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.

A grouping model for distributed pipeline assets maintenance decision

Distributed pipeline assets systems are crucial to society. The deterioration of these assets and the optimal allocation of limited budget for their maintenance correspond to crucial challenges for water utility managers. Decision makers should be assisted with optimal solutions to select the best maintenance plan concerning available resources and management strategies. Much research effort has been dedicated to the development of optimal strategies for maintenance of water pipes. Most of the maintenance strategies are intended for scheduling individual water pipe. Consideration of optimal group scheduling replacement jobs for groups of pipes or other linear assets has so far not received much attention in literature. It is a common practice that replacement planners select two or three pipes manually with ambiguous criteria to group into one replacement job. This is obviously not the best solution for job grouping and may not be cost effective, especially when total cost can be up to multiple million dollars. In this paper, an optimal group scheduling scheme with three decision criteria for distributed pipeline assets maintenance decision is proposed. A Maintenance Grouping Optimization (MGO) model with multiple criteria is developed. An immediate challenge of such modeling is to deal with scalability of vast combinatorial solution space. To address this issue, a modified genetic algorithm is developed together with a Judgment Matrix. This Judgment Matrix is corresponding to various combinations of pipe replacement schedules. An industrial case study based on a section of a real water distribution network was conducted to test the new model. The results of the case study show that new schedule generated a significant cost reduction compared with the schedule without grouping pipes.

Australian Library and Information Studies (LIS) researchers' ranking of LIS journals

The paper describes the processes and the outcomes of the ranking of LIS journal titles by Australia’s LIS researchers during 2007-8, firstly through the Australian federal government’s Research Quality Framework (RQF) process and then its replacement, the Excellence in Research for Australia (ERA) initiative. The requirement to rank the journals titles used came from discussions held at the RQF panel meeting held in February 2007 in Canberra, Australia. While it was recognised that the Web of Science (formerly ISI) journal impact approach of journal acceptance for measures of research quality and impact might not work for LIS, it was apparent that this model would be the default if no other ranking of journal titles became apparent. Although an increasing number of LIS and related discipline journals were appearing in the Web of Science listed rankings, the number was few and it was thus decided by the Australian LIS research community to undertake the ranking exercise.

Design of a preliminary error impact analysis model for spatial safety assessment of earthmoving operations

Regardless of technology benefits, safety planners still face difficulties explaining errors related to the use of different technologies and evaluating how the errors impact the performance of safety decision making. This paper presents a preliminary error impact analysis testbed to model object identification and tracking errors caused by image-based devices and algorithms and to analyze the impact of the errors for spatial safety assessment of earthmoving and surface mining activities. More specifically, this research designed a testbed to model workspaces for earthmoving operations, to simulate safety-related violations, and to apply different object identification and tracking errors on the data collected and processed for spatial safety assessment. Three different cases were analyzed based on actual earthmoving operations conducted at a limestone quarry. Using the testbed, the impacts of the errors were investigated for the safety planning purpose.

Maintenance strategy optimization using a continuous-state partially observable semi-Markov decision process

Due to the limitation of current condition monitoring technologies, the estimates of asset health states may contain some uncertainties. A maintenance strategy ignoring this uncertainty of asset health state can cause additional costs or downtime. The partially observable Markov decision process (POMDP) is a commonly used approach to derive optimal maintenance strategies when asset health inspections are imperfect. However, existing applications of the POMDP to maintenance decision-making largely adopt the discrete time and state assumptions. The discrete-time assumption requires the health state transitions and maintenance activities only happen at discrete epochs, which cannot model the failure time accurately and is not cost-effective. The discrete health state assumption, on the other hand, may not be elaborate enough to improve the effectiveness of maintenance. To address these limitations, this paper proposes a continuous state partially observable semi-Markov decision process (POSMDP). An algorithm that combines the Monte Carlo-based density projection method and the policy iteration is developed to solve the POSMDP. Different types of maintenance activities (i.e., inspections, replacement, and imperfect maintenance) are considered in this paper. The next maintenance action and the corresponding waiting durations are optimized jointly to minimize the long-run expected cost per unit time and availability. The result of simulation studies shows that the proposed maintenance optimization approach is more cost-effective than maintenance strategies derived by another two approximate methods, when regular inspection intervals are adopted. The simulation study also shows that the maintenance cost can be further reduced by developing maintenance strategies with state-dependent maintenance intervals using the POSMDP. In addition, during the simulation studies the proposed POSMDP shows the ability to adopt a cost-effective strategy structure when multiple types of maintenance activities are involved.

