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.

Stability boundary analysis of the dynamic voltage restorer in weak systems with dynamic loads

In this contribution, a stability analysis for a dynamic voltage restorer (DVR) connected to a weak ac system containing a dynamic load is presented using continuation techniques and bifurcation theory. The system dynamics are explored through the continuation of periodic solutions of the associated dynamic equations. The switching process in the DVR converter is taken into account to trace the stability regions through a suitable mathematical representation of the DVR converter. The stability regions in the Thevenin equivalent plane are computed. In addition, the stability regions in the control gains space, as well as the contour lines for different Floquet multipliers, are computed. Besides, the DVR converter model employed in this contribution avoids the necessity of developing very complicated iterative map approaches as in the conventional bifurcation analysis of converters. The continuation method and the DVR model can take into account dynamics and nonlinear loads and any network topology since the analysis is carried out directly from the state space equations. The bifurcation approach is shown to be both computationally efficient and robust, since it eliminates the need for numerically critical and long-lasting transient simulations.

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.

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.

Cardiac health diagnosis using higher order spectra and support vector machine

The Electrocardiogram (ECG) is an important bio-signal representing the sum total of millions of cardiac cell depolarization potentials. It contains important insight into the state of health and nature of the disease afflicting the heart. Heart rate variability (HRV) refers to the regulation of the sinoatrial node, the natural pacemaker of the heart by the sympathetic and parasympathetic branches of the autonomic nervous system. The HRV signal can be used as a base signal to observe the heart's functioning. These signals are non-linear and non-stationary in nature. So, higher order spectral (HOS) analysis, which is more suitable for non-linear systems and is robust to noise, was used. An automated intelligent system for the identification of cardiac health is very useful in healthcare technology. In this work, we have extracted seven features from the heart rate signals using HOS and fed them to a support vector machine (SVM) for classification. Our performance evaluation protocol uses 330 subjects consisting of five different kinds of cardiac disease conditions. We demonstrate a sensitivity of 90% for the classifier with a specificity of 87.93%. Our system is ready to run on larger data sets.

From national integrity systems to global integrity systems

The primary focus of corruption studies and anti-corruption activism has been corruption within sovereign states. However, over the last twenty years ‘globalization’, the flow of money, goods, people and ideas across borders, has threatened to overwhelm the system of sovereign states. Much activity has moved outside the control of nation states at the same time as nation states have ‘deregulated’ and in so doing have transferred power from those exercising governmental power at the nominal behest of the majority of its citizens to those with greater wealth and/or greater knowledge in markets in which knowledge is typically asymmetric. It is now recognized that many governance problems have arisen because of globalisation and can only be addressed by global solutions. It must also be recognized that governance problems at the national level contribute to governance problems and the global level and vice versa. Nevertheless, many of the lessons learned in combating corruption at the national level are relevant to a globalized world – in particular, the need for ethics and leadership in addition to legal and institutional reform; the need to integrate these measures into integrity systems; and the awareness of corruption systems. These are applied to areas of concern within sustainable globalisation raised by the conference – including peace and security, extractive industries, climate change and sustainable banking.

