67 resultados para on-line condition monitoring


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By means of evidence-based practice, this paper describes the residential-school component of an accredited online (distance education) undergraduate engineering program in Australia, with a particular focus on how the residential school program is implemented at freshman year. During these residential schools, activities were organised around the respective engineering courses undertaken by students during the semester. Elements considered suitable and worthwhile for inclusion in residential-school programs included: • In-person engagement with academic lecturers, • Practical and laboratory learning activities, • Presentations and interaction with guest speakers from industry, • Industry-based site visits, • Engagement in sole and group-based learning and assessment activities on campus, and • Social interaction with other students. After running pilot residential schools for two years, it was found that a workable format consisted in a two-week residential experience in the first semester, linked to two key freshman courses, Fundamentals of Technology Management, and Engineering Physics. On-campus and online students’ academic grades were compared for both courses over the years 2005 to 2012. We found that for physics lab, on-campus students’ grades tended to be higher than those for online students, and vice versa for technology management. We also conclude that when carefully designed, residential schools for online students do enhance learning for both online students and their on-campus counterparts.

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The dynamic characteristics of gas bubbles in fluidized beds are important to determine the heat and mass transfer rates at component surfaces and the treated profiles of components. They also have great impact on the components’ structural, mechanical and physical properties. However, it has been very difficult to monitor those characteristics dynamically. In this paper, a specifically designed fluidized bed was introduced to facilitate the capturing of its dynamic characteristics and a new video image processing and analysis algorithm was developed. The algorithm is robust and adaptive in terms of locating both bubbles and components in beds with a single or multiple components. It has many advantages in dynamic characterization of gas bubbles and monitoring component treatment. By using this algorithm, the properties of gas bubbles over any period of time can be accurately obtained. This technology will provide a potential on-line dynamic monitoring and quality control system for the chemical heat treatment processes with fluidized beds.

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A major challenge facing freshwater ecologists and managers is the development of models that link stream ecological condition to catchment scale effects, such as land use. Previous attempts to make such models have followed two general approaches. The bottom-up approach employs mechanistic models, which can quickly become too complex to be useful. The top-down approach employs empirical models derived from large data sets, and has often suffered from large amounts of unexplained variation in stream condition.

We believe that the lack of success of both modelling approaches may be at least partly explained by scientists considering too wide a breadth of catchment type. Thus, we believe that by stratifying large sets of catchments into groups of similar types prior to modelling, both types of models may be improved. This paper describes preliminary work using a Bayesian classification software package, ‘Autoclass’ (Cheeseman and Stutz 1996) to create classes of catchments within the Murray Darling Basin based on physiographic data.

Autoclass uses a model-based classification method that employs finite mixture modelling and trades off model fit versus complexity, leading to a parsimonious solution. The software provides information on the posterior probability that the classification is ‘correct’ and also probabilities for alternative classifications. The importance of each attribute in defining the individual classes is calculated and presented, assisting description of the classes. Each case is ‘assigned’ to a class based on membership probability, but the probability of membership of other classes is also provided. This feature deals very well with cases that do not fit neatly into a larger class. Lastly, Autoclass requires the user to specify the measurement error of continuous variables.

Catchments were derived from the Australian digital elevation model. Physiographic data werederived from national spatial data sets. There was very little information on measurement errors for the spatial data, and so a conservative error of 5% of data range was adopted for all continuous attributes. The incorporation of uncertainty into spatial data sets remains a research challenge.

The results of the classification were very encouraging. The software found nine classes of catchments in the Murray Darling Basin. The classes grouped together geographically, and followed altitude and latitude gradients, despite the fact that these variables were not included in the classification. Descriptions of the classes reveal very different physiographic environments, ranging from dry and flat catchments (i.e. lowlands), through to wet and hilly catchments (i.e. mountainous areas). Rainfall and slope were two important discriminators between classes. These two attributes, in particular, will affect the ways in which the stream interacts with the catchment, and can thus be expected to modify the effects of land use change on ecological condition. Thus, realistic models of the effects of land use change on streams would differ between the different types of catchments, and sound management practices will differ.

A small number of catchments were assigned to their primary class with relatively low probability. These catchments lie on the boundaries of groups of catchments, with the second most likely class being an adjacent group. The locations of these ‘uncertain’ catchments show that the Bayesian classification dealt well with cases that do not fit neatly into larger classes.

