Nutritional analysis of gastric contents and body condition score at a single time point in feral horses in Australia

Objective: To determine the impact of a free-choice diet on nutritional intake and body condition of feral horses. Animals: Cadavers of 41 feral horses from 5 Australian locations. Procedures: Body condition score (BCS) was determined (scale of 1 to 9), and the stomach was removed from horses during postmortem examination. Stomach contents were analyzed for nutritional variables and macroelement and microelement concentrations. Data were compared among the locations and also compared with recommended daily intakes for horses. Results: Mean BCS varied by location; all horses were judged to be moderately thin. The BCS for males was 1 to 3 points higher than that of females. Amount of protein in the stomach contents varied from 4.3% to 14.9% and was significantly associated with BCS. Amounts of water-soluble carbohydrate and ethanol-soluble carbohydrate in stomach contents of feral horses from all 5 locations were higher than those expected for horses eating high-quality forage. Some macroelement and microelement concentrations were grossly excessive, whereas others were grossly deficient. There was no evidence of ill health among the horses. Conclusions and Clinical Relevance: Results suggested that the diet for several populations of feral horses in Australia appeared less than optimal. However, neither low BCS nor trace mineral deficiency appeared to affect survival of the horses. Additional studies on food sources in these regions, including analysis of water-soluble carbohydrate, ethanol-soluble carbohydrate, and mineral concentrations, are warranted to determine the provenance of such rich sources of nutrients. Determination of the optimal diet for horses may need revision.

A Systematic review of a statistical vacuum circuit breaker model for condition monitoring

Vacuum circuit breaker (VCB) overvoltage failure and its catastrophic failures during shunt reactor switching have been analyzed through computer simulations for multiple reignitions with a statistical VCB model found in the literature. However, a systematic review (SR) that is related to the multiple reignitions with a statistical VCB model does not yet exist. Therefore, this paper aims to analyze and explore the multiple reignitions with a statistical VCB model. It examines the salient points, research gaps and limitations of the multiple reignition phenomenon to assist with future investigations following the SR search. Based on the SR results, seven issues and two approaches to enhance the current statistical VCB model are identified. These results will be useful as an input to improve the computer modeling accuracy as well as the development of a reignition switch model with point-on-wave controlled switching for condition monitoring

Drying condition effects on fluidization quality and fluidization velocity at initial and final stages of cubical particulates for pet food

In this research fluidization behavior of cubical Bovine intestine samples was studied. Bovine intestine samples were heat pump dried at atmospheric pressure and at emperatures below and above the material freezing points. Experiments were conducted to study fluidization characteristics and drying kinetics at different drying conditions. Bovine particles were characterized according to Geldart classification and minimum fluidization velocity was calculated using Ergun Equation and generalized equation for all drying conditions at the beginning of the trials and end of the trials. Walli’s model was used to categorize stability of the fluidization at the beginning and end of the drying for each trial. Walli’s values determined were positive at the beginning and end of all trials indicating stable fluidisation at the beginning and end for each drying condition.

Damage identification and condition assessment of building structures using frequency response functions and neural networks

This thesis investigated the viability of using Frequency Response Functions in combination with Artificial Neural Network technique in damage assessment of building structures. The proposed approach can help overcome some of limitations associated with previously developed vibration based methods and assist in delivering more accurate and robust damage identification results. Excellent results are obtained for damage identification of the case studies proving that the proposed approach has been developed successfully.

Characterisation of combustion related acoustic emission sources for diesel engine condition monitoring

This thesis represents a major step forward in understanding the link between the development of combustion related faults in diesel engines and the generation of acoustic emissions. The findings presented throughout the thesis provide a foundation so that future diesel engine monitoring systems are able to more effectively detect and monitor developing faults. In undertaking this research knowledge concerning engine function and relevant failure mechanisms was combined with different modelling methods to generate a framework that was used to effectively identify fault related activity within acoustic emissions recorded from different engines.

