A NEURAL NETWORK BASED APPROACH TO FAULT DETECTION IN INDUSTRIAL PROCESSES

The need for automated fault detection methods has increased in line with the complexity of processing plant technology and their control systems. Fast and accurate fault detection and isolation (FDI) is essential if a controller is to be effective in a supervisory role. This thesis is concerned with developing an FDI system based upon artificial neural network techniques. The artificial neural network (ANN) is a mechanism based upon the concepts of information processing within the brain, and consequently has the ability to self adjust, or learn about a given problem domain. It can thus be utilised in currently favoured model-based FDI systems with the advantage that it can learn process dynamics by being presented examples of process input-output pairs without the need for traditional mathematically complex models. Similarly, ANNs can be taught to classify characteristics in the residual (or plant-model difference) signal without the necessity of constructing the types of filter used in more classical solutions. Initially, a class of feedforward neural network called the multilayer perceptron (MLP) is used to model mathematically simulated linear and nonlinear plants in order to demonstrate their abilities in this field, as well as investigating the consequence of parameter variation on model effectiveness and how the model can be utilised in a model-based FDI system. A principle aim of this research is to demonstrate the ability of the system to work online and in real-time on genuine industrial processes, and the plant nominated as a test bed - the Unilever Automated Freezer (UAF) - is introduced. The UAF, being a time-varying system, requires a novel system identification approach which has resulted in a number of cascaded MLPs to model the various stages in the phased startup of the process. In order to reduce model mismatch to a minimum, it was necessary to develop an effective switching mechanism between one MLP in the cascade and the next. Attempts using a rule-based switching mechanism, a simple MLP switch and an error based switching mechanism were made, before a solution incorporating a genetic algorithm and an MLP network was developed which had the capability of learning the optimum switching points. After the successful development of the model, a series of MLPs were trained to recognise the characteristics of a number of faults within the residual signals. Problems involving false alarms between certain faults were reduced by the introduction of templates - or information pertaining to when a particular fault was most evident in the residuals. The final solution consisting of an MLP Cascade model and fault isolation MLPs is essentially generic for this class of time-varying system, and the results achieved on the UAF were far superior to those of the currently used FDI system without the need for any extra sensory information. The MLP Cascade and associated switching device together with the development of an online real-time FDI system for a time-varying piece of industrial machinery, are deemed to be original contributions to knowledge.Unilever Research Colworth Laborator

Chemical Composition And Nutritional Benefits Of Acid Resistant Hemicellulose.

The present work was conducted to determine the influence of methods of preparation of corncob acid-resistant hemicellulose on its chemical composition and rat growth stimulation. ARH preparations involved either treatment with cation resin, solubilization of crude ARH and reprecipitation in ethanol, treatment of the hydrolysate with carbon or recovery of the non-dialyzable portion of corncob hydrolysate. All ARH preparations significantly improved growth and feed utilization of rats when fed at the 0.05% level of the diet. The reduction of the phenolic content of the ARH preparation did not influence rat growth. The carbohydrate components of the ARH preparation were similar to carbohydrate components of hemicellulose reported by other workers

Age and growth of swordfish (Xiphias gladius) caught by the Hawaii-based pelagic longline fishery

We verified the age and growth of swordfish (Xiphias gla-dius) by comparing ages determined from annuli in fin ray sections with daily growth increments in otoliths. Growth of swordfish of exploitable sizes is described on the basis of annuli present in cross sections of the second ray of the first anal fins of 1292 specimens (60−260 cm eye-to-fork length, EFL) caught in the region of the Hawaii-based pelagic longline fishery. The position of the initial fin ray annulus of swordfish was verified for the first time with the use of scanning electron micrographs of presumed daily growth increments present in the otoliths of juveniles. Fish growth through age 7 was validated by marginal increment analysis. Faster growth of females was confirmed, and the standard von Bertalanffy growth model was identified as the most parsimonious for describing growth in length for fish greater than 60 cm EFL. The observed growth of three fish, a year-old in size when first caught and then recaptured from 364 to1490 days later, is consistent with modeled growth for fish of this size range. Our novel approach to verifying age and growth should increase confidence in conducting an age-structured stock assessment for swordfish in the North Pacific Ocean

Impact of creep feeding on subsequent performance and plasma parameters of feedlot steers