Preliminary characterisation of the surface of cartilage following exposure to saturated and unsaturated synthetic lipids

Articular cartilage is covered by a microscopic structure known as surface amorphous layer. This surface structure is often the first victim of attack during cartilage degeneration, thereby resulting in a gross impairment in cartilage function such as lubrication and load bearing. We hypothesize that incubation of degraded cartilage in solutions of different species of synthetic surface active phospholipids (saturated and unsaturated species) can remodel this lost surface structure. To test this hypothesis, the structural configuration of the surface of articular cartilage was studied and characterised with the lipid filled surface amorphous layer intact using the AFM. The results were then compared with those obtained following a systematic removal (delipidization) and replacement (relipidization) of this layer. Our results show that the unsaturated surfactant partially restored the lost surface amorphous layer while the saturated surfactant specie settled on the surface due to its poor solubility in aqueous solution.

Palliative care challenges : implications for nurses' practice in renal settings

The very act of withdrawing dialysis places renal nurses in a unique practice setting requiring a sudden shift in care delivery from one of providing Ife-sustaining, active treatment to that of palliation. The impact of this act on the renal nurse remains largely invisible. Minimal research has been conducted that explores the significant issues and challenges that exist for renal nurses in the delivery of palliation following withdrawal of dialysis treatment. This paper attempts to highlight the issues and challenges that do exist for renal nurses in providing palliation and the subsequent lack of available research knowledge to inform practice in the renal setting. It recommends further research be conducted into the renal setting so as to inform the development of appropriate education to support renal nurses practice in the future.

Minimising capacitive couplings and distributing copper losses in planar magnetic elements

Planar magnetic elements are becoming a replacement for their conventional rivals. Among the reasons supporting their application, is their smaller size. Taking less bulk in the electronic package is a critical advantage from the manufacturing point of view. The planar structure consists of the PCB copper tracks to generate the desired windings .The windings on each PCB layer could be connected in various ways to other winding layers to produce a series or parallel connection. These windings could be applied coreless or with a core depending on the application in Switched Mode Power Supplies (SMPS). Planar shapes of the tracks increase the effective conduction area in the windings, brings about more inductance compared to the conventional windings with the similar copper loss case. The problem arising from the planar structure of magnetic inductors is the leakage current between the layers generated by a pulse width modulated voltage across the inductor. This current value relies on the capacitive coupling between the layers, which in its turn depends on the physical parameters of the planar scheme. In order to reduce this electrical power dissipation due to the leakage current and Electromagnetic Interference (EMI), reconsideration in the planar structure might be effective. The aim of this research is to address problem of these capacitive coupling in planar layers and to find out a better structure for the planar inductance which offers less total capacitive coupling and thus less thermal dissipation from the leakage currents. Through Finite Element methods (FEM) several simulations have been carried out for various planar structures. The labs prototypes of these structures are built with the similar specification of the simulation cases. The capacitive couplings of the samples are determined with Spectrum Analyser whereby the test analysis verified the simulation results.

Understanding the role of knowledge in the practice of expert nephrology nurses in Australia

This paper, which is abstracted from a larger study into the acquisition and exercise of nephrology nursing expertise, aims to explore the role of knowledge in expert practice. Using grounded theory methodology, the study involved 17 registered nurses who were practicing in a metropolitan renal unit in New South Wales, Australia. Concurrent data collection and analysis was undertaken, incorporating participants' observations and interviews. Having extensive nephrology nursing knowledge was a striking characteristic of a nursing expert. Expert nurses clearly relied on and utilized extensive nephrology nursing knowledge to practice. Of importance for nursing, the results of this study indicate that domain-specific knowledge is a crucial feature of expert practice.

Group maintenance scheduling : a case study for a pipeline network

This paper presents a group maintenance scheduling case study for a water distributed network. This water pipeline network presents the challenge of maintaining aging pipelines with the associated increases in annual maintenance costs. The case study focuses on developing an effective maintenance plan for the water utility. Current replacement planning is difficult as it needs to balance the replacement needs under limited budgets. A Maintenance Grouping Optimization (MGO) model based on a modified genetic algorithm was utilized to develop an optimum group maintenance schedule over a 20-year cycle. The adjacent geographical distribution of pipelines was used as a grouping criterion to control the searching space of the MGO model through a Judgment Matrix. Based on the optimum group maintenance schedule, the total cost was effectively reduced compared with the schedules without grouping maintenance jobs. This optimum result can be used as a guidance to optimize the current maintenance plan for the water utility.