Predicting residue-wise contact orders in proteins by support vector regression

Background The residue-wise contact order (RWCO) describes the sequence separations between the residues of interest and its contacting residues in a protein sequence. It is a new kind of one-dimensional protein structure that represents the extent of long-range contacts and is considered as a generalization of contact order. Together with secondary structure, accessible surface area, the B factor, and contact number, RWCO provides comprehensive and indispensable important information to reconstructing the protein three-dimensional structure from a set of one-dimensional structural properties. Accurately predicting RWCO values could have many important applications in protein three-dimensional structure prediction and protein folding rate prediction, and give deep insights into protein sequence-structure relationships. Results We developed a novel approach to predict residue-wise contact order values in proteins based on support vector regression (SVR), starting from primary amino acid sequences. We explored seven different sequence encoding schemes to examine their effects on the prediction performance, including local sequence in the form of PSI-BLAST profiles, local sequence plus amino acid composition, local sequence plus molecular weight, local sequence plus secondary structure predicted by PSIPRED, local sequence plus molecular weight and amino acid composition, local sequence plus molecular weight and predicted secondary structure, and local sequence plus molecular weight, amino acid composition and predicted secondary structure. When using local sequences with multiple sequence alignments in the form of PSI-BLAST profiles, we could predict the RWCO distribution with a Pearson correlation coefficient (CC) between the predicted and observed RWCO values of 0.55, and root mean square error (RMSE) of 0.82, based on a well-defined dataset with 680 protein sequences. Moreover, by incorporating global features such as molecular weight and amino acid composition we could further improve the prediction performance with the CC to 0.57 and an RMSE of 0.79. In addition, combining the predicted secondary structure by PSIPRED was found to significantly improve the prediction performance and could yield the best prediction accuracy with a CC of 0.60 and RMSE of 0.78, which provided at least comparable performance compared with the other existing methods. Conclusion The SVR method shows a prediction performance competitive with or at least comparable to the previously developed linear regression-based methods for predicting RWCO values. In contrast to support vector classification (SVC), SVR is very good at estimating the raw value profiles of the samples. The successful application of the SVR approach in this study reinforces the fact that support vector regression is a powerful tool in extracting the protein sequence-structure relationship and in estimating the protein structural profiles from amino acid sequences.

A Krylov-based finite state projection algorithm for solving the chemical master equation arising in the discrete modelling of biological systems

Biochemical reactions underlying genetic regulation are often modelled as a continuous-time, discrete-state, Markov process, and the evolution of the associated probability density is described by the so-called chemical master equation (CME). However the CME is typically difficult to solve, since the state-space involved can be very large or even countably infinite. Recently a finite state projection method (FSP) that truncates the state-space was suggested and shown to be effective in an example of a model of the Pap-pili epigenetic switch. However in this example, both the model and the final time at which the solution was computed, were relatively small. Presented here is a Krylov FSP algorithm based on a combination of state-space truncation and inexact matrix-vector product routines. This allows larger-scale models to be studied and solutions for larger final times to be computed in a realistic execution time. Additionally the new method computes the solution at intermediate times at virtually no extra cost, since it is derived from Krylov-type methods for computing matrix exponentials. For the purpose of comparison the new algorithm is applied to the model of the Pap-pili epigenetic switch, where the original FSP was first demonstrated. Also the method is applied to a more sophisticated model of regulated transcription. Numerical results indicate that the new approach is significantly faster and extendable to larger biological models.

The HarmonyGrid : music, space and performance in grid music systems

This research explores music in space, as experienced through performing and music-making with interactive systems. It explores how musical parameters may be presented spatially and displayed visually with a view to their exploration by a musician during performance. Spatial arrangements of musical components, especially pitches and harmonies, have been widely studied in the literature, but the current capabilities of interactive systems allow the improvisational exploration of these musical spaces as part of a performance practice. This research focuses on quantised spatial organisation of musical parameters that can be categorised as grid music systems (GMSs), and interactive music systems based on them. The research explores and surveys existing and historical uses of GMSs, and develops and demonstrates the use of a novel grid music system designed for whole body interaction. Grid music systems provide plotting of spatialised input to construct patterned music on a two-dimensional grid layout. GMSs are navigated to construct a sequence of parametric steps, for example a series of pitches, rhythmic values, a chord sequence, or terraced dynamic steps. While they are conceptually simple when only controlling one musical dimension, grid systems may be layered to enable complex and satisfying musical results. These systems have proved a viable, effective, accessible and engaging means of music-making for the general user as well as the musician. GMSs have been widely used in electronic and digital music technologies, where they have generally been applied to small portable devices and software systems such as step sequencers and drum machines. This research shows that by scaling up a grid music system, music-making and musical improvisation are enhanced, gaining several advantages: (1) Full body location becomes the spatial input to the grid. The system becomes a partially immersive one in four related ways: spatially, graphically, sonically and musically. (2) Detection of body location by tracking enables hands-free operation, thereby allowing the playing of a musical instrument in addition to “playing” the grid system. (3) Visual information regarding musical parameters may be enhanced so that the performer may fully engage with existing spatial knowledge of musical materials. The result is that existing spatial knowledge is overlaid on, and combined with, music-space. Music-space is a new concept produced by the research, and is similar to notions of other musical spaces including soundscape, acoustic space, Smalley's “circumspace” and “immersive space” (2007, 48-52), and Lotis's “ambiophony” (2003), but is rather more textural and “alive”—and therefore very conducive to interaction. Music-space is that space occupied by music, set within normal space, which may be perceived by a person located within, or moving around in that space. Music-space has a perceivable “texture” made of tensions and relaxations, and contains spatial patterns of these formed by musical elements such as notes, harmonies, and sounds, changing over time. The music may be performed by live musicians, created electronically, or be prerecorded. Large-scale GMSs have the capability not only to interactively display musical information as music representative space, but to allow music-space to co-exist with it. Moving around the grid, the performer may interact in real time with musical materials in music-space, as they form over squares or move in paths. Additionally he/she may sense the textural matrix of the music-space while being immersed in surround sound covering the grid. The HarmonyGrid is a new computer-based interactive performance system developed during this research that provides a generative music-making system intended to accompany, or play along with, an improvising musician. This large-scale GMS employs full-body motion tracking over a projected grid. Playing with the system creates an enhanced performance employing live interactive music, along with graphical and spatial activity. Although one other experimental system provides certain aspects of immersive music-making, currently only the HarmonyGrid provides an environment to explore and experience music-space in a GMS.