Abstract only availableA study was conducted to evaluate the impact of creep feeding and protein sources, notably DDGS, on blood plasma variables and growth in crossbred steer calves. Thirty-six crossbred steer calves were used in a randomized block design to compare creep feeding vs. non-creep feeding on plasma glucose, plasma urea nitrogen (PUN) and plasma non-esterified fatty acids (NEFA), as well as overall performance. NEFA was tested as a measure of the mobilization of body fat reserves due to diet. Steers were randomly selected to one of three treatments. Steers were creep fed supplements daily during the creep feeding phase. They remained in the same treatment groups and were moved to drylot pens (6 animals/pen; 2 pens/treatment) for the feedlot phase. Live weights and blood samples were taken on selected days. Glucose, PUN and NEFA concentrations were analyzed colorimetrically using Linear Enzymatic Glucose, PUN and NEFA-C kits, respectively. Additionally, fat depth and rib eye area (REA) were measured using ultrasonography. There were no differences in average daily gains (ADG) between protein sources during the creep feeding or feedlot phases. However, creep fed calves had a greater (P .10) in plasma NEFA concentrations among treatments and concentrations remained similar from day 14 to 176. Fat depth and REA steadily increased from day 0 to 176, but no differences were found among treatments. In conclusion, creep feeding with DDGS increased plasma glucose and NEFA concentrations; however these changes were not evident after 14 days on the feedlot phase. Creep feeding had little effect on PUN concentrations, but differences were seen at 14 days on the feedlot phase. These changes in plasma glucose and NEFA concentrations may be attributed to the fat content of DDGS.F.B. Miller Summer Undergraduate Research Program in Animal Science

Transcatheter Heart Valve Leaflet Assembly Tooling Improvement Final Design Report

Statement of Confidentiality: The complete senior project report was submitted to the project advisor and sponsor. The results of this project are of a confidential nature and will not be published at this time

Question of Window-Tax on Hospitals.

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Using machine learning to study the kinematics of cold gas in galaxies

Next generation interferometers, such as the Square Kilometre Array, are set to obtain vast quantities of information about the kinematics of cold gas in galaxies. Given the volume of data produced by such facilities astronomers will need fast, reliable, tools to informatively filter and classify incoming data in real time. In this paper, we use machine learning techniques with a hydrodynamical simulation training set to predict the kinematic behaviour of cold gas in galaxies and test these models on both simulated and real interferometric data. Using the power of a convolutional autoencoder we embed kinematic features, unattainable by the human eye or standard tools, into a 3D space and discriminate between disturbed and regularly rotating cold gas structures. Our simple binary classifier predicts the circularity of noiseless, simulated, galaxies with a recall of 85% and performs as expected on observational CO and H i velocity maps, with a heuristic accuracy of 95%. The model output exhibits predictable behaviour when varying the level of noise added to the input data and we are able to explain the roles of all dimensions of our mapped space. Our models also allow fast predictions of input galaxies’ position angles with a 1σ uncertainty range of ±17° to ±23° (for galaxies with inclinations of 82.5° to 32.5°, respectively), which may be useful for initial parametrization in kinematic modelling samplers. Machine learning models, such as the one outlined in this paper, may be adapted for SKA science usage in the near future

The Ursinus Weekly, April 30, 1970

Bomberger changes considered by UCC • Alan Cary Gold re-appointed editor-in-chief of Weekly • Y officers elected; John Gray president • Pettit announces changes in faculty for next Fall • Bill of Rights draft revised for students • Bozorth announces dress code • Editorial: Statement of purpose • Focus: Mike Mangan • The Ursinus Student Bill of Rights • From the other side: Victory in Vietnam • Faculty portrait: Dean Richard Bozorth • UC chemists first • Letters to the editor: Earth Day; Another side; Pater noster revisited • Would you believe? • Shuman leads Bears over F&M, W. Maryland • PMC defeat avenged as Albert triumphs • Contemplations: Glorious revolutionhttps://digitalcommons.ursinus.edu/weekly/1159/thumbnail.jp

The impact of ash pellet characteristics and pellet processing parameters on ash fusion behaviour

The Ash Fusion Test (AFT) is considered to be the most popular method of characterising the melt characteristics of solid fuel ash. This study shows how pellet preparation can make significant improvements to repeatability. Pelleting pressure, pellet particle size, pellet shape, and furnace ramp rate were investigated to establish the most repeatable representation of ash melting relevant to pulverised fuel combustion in a furnace in an oxidizing atmosphere up to 1600 °C. A 5 mm machine pressed pellet was found to produce the best results as it identified the earliest initial deformation temperature (IDT), gave the least error, and displayed the greatest visible change in pellet height to enable easy identification. Reducing maximum ash particle size to <72 µm and increasing the pressure of the pelleting process was also shown to produce a 120 °C reduction in the IDT when compared with other methods. Reducing the ashing temperature and retaining volatiles lost during high temperature ashing were shown to have a negligible impact on IDT. The characteristic AFT curve was also used to quantify the extent of shrinkage and swelling during the test