The influence of climate variability on hydrological processes and surface and groundwater hydrochemistry : the tropical upper roper river catchment, Northern Territory, Australia

The Upper Roper River is one of the Australia’s unique tropical rivers which have been largely untouched by development. The Upper Roper River catchment comprises the sub-catchments of the Waterhouse River and Roper Creek, the two tributaries of the Roper River. There is a complex geological setting with different aquifer types. In this seasonal system, close interaction between surface water and groundwater contributes to both streamflow and sustaining ecosystems. The interaction is highly variable between seasons. A conceptual hydrogeological model was developed to investigate the different hydrological processes and geochemical parameters, and determine the baseline characteristics of water resources of this pristine catchment. In the catchment, long term average rainfall is around 850 mm and is summer dominant which significantly influences the total hydrological system. The difference between seasons is pronounced, with high rainfall up to 600 mm/month in the wet season, and negligible rainfall in the dry season. Canopy interception significantly reduces the amount of effective rainfall because of the native vegetation cover in the pristine catchment. Evaporation exceeds rainfall the majority of the year. Due to elevated evaporation and high temperature in the tropics, at least 600 mm of annual rainfall is required to generate potential recharge. Analysis of 120 years of rainfall data trend helped define “wet” and “dry periods”: decreasing trend corresponds to dry periods, and increasing trend to wet periods. The period from 1900 to 1970 was considered as Dry period 1, when there were years with no effective rainfall, and if there was, the intensity of rainfall was around 300 mm. The period 1970 – 1985 was identified as the Wet period 2, when positive effective rainfall occurred in almost every year, and the intensity reached up to 700 mm. The period 1985 – 1995 was the Dry period 2, with similar characteristics as Dry period 1. Finally, the last decade was the Wet period 2, with effective rainfall intensity up to 800 mm. This variability in rainfall over decades increased/decreased recharge and discharge, improving/reducing surface water and groundwater quantity and quality in different wet and dry periods. The stream discharge follows the rainfall pattern. In the wet season, the aquifer is replenished, groundwater levels and groundwater discharge are high, and surface runoff is the dominant component of streamflow. Waterhouse River contributes two thirds and Roper Creek one third to Roper River flow. As the dry season progresses, surface runoff depletes, and groundwater becomes the main component of stream flow. Flow in Waterhouse River is negligible, the Roper Creek dries up, but the Roper River maintains its flow throughout the year. This is due to the groundwater and spring discharge from the highly permeable Tindall Limestone and tufa aquifers. Rainfall seasonality and lithology of both the catchment and aquifers are shown to influence water chemistry. In the wet season, dilution of water bodies by rainwater is the main process. In the dry season, when groundwater provides baseflow to the streams, their chemical composition reflects lithology of the aquifers, in particular the karstic areas. Water chemistry distinguishes four types of aquifer materials described as alluvium, sandstone, limestone and tufa. Surface water in the headwaters of the Waterhouse River, the Roper Creek and their tributaries are freshwater, and reflect the alluvium and sandstone aquifers. At and downstream of the confluence of the Roper River, river water chemistry indicates the influence of rainfall dilution in the wet season, and the signature of the Tindall Limestone and tufa aquifers in the dry. Rainbow Spring on the Waterhouse River and Bitter Spring on the Little Roper River (known as Roper Creek at the headwaters) discharge from the Tindall Limestone. Botanic Walk Spring and Fig Tree Spring discharge into the Roper River from tufa. The source of water was defined based on water chemical composition of the springs, surface and groundwater. The mechanisms controlling surface water chemistry were examined to define the dominance of precipitation, evaporation or rock weathering on the water chemical composition. Simple water balance models for the catchment have been developed. The important aspects to be considered in water resource planning of this total system are the naturally high salinity in the region, especially the downstream sections, and how unpredictable climate variation may impact on the natural seasonal variability of water volumes and surface-subsurface interaction.

Application of Mesenchymal Stem Cells for repair and regeneration of cartilage and bone

Australian efforts to provide orthopaedic surgeons with living, load-bearing scaffolds suitable for current joint (knee and hip) replacement surgery, non-union fracture repair, and miniscal and growth plate cartilage regeneration are being lead by teams at the Institute for Medical and Veterinary Science and Women's and Children's Hospital in Adelaide; the Peter MacCallum and St Vincent's Medical Research Institutes in Melbourne; and the Mater Medical Research Institute and new Institute for Health and Biomedical Innovation at QUT, Brisbane. In each case multidisciplinary teams are attempting to develop autologous living tissue constructs, utilising mesenchymal stem cells (MSC), with the intention of effecting seamless repair and regeneration of skeletal trauma and defects. In this article we will briefly review current knowledge of the phenotypic properties of MSC and discuss the potential therapeutic applications of these cells as exemplified by their use in cartilage repair and tissue engineering based approaches to the treatment of skeletal defects.