Dissertations / Theses on the topic 'Truck components'

In the truck industry, it is crustal to test components against fatigue to make sure that the trucks stand up to the high demands on durability. Today’s testing methods have some disadvantages; it is quite a time-consuming process, but more important, similar tested components cannot easily be compared due to the load spread the components are subjected to. It is therefore desirable to test the components in a standardized way. One way to do this is to use a synthetic signal which is a large number of unique truck measurements combined. The synthetic signal only contains information of the frame’s vibration and not any components. The purpose of this project was to create a model that uses the synthetic signal to describe the motion of components.   Two approaches were used, the first was to base the model on previous measurements, the second one was to base the model on analytical equations. These models were experimentally tested in a 4 channel shake rig, and a silencer was the component chosen to be tested. For the model based on measurements, the load was shown to have a large spread which was hard to control due to the spread in the measurements. The second model was easier to control where the damping factor can be chosen and varied. A promising model was the analytical model using 10% damping applied to the synthetic signal, it covers most measurements without overestimate the load of the component. However, the model was only developed for the silencer acceleration in the z-direction, and it is recommended to develop it for the x-direction as well. The method used in this project could also be used to develop models for other components.
Inom lastbilsindustrin är det viktigt att prova komponenter mot utmattning för att säkerställa att produkterna håller de höga krav som ställs på tillförlitlighet. Dagens provmetoder har några svagheter, dels är det en tidsödande process. En ännu viktigare svaghet är dock att liknande komponenter som provats kan inte på ett enkelt sätt jämföras med varandra, eftersom belastningen på en komponent kan skilja sig rejält beroende på vilken fordonskonfiguration som väljs att studera. Det är därför önskvärt att prova komponenterna på ett standardiserat sätt. Detta kan göras genom att använda den syntetiska signalen, som är en kombination av ett antal unika lastbilsmätningar. Men den syntetiska signalen innehåller bara information om ramens vibrationer och inte komponenten. Syftet med det här projektet är att ta fram en modell som använder den syntetiska signalen för att beskriva en komponentens rörelse. Två angreppssätt användes, det första var att basera modellen på tidigare mätningar, det andra var att basera modellen på analytiska ekvationer. Dessa modeller var experimentellt provade i en skakrigg med fyra kanaler, och en ljuddämpare blev det valda testobjektet. Modellen baserad på mätningar resulterade i en stor spridning av last, som var svårkontrollerad till följd av spridningen från mätningarna. Modellen baserad på de analytiska ekvationerna var däremot lättare att kontrollera där dämpningen kunde väljas och varieras. En lovande modell verkar vara att använda modellen med 10% dämpning på den syntetiska signalen, den täcker in dem flesta mätningarna utan att överskatta komponentbelastningen. Modellen är dock bara utvecklad för komponentens acceleration i z-riktning och det är rekommenderat att utveckla en liknande modell för x-riktningen också. Metoden kan även användas för andra utveckla modeller för andra komponenter än ljuddämparen.

Context: The context of this research is to forecast the sales of truck componentsusing machine learning algorithms which can help the organization in activity oftrade and business and it also plays a major role for firms in decision-making operationsin the areas corresponding to sales, production, purchasing, finance, and accounting. Objectives: This study first investigates to find the suitable machine learning algorithmsthat can be used to forecast the sales of truck components and then theexperiment is performed with the chosen algorithms to forecast the sales and to evaluatethe performances of the chosen machine learning algorithms. Methods: Firstly, a Literature review is used to find suitable machine learningalgorithms and then based on the results obtained, an experiment is performed toevaluate the performances of machine learning algorithms. Results: Results from the literature review shown that regression algorithms namely Supports Vector Machine Regression, Ridge Regression, Gradient Boosting Regression, and Random Forest Regression are suitable algorithms and results from theexperiment showed that Ridge Regression has performed well than the other machine learning algorithms for the chosen dataset. Conclusion: After the experimentation and the analysis, the Ridge regression algorithmhas been performed well when compared with the performances of the otheralgorithms and therefore, Ridge Regression is chosen as the optimal algorithm forperforming the sales forecasting of truck components for the chosen data.