Condition monitoring and operational decision making in semiconductor manufacturing

Today, the majority of semiconductor fabrication plants (fabs) conduct equipment preventive maintenance based on statistically-derived time- or wafer-count-based intervals. While these practices have had relative success in managing equipment availability and product yield, the cost, both in time and materials, remains high. Condition-based maintenance has been successfully adopted in several industries, where costs associated with equipment downtime range from potential loss of life to unacceptable affects to companies’ bottom lines. In this paper, we present a method for the monitoring of complex systems in the presence of multiple operating regimes. In addition, the new representation of degradation processes will be used to define an optimization procedure that facilitates concurrent maintenance and operational decision-making in a manufacturing system. This decision-making procedure metaheuristically maximizes a customizable cost function that reflects the benefits of production uptime, and the losses incurred due to deficient quality and downtime. The new degradation monitoring method is illustrated through the monitoring of a deposition tool operating over a prolonged period of time in a major fab, while the operational decision-making is demonstrated using simulated operation of a generic cluster tool.

Towards condition-invariant, top-down visual place recognition

In this paper we present a novel place recognition algorithm inspired by recent discoveries in human visual neuroscience. The algorithm combines intolerant but fast low resolution whole image matching with highly tolerant, sub-image patch matching processes. The approach does not require prior training and works on single images (although we use a cohort normalization score to exploit temporal frame information), alleviating the need for either a velocity signal or image sequence, differentiating it from current state of the art methods. We demonstrate the algorithm on the challenging Alderley sunny day – rainy night dataset, which has only been previously solved by integrating over 320 frame long image sequences. The system is able to achieve 21.24% recall at 100% precision, matching drastically different day and night-time images of places while successfully rejecting match hypotheses between highly aliased images of different places. The results provide a new benchmark for single image, condition-invariant place recognition.

Acoustic emission condition monitoring : an application for wind turbine fault detection

Low speed rotating machines which are the most critical components in drive train of wind turbines are often menaced by several technical and environmental defects. These factors contribute to mount the economic requirement for Health Monitoring and Condition Monitoring of the systems. When a defect is happened in such system result in reduced energy loss rates from related process and due to it Condition Monitoring techniques that detecting energy loss are very difficult if not possible to use. However, in the case of Acoustic Emission (AE) technique this issue is partly overcome and is well suited for detecting very small energy release rates. Acoustic Emission (AE) as a technique is more than 50 years old and in this new technology the sounds associated with the failure of materials were detected. Acoustic wave is a non-stationary signal which can discover elastic stress waves in a failure component, capable of online monitoring, and is very sensitive to the fault diagnosis. In this paper the history and background of discovering and developing AE is discussed, different ages of developing AE which include Age of Enlightenment (1950-1967), Golden Age of AE (1967-1980), Period of Transition (1980-Present). In the next section the application of AE condition monitoring in machinery process and various systems that applied AE technique in their health monitoring is discussed. In the end an experimental result is proposed by QUT test rig which an outer race bearing fault was simulated to depict the sensitivity of AE for detecting incipient faults in low speed high frequency machine.

Linking ecological condition and the soundscape in fragmented Australian forests

Natural landscapes are increasingly subjected to anthropogenic pressure and fragmentation resulting in reduced ecological condition. In this study we examined the relationship between ecological condition and the soundscape in fragmented forest remnants of south-east Queensland, Australia. The region is noted for its high biodiversity value and increased pressure associated with habitat fragmentation and urbanisation. Ten sites defined by a distinct open eucalypt forest community dominated by spotted gum (Corymbia citriodora ssp. variegata) were stratified based on patch size and patch connectivity. Each site underwent a series of detailed vegetation condition and landscape assessments, together with bird surveys and acoustic analysis using relative soundscape power. Univariate and multivariate analyses indicated that the measurement of relative soundscape power reflects ecological condition and bird species richness, and is dependent on the extent of landscape fragmentation. We conclude that acoustic monitoring technologies provide a cost effective tool for measuring ecological condition, especially in conjunction with established field observations and recordings.