Fiber Bragg Grating sensors for railway systems

Fiber Bragg grating (FBG) sensor technology has been attracting substantial industrial interests for the last decade. FBG sensors have seen increasing acceptance and widespread use for structural sensing and health monitoring applications in composites, civil engineering, aerospace, marine, oil & gas, and smart structures. One transportation system that has been benefitted tremendously from this technology is railways, where it is of the utmost importance to understand the structural and operating conditions of rails as well as that of freight and passenger service cars to ensure safe and reliable operation. Fiberoptic sensors, mostly in the form of FBGs, offer various important characteristics, such as EMI/RFI immunity, multiplexing capability, and very long-range interrogation (up to 230 km between FBGs and measurement unit), over the conventional electrical sensors for the distinctive operational conditions in railways. FBG sensors are unique from other types of fiber-optic sensors as the measured information is wavelength-encoded, which provides self-referencing and renders their signals less susceptible to intensity fluctuations. In addition, FBGs are reflective sensors that can be interrogated from either end, providing redundancy to FBG sensing networks. These two unique features are particularly important for the railway industry where safe and reliable operations are the major concerns. Furthermore, FBGs are very versatile and transducers based on FBGs can be designed to measure a wide range of parameters such as acceleration and inclination. Consequently, a single interrogator can deal with a large number of FBG sensors to measure a multitude of parameters at different locations that spans over a large area.

A comparison of two commercially available motion capture systems for gait analysis : high end vs low-cost

In 1999 Richards compared the accuracy of commercially available motion capture systems commonly used in biomechanics. Richards identified that in static tests the optical motion capture systems generally produced RMS errors of less than 1.0 mm. During dynamic tests, the RMS error increased to up to 4.2 mm in some systems. In the last 12 years motion capture systems have continued to evolve and now include high-resolution CCD or CMOS image sensors, wireless communication, and high full frame sampling frequencies. In addition to hardware advances, there have also been a number of advances in software, which includes improved calibration and tracking algorithms, real time data streaming, and the introduction of the c3d standard. These advances have allowed the system manufactures to maintain a high retail price in the name of advancement. In areas such as gait analysis and ergonomics many of the advanced features such as high resolution image sensors and high sampling frequencies are not required due to the nature of the task often investigated. Recently Natural Point introduced low cost cameras, which on face value appear to be suitable as at very least a high quality teaching tool in biomechanics and possibly even a research tool when coupled with the correct calibration and tracking software. The aim of the study was therefore to compare both the linear accuracy and quality of angular kinematics from a typical high end motion capture system and a low cost system during a simple task.