During the last 10 years, low pressure carburizing and high pressure gas quenching has become more popular since it is a “new” process and researchers reports that the distortion characteristics gained from these processes is improved in relation to conventional processes. The aim of this work was to investigate the distortions gained from three different heat treatment processes on main shaft gears and crown wheels. Experiments with atmospheric carburization with oil quenching and low pressure carburization with nitrogen gas quenching or oil quenching were made and distortion characteristics gained from these processes were then measured and compared. It was found that components treaded in the relatively new LPC-HPGQ process are similar and often even better than the conventional method. The larger main shaft gear improves the most with newer method but instead gets a lower core hardness and systematic diametrical shrinkage. Some measurements on the crown wheel were also improved but others were affected negatively.

This thesis evaluates truck route choice set generation algorithms and derives guidance on using the algorithms for effective generation of choice sets for modeling truck route choice. Specifically, route choice sets generated from a breadth first search link elimination (BFS-LE) algorithm are evaluated against observed truck routes derived from large streams of GPS traces of a sizeable truck fleet in the Tampa Bay region of Florida. A systematic evaluation approach is presented to arrive at an appropriate combination of spatial aggregation and minimum number of trips to be observed between each origin-destination (OD) location for evaluating algorithm-generated choice sets. The evaluation is based on both the ability to generate relevant routes that are typically considered by the travelers and the generation of irrelevant (or extraneous) routes that are seldom chosen. Based on this evaluation, the thesis offers guidance on effectively using the BFS-LE approach to maximize the generation of relevant routes. It is found that carefully chosen spatial aggregation can reduce the need to generate large number of routes for each trip. Further, estimation of route choice models and their subsequent application on validation datasets revealed that the benefits of spatial aggregation might be harnessed better if irrelevant routes are eliminated from the choice sets. Lastly, a comparison of route attributes of the relevant and irrelevant routes shed light on presence of systematic differences in route characteristics of the relevant and irrelevant routes.

The aim of this thesis is to investigate a selected set of fatigue life calculation methods and evaluate if they are suitable for fatigue life estimation of truck components at Scania. Failure due to fatigue can be cause by road induced vibrations, which is an inevitable phenomenon trucks are exposed to. By estimating when and where these components will fail, they can be designed to reduce the amount of failure per vehicle. Three types of fatigue life calculation methods, namely equivalent stress methods, critical plane methods and spectral methods, have been evaluated. These are methods for calculating fatigue life in both the time domain and the frequency domain. The chosen calculation methods have been evaluated based on their sensitivity to input parameters, their accuracy on predicting fatigue life and their ability to find the critical areas where the components are most likely to fail. The methods have also been compared to a method already implemented at Scania. To evaluate the methods, two different components were used. The first component was designed to give a multiaxial stress state and the other was a real truck component where fatigue data had been collected from a shake rig test at Scania. It was found that all investigated methods were successful in finding critical areas where failure will occur. However, the resulting estimated fatigue life had a very low accuracy. To draw any conclusions about the accuracy of the fatigue life estimations, a model that better reflects the dynamics of the real truck component is needed. Therefore, the conclusion is that the chosen methods can be used for finding critical areas in a component but not to determine the absolute time to failure for the model used. However, the method already implemented at Scania was equally successful in finding the critical areas and it has a much shorter computational time than the methods in the time domain. Since it is already implemented and used, the Scania method is recommended for the purpose of finding the critical areas of a component. A sensitivity study was conducted in order to investigate the influence of a variation of material parameters on the fatigue life calculated with the different methods. This study showed that the SN-curve parameters are important for the resulting fatigue life of methods that consider the endurance limit, and, therefore, that the choice of SN-curve is important. Since the road induced vibrations in this study caused load signals where the majority of the cycles were found below the endurance limit, methods that account for the endurance limit have to be used for calculations on components experiencing similar conditions. Furthermore, it was found that the resulting stress signal from the FE-analysis using input data from the shake rig test was non-Gaussian, this makes the results from all the chosen frequency domain methods invalid. To use these methods, they need to be extended to consider non-Gaussian signals.
Syftet med detta examensarbete är att undersöka ett antal utvalda metoder för utmattningsberäkning och utvärdera om dessa är lämpliga för att uppskatta livslängden på lastbilskomponenter hos Scania. Haveri på grund av utmattning kan orsakas av vibrationer från vägen, ett fenomen som påverkar komponenter på lastbilar. Genom att uppskatta när och var dessa komponenter går sönder kan de konstrueras för att minska antalet haverier. Olika typer av metoder för utmattningsberäkning i både tidsdomänen och frekvensdomänen har utvärderats. Dessa inkluderade ekvivalenta spännings-metoder, kritiska plan-metoder samt spektrala metoder. Metoderna har utvärderats med avseende på deras känslighet för variation i materialparametrar, hur den beräknade livslängden skiljer sig mot verkliga tester och hur bra de är på att hitta de kritiska områdena på en lastbilskomponent. Detta har även jämförts mot en beräkningsmetod som redan används på Scania. Två olika komponenter användes för att utvärdera metoderna. En av komponenterna var designad för att ge ett multiaxiellt spänningstillstånd och en var en riktig lastbilskomponent med data uppmätt från ett skaktest på Scania. Alla studerade metoder fann de kritiska områdena där utmattningsbrott riskerar att uppstå. Däremot visade det sig att beräkningsmetoderna inte lyckades estimera livslängder som låg i närheten av de som uppmättes under testet i skakriggen. En mer verklighetsnära modell vilken bättre motsvarar de dynamiska egenskaperna av systemet behövs för att kunna dra en slutsats om modellernas träffsäkerhet gällande estimeringen av livslängden. För ändamålet att hitta kritiska områden rekommenderas metoden som redan används hos Scania, eftersom denna var lika framgångsrik att hitta dessa, men gjorde det på en avsevärt kortare tid. Därutöver identifierades att spänningssignalen från FE-analysen, där indata från skakriggen användes, inte var gaussisk. Detta innebär att signalen inte uppfyller kraven för de spektrala metoderna och därmed att resultaten från beräkningarna på lastbilskomponenten inte går att använda för att dra några slutsatser. Känslighetsanalysen visade att de metoder som tar hänsyn till utmattningsgränsen är känsliga för ändringar i SN-parametrar. Detta beror på att många cykler, för det studerade lastfallet, låg nära utmattningsgränsen och att antalet cykler som ingick i beräkningarna därför påverkades stort av SN-parametrarna. Eftersom de vibrationer som uppstår då lastbilar framförs på vägar kan ge upphov till många cykler med amplituder nära utmattningsgränsen bör endast metoder som kan ta hänsyn till utmattningsgränsen användas vid dessa fall.