Accurately defining the reference condition for summary biotic metrics: A comparison of four approaches

Protocols for bioassessment often relate changes in summary metrics that describe aspects of biotic assemblage structure and function to environmental stress. Biotic assessment using multimetric indices now forms the basis for setting regulatory standards for stream quality and a range of other goals related to water resource management in the USA and elsewhere. Biotic metrics are typically interpreted with reference to the expected natural state to evaluate whether a site is degraded. It is critical that natural variation in biotic metrics along environmental gradients is adequately accounted for, in order to quantify human disturbance-induced change. A common approach used in the IBI is to examine scatter plots of variation in a given metric along a single stream size surrogate and a fit a line (drawn by eye) to form the upper bound, and hence define the maximum likely value of a given metric in a site of a given environmental characteristic (termed the 'maximum species richness line' - MSRL). In this paper we examine whether the use of a single environmental descriptor and the MSRL is appropriate for defining the reference condition for a biotic metric (fish species richness) and for detecting human disturbance gradients in rivers of south-eastern Queensland, Australia. We compare the accuracy and precision of the MSRL approach based on single environmental predictors, with three regression-based prediction methods (Simple Linear Regression, Generalised Linear Modelling and Regression Tree modelling) that use (either singly or in combination) a set of landscape and local scale environmental variables as predictors of species richness. We compared the frequency of classification errors from each method against set biocriteria and contrast the ability of each method to accurately reflect human disturbance gradients at a large set of test sites. The results of this study suggest that the MSRL based upon variation in a single environmental descriptor could not accurately predict species richness at minimally disturbed sites when compared with SLR's based on equivalent environmental variables. Regression-based modelling incorporating multiple environmental variables as predictors more accurately explained natural variation in species richness than did simple models using single environmental predictors. Prediction error arising from the MSRL was substantially higher than for the regression methods and led to an increased frequency of Type I errors (incorrectly classing a site as disturbed). We suggest that problems with the MSRL arise from the inherent scoring procedure used and that it is limited to predicting variation in the dependent variable along a single environmental gradient.

A comparison of spatially explicit landscape representation methods and their relationship to stream condition

1. Biodiversity, water quality and ecosystem processes in streams are known to be influenced by the terrestrial landscape over a range of spatial and temporal scales. Lumped attributes (i.e. per cent land use) are often used to characterise the condition of the catchment; however, they are not spatially explicit and do not account for the disproportionate influence of land located near the stream or connected by overland flow. 2. We compared seven landscape representation metrics to determine whether accounting for the spatial proximity and hydrological effects of land use can be used to account for additional variability in indicators of stream ecosystem health. The landscape metrics included the following: a lumped metric, four inverse-distance-weighted (IDW) metrics based on distance to the stream or survey site and two modified IDW metrics that also accounted for the level of hydrologic activity (HA-IDW). Ecosystem health data were obtained from the Ecological Health Monitoring Programme in Southeast Queensland, Australia and included measures of fish, invertebrates, physicochemistry and nutrients collected during two seasons over 4 years. Linear models were fitted to the stream indicators and landscape metrics, by season, and compared using an information-theoretic approach. 3. Although no single metric was most suitable for modelling all stream indicators, lumped metrics rarely performed as well as other metric types. Metrics based on proximity to the stream (IDW and HA-IDW) were more suitable for modelling fish indicators, while the HA-IDW metric based on proximity to the survey site generally outperformed others for invertebrates, irrespective of season. There was consistent support for metrics based on proximity to the survey site (IDW or HA-IDW) for all physicochemical indicators during the dry season, while a HA-IDW metric based on proximity to the stream was suitable for five of the six physicochemical indicators in the post-wet season. Only one nutrient indicator was tested and results showed that catchment area had a significant effect on the relationship between land use metrics and algal stable isotope ratios in both seasons. 4. Spatially explicit methods of landscape representation can clearly improve the predictive ability of many empirical models currently used to study the relationship between landscape, habitat and stream condition. A comparison of different metrics may provide clues about causal pathways and mechanistic processes behind correlative relationships and could be used to target restoration efforts strategically.