Although the results are intuitive, we cannot yet assess whether the classifications described in this paper would assist the modelling of catchment scale effects on stream ecological condition. It is most likely that catchment classification and modelling will be an iterative process, where the needs of the model are used to guide classification, and the results of classifications used to suggest further refinements to models.

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We show how in-line Raman spectroscopy can be used to monitor both reactant and product concentrations for a heterogeneously catalysed Suzuki cross reaction operating in continuous flow. The flow system consisted of an HPLC pump to drive a homogeneous mixture of the reactants (4-bromobenzonitrile, phenylboronic acid, and potassium carbonate) through an oven heated (80°C) palladium catalyst immobilised on a silica monolith. A custom built PTFE in-line flow cell with a quartz window enabled the coupling of an Ocean Optics Raman spectrometer probe to monitor both the reactants and product (4-cyanobiphenyl). Calibration was based on obtaining multivariate spectral data in the range 1530 cm–1 and 1640 cm–1 and using partial least-squares regression (PLSR) to obtain a calibration model which was validated using gas chromatography–mass spectrometry (GCMS) analysis. In-line Raman monitoring of the reactant and product concentrations enable (i) determination of reaction kinetic information such as the empirical rate law and associated rate constant and (ii) optimisation of either the product conversion (61 % at 0.02 mL min–1 generating 17 g h–1) or product yield (14 % at 0.24 mL min–1 generating 53 g h–1).

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An approach to achieving the ambitious goal of cost effectively extending the safe operation life of energy pipelines to, for instance, 100 years is the application of structural health monitoring and life prediction tools that are able to provide long-term remnant pipeline life prediction and in-situ pipeline condition monitoring. A critical step in pipeline structural health monitoring is the enhancement of technological capabilities that are required for quantifying the effects of key factors influencing buried pipeline corrosion and environmentally assisted materials degradation, and the development of condition monitoring technologies that are able to provide in-situ monitoring and site-specific warning of pipeline damage. This paper provides an overview of our current research aimed at developing new sensors for monitoring, categorising and quantifying the level and nature of external pipeline and coating damages under the combined effects of various inter-related variables and processes such as localised corrosion, coating damage and disbondment, cathodic shielding. The concept of in-situ monitoring and site-specific warning of pipeline corrosion is illustrated by a case of monitoring localised corrosion under disbonded coatings using a new corrosion monitoring probe. A basic principle that underpins the use of sensors to monitor localised corrosion has been presented: Localised corrosion and coating failure are not an accidental occurrence, it occurs as the result of fundamental thermodynamic instability of a metal exposed to a specific environment. Therefore corrosion and coating disbondment occurring on a pipeline will also occur on a sensor made of the same material and exposed to the same pipeline condition. Although the exact location of localised corrosion or coating disbondment could be difficult to pinpoint along the length of a buried pipeline, the ‘worst-case scenario’ and high risk pipeline sections and sites are predictable. Sensors can be embedded at these strategic sites to collect data that contain ‘predictor features’ signifying the occurrence of localised corrosion, CP failure, coating disbondment and degradation. Information from these sensors will enable pipeline owners to prioritise site survey and inspection operations, and to develop maintenance strategy to manage aged pipelines, rather than replace them.

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OBJECTIVE: To investigate the interactions between low parental warmth and monitoring at age 13-14 years and disordered eating attitudes and behaviours at age 15-16 years. METHOD: Data on 1300 (667 females) adolescents and their parents were drawn from The Australian Temperament Project (ATP), a 30 year (15 wave) population based longitudinal study of social-emotional development. Parent participants completed surveys on parenting practices in late childhood, and adolescent participants reported disordered eating using the drive for thinness and bulimia subscales of the Eating Disorder Inventory (EDI) and an additional body dissatisfaction scale. Interaction was examined on the additive scale by estimating super-additive risk; i.e., risk in excess of the sum of individual risks. RESULTS: For boys, neither parental warmth or monitoring, nor their interaction, was related to disordered eating. For girls, low parental warmth (alone) was associated with bulimic behaviours. In contrast, exposure to both low monitoring and warmth was associated with ∼3½-fold, ∼4-fold and ∼5-fold increases in the odds of reporting body dissatisfaction, drive for thinness and bulimia, respectively. For body dissatisfaction and drive for thinness, risk associated with joint exposure exceeded the sum of individual risks, suggesting an additive interaction between parenting styles. CONCLUSION: Further investment in family-level interventions that focus on promoting parental monitoring behaviour and a warm parent-child relationship remain important strategies for preventing a range of disordered eating behaviours in adolescents.