An integrated approach for precise road reconstruction from aerial imagery and LiDAR data

Accurate and detailed road models play an important role in a number of geospatial applications, such as infrastructure planning, traffic monitoring, and driver assistance systems. In this thesis, an integrated approach for the automatic extraction of precise road features from high resolution aerial images and LiDAR point clouds is presented. A framework of road information modeling has been proposed, for rural and urban scenarios respectively, and an integrated system has been developed to deal with road feature extraction using image and LiDAR analysis. For road extraction in rural regions, a hierarchical image analysis is first performed to maximize the exploitation of road characteristics in different resolutions. The rough locations and directions of roads are provided by the road centerlines detected in low resolution images, both of which can be further employed to facilitate the road information generation in high resolution images. The histogram thresholding method is then chosen to classify road details in high resolution images, where color space transformation is used for data preparation. After the road surface detection, anisotropic Gaussian and Gabor filters are employed to enhance road pavement markings while constraining other ground objects, such as vegetation and houses. Afterwards, pavement markings are obtained from the filtered image using the Otsu's clustering method. The final road model is generated by superimposing the lane markings on the road surfaces, where the digital terrain model (DTM) produced by LiDAR data can also be combined to obtain the 3D road model. As the extraction of roads in urban areas is greatly affected by buildings, shadows, vehicles, and parking lots, we combine high resolution aerial images and dense LiDAR data to fully exploit the precise spectral and horizontal spatial resolution of aerial images and the accurate vertical information provided by airborne LiDAR. Objectoriented image analysis methods are employed to process the feature classiffcation and road detection in aerial images. In this process, we first utilize an adaptive mean shift (MS) segmentation algorithm to segment the original images into meaningful object-oriented clusters. Then the support vector machine (SVM) algorithm is further applied on the MS segmented image to extract road objects. Road surface detected in LiDAR intensity images is taken as a mask to remove the effects of shadows and trees. In addition, normalized DSM (nDSM) obtained from LiDAR is employed to filter out other above-ground objects, such as buildings and vehicles. The proposed road extraction approaches are tested using rural and urban datasets respectively. The rural road extraction method is performed using pan-sharpened aerial images of the Bruce Highway, Gympie, Queensland. The road extraction algorithm for urban regions is tested using the datasets of Bundaberg, which combine aerial imagery and LiDAR data. Quantitative evaluation of the extracted road information for both datasets has been carried out. The experiments and the evaluation results using Gympie datasets show that more than 96% of the road surfaces and over 90% of the lane markings are accurately reconstructed, and the false alarm rates for road surfaces and lane markings are below 3% and 2% respectively. For the urban test sites of Bundaberg, more than 93% of the road surface is correctly reconstructed, and the mis-detection rate is below 10%.

Improved sugar cane juice clarification by understanding calcium oxide-phosphate-sucrose systems

It is accepted that the efficiency of sugar cane clarification is closely linked with sugar juice composition (including suspended or insoluble impurities), the inorganic phosphate content, the liming condition and type, and the interactions between the juice components. These interactions are not well understood, particularly those between calcium, phosphate, and sucrose in sugar cane juice. Studies have been conducted on calcium oxide (CaO)/phosphate/sucrose systems in both synthetic and factory juices to provide further information on the defecation process (i.e., simple liming to effect impurity removal) and to identify an effective clarification process that would result in reduced scaling of sugar factory evaporators, pans, and centrifugals. Results have shown that a two-stage process involving the addition of lime saccharate to a set juice pH followed by the addition of sodium hydroxide to a final juice pH or a similar two-stage process where the order of addition of the alkalis is reversed prior to clarification reduces the impurity loading of the clarified juice compared to that of the clarified juice obtained by the conventional defecation process. The treatment process showed reductions in CaO (27% to 50%) and MgO (up to 20%) in clarified juices with no apparent loss in juice clarity or increase in residence time of the mud particles compared to those in the conventional process. There was also a reduction in the SiO2 content. However, the disadvantage of this process is the significant increase in the Na2O content.