Condition monitoring and fault diagnosis of diesel engines using instantaneous angular speed analysis

Continuous monitoring of diesel engine performance is critical for early detection of fault developments in an engine before they materialize into a functional failure. Instantaneous crank angular speed (IAS) analysis is one of a few nonintrusive condition monitoring techniques that can be utilized for such a task. Furthermore, the technique is more suitable for mass industry deployments than other non-intrusive methods such as vibration and acoustic emission techniques due to the low instrumentation cost, smaller data size and robust signal clarity since IAS is not affected by the engine operation noise and noise from the surrounding environment. A combination of IAS and order analysis was employed in this experimental study and the major order component of the IAS spectrum was used for engine loading estimation and fault diagnosis of a four-stroke four-cylinder diesel engine. It was shown that IAS analysis can provide useful information about engine speed variation caused by changing piston momentum and crankshaft acceleration during the engine combustion process. It was also found that the major order component of the IAS spectra directly associated with the engine firing frequency (at twice the mean shaft rotating speed) can be utilized to estimate engine loading condition regardless of whether the engine is operating at healthy condition or with faults. The amplitude of this order component follows a distinctive exponential curve as the loading condition changes. A mathematical relationship was then established in the paper to estimate the engine power output based on the amplitude of this order component of the IAS spectrum. It was further illustrated that IAS technique can be employed for the detection of a simulated exhaust valve fault in this study.

A test rig for the condition-based maintenance application on the traction chain of very high speed trains

The preventive maintenance of traction equipment for Very High Speed Trains (VHST) nowadays is becoming very expensive owing to the high complexity and quality of these components that require high reliability. An efficient maintenance approach like the Condition-Based Maintenance (CBM) should be implemented to reduce the costs. For this purpose, an experimental full-scale test rig for the CBM of VHST traction equipment has been designed to investigate in detail failures in the main mechanical components of system, i.e. motor, bearings and gearbox. The paper describes the main characteristics of this unique test rig, able to reproduce accurately the train operating conditions, including the relative movements of the motor, the gearbox and the wheel axle. Gearbox, bearing seats and motor are equipped by accelerometers, thermocouples, torque meter and other sensors in different positions. The testing results give important information about the most suitable sensor position and type to be installed for each component and show the effectiveness of the techniques used for the signal analysis in order to identify faults of the gearbox and motor bearings.

Breathe easier online : evaluation of a randomized controlled pilot trial of an internet-based intervention to improve well-being in children and adolescents with a chronic respiratory condition

Background Chronic respiratory illnesses are the most common group of childhood chronic health conditions and are overrepresented in socially isolated groups. Objective To conduct a randomized controlled pilot trial to evaluate the efficacy of Breathe Easier Online (BEO), an Internet-based problem-solving program with minimal facilitator involvement to improve psychosocial well-being in children and adolescents with a chronic respiratory condition. Methods We randomly assigned 42 socially isolated children and adolescents (18 males), aged between 10 and 17 years to either a BEO (final n = 19) or a wait-list control (final n = 20) condition. In total, 3 participants (2 from BEO and 1 from control) did not complete the intervention. Psychosocial well-being was operationalized through self-reported scores on depression symptoms and social problem solving. Secondary outcome measures included self-reported attitudes toward their illness and spirometry results. Paper-and-pencil questionnaires were completed at the hospital when participants attended a briefing session at baseline (time 1) and in their homes after the intervention for the BEO group or a matched 9-week time period for the wait-list group (time 2). Results The two groups were comparable at baseline across all demographic measures (all F < 1). For the primary outcome measures, there were no significant group differences on depression (P = .17) or social problem solving (P = .61). However, following the online intervention, those in the BEO group reported significantly lower depression (P = .04), less impulsive/careless problem solving (P = .01), and an improvement in positive attitude toward their illness (P = .04) compared with baseline. The wait-list group did not show these differences. Children in the BEO group and their parents rated the online modules very favorably. Conclusions Although there were no significant group differences on primary outcome measures, our pilot data provide tentative support for the feasibility (acceptability and user satisfaction) and initial efficacy of an Internet-based intervention for improving well-being in children and adolescents with a chronic respiratory condition. Trial registration Australian New Zealand Clinical Trials Registry number: ACTRN12610000214033;