This project has been sponsored by QinetiQ Limited (QinetiQ); whose aim it is to model the dynamics of a prototype high-speed military tracked vehicle. Specifically their objective is to describe the mechanism by which force inputs are transmitted from the ground to the vehicle’s hull. Many track running gear components are steel and can be modelled as simple lumped masses or as linear springs without internal damping. These present no difficulty to the modeller. However tracked vehicle running gear also has nonlinear components that require more detailed descriptions. Models for two rubber components, the road wheel tyre and track link bush, and a model for the suspensions rotary damper, are developed here. These three components all have highly nonlinear dynamic responses. Rubber component nonlinearities are caused by the materials nonlinear elastic and viscoelastic characteristics. Stiffness is amplitude dependent and the material exhibits a significant amount of internal damping, which is predominantly Coulombic in nature but also relaxes overtime. In this work, a novel method for measuring the elastic and viscoelastic response of Carbon Black Filled Natural Rubber components has been devised and a ‘general purpose’ mathematical model developed that describes the materials response and is suited to use in multibody dynamic analysis software. The vehicle’s suspension rotary damper model describes three viscous flow regimes (laminar, turbulent and pressure relief), as a continuous curved response that relates angular velocity to damping torque. Hysteresis due to the compression of entrapped gas, compliance of the dampers structure and compression of damper oil is described by a single non-parametric equation. Friction is considered negligible and is omitted from the model. All components are modelled using MSC.ADAMS TM multibody dynamic analysis software. The models are shown to be easily implemented and computationally robust. QinetiQ’s requirement for ‘practical’ track running gear component models has been met.

This project has been sponsored by QinetiQ Limited (QinetiQ); whose aim it is to model the dynamics of a prototype high-speed military tracked vehicle. Specifically their objective is to describe the mechanism by which force inputs are transmitted from the ground to the vehicle’s hull. Many track running gear components are steel and can be modelled as simple lumped masses or