of the corneal surface and tear-film dynamics. Unlike its static predecessor, this new technique results in a very large amount of digital data for which storage needs become significant. We aimed to design a compression technique that would use mathematical functions to parsimoniously fit corneal surface data with a minimum number of coefficients. Since the Zernike polynomial functions that have been traditionally used for modeling corneal surfaces may not necessarily correctly represent given corneal surface data in terms of its optical performance, we introduced the concept of Zernike polynomial-based rational functions. Modeling optimality criteria were employed in terms of both the rms surface error as well as the point spread function cross-correlation. The parameters of approximations were estimated using a nonlinear least-squares procedure based on the Levenberg-Marquardt algorithm. A large number of retrospective videokeratoscopic measurements were used to evaluate the performance of the proposed rational-function-based modeling approach. The results indicate that the rational functions almost always outperform the traditional Zernike polynomial approximations with the same number of coefficients.