Ontology-based knowledge management for enterprise systems

Companies face the challenges of expanding their markets, improving products, services and processes, and exploiting intellectual capital in a dynamic network. Therefore, more companies are turning to an Enterprise System (ES). Knowledge management (KM) has also received considerable attention and is continuously gaining the interest of industry, enterprises, and academia. For ES, KM can provide support across the entire lifecycle, from selection and implementation to use. In addition, it is also recognised that an ontology is an appropriate methodology to accomplish a common consensus of communication, as well as to support a diversity of KM activities, such as knowledge repository, retrieval, sharing, and dissemination. This paper examines the role of ontology-based KM for ES (OKES) and investigates the possible integration of ontology-based KM and ES. The authors develop a taxonomy as a framework for understanding OKES research. In order to achieve the objective of this study, a systematic review of existing research was conducted. Based on a theoretical framework of the ES lifecycle, KM, KM for ES, ontology, and ontology-based KM, guided by the framework of study, a taxonomy for OKES is established.

Decision support for the safe design and operation of unmanned aircraft systems

Unmanned Aircraft Systems (UAS) describe a diverse range of aircraft that are operated without a human pilot on-board. Unmanned aircraft range from small rotorcraft, which can fit in the palm of your hand, through to fixed wing aircraft comparable in size to that of a commercial passenger jet. The absence of a pilot on-board allows these aircraft to be developed with unique performance capabilities facilitating a wide range of applications in surveillance, environmental management, agriculture, defence, and search and rescue. However, regulations relating to the safe design and operation of UAS first need to be developed before the many potential benefits from these applications can be realised. According to the International Civil Aviation Organization (ICAO), a Risk Management Process (RMP) should support all civil aviation policy and rulemaking activities (ICAO 2009). The RMP is described in International standard, ISO 31000:2009 (ISO, 2009a). This standard is intentionally generic and high-level, providing limited guidance on how it can be effectively applied to complex socio-technical decision problems such as the development of regulations for UAS. Through the application of principles and tools drawn from systems philosophy and systems engineering, this thesis explores how the RMP can be effectively applied to support the development of safety regulations for UAS. A sound systems-theoretic foundation for the RMP is presented in this thesis. Using the case-study scenario of a UAS operation over an inhabited area and through the novel application of principles drawn from general systems modelling philosophy, a consolidated framework of the definitions of the concepts of: safe, risk and hazard is made. The framework is novel in that it facilitates the representation of broader subjective factors in an assessment of the safety of a system; describes the issues associated with the specification of a system-boundary; makes explicit the hierarchical nature of the relationship between the concepts and the subsequent constraints that exist between them; and can be evaluated using a range of analytic or deliberative modelling techniques. Following the general sequence of the RMP, the thesis explores the issues associated with the quantified specification of safety criteria for UAS. A novel risk analysis tool is presented. In contrast to existing risk tools, the analysis tool presented in this thesis quantifiably characterises both the societal and individual risk of UAS operations as a function of the flight path of the aircraft. A novel structuring of the risk evaluation and risk treatment decision processes is then proposed. The structuring is achieved through the application of the Decision Support Problem Technique; a modelling approach that has been previously used to effectively model complex engineering design processes and to support decision-making in relation to airspace design. The final contribution made by this thesis is in the development of an airworthiness regulatory framework for civil UAS. A novel "airworthiness certification matrix" is proposed as a basis for the definition of UAS "Part 21" regulations. The outcome airworthiness certification matrix provides a flexible, systematic and justifiable method for promulgating airworthiness regulations for UAS. In addition, an approach for deriving "Part 1309" regulations for UAS is presented. In contrast to existing approaches, the approach presented in this thesis facilitates a traceable and objective tailoring of system-level reliability requirements across the diverse range of UAS operations. The significance of the research contained in this thesis is clearly demonstrated by its practical real world outcomes. Industry regulatory development groups and the Civil Aviation Safety Authority have endorsed the proposed airworthiness certification matrix. The risk models have also been used to support research undertaken by the Australian Department of Defence. Ultimately, it is hoped that the outcomes from this research will play a significant part in the shaping of regulations for civil UAS, here in Australia and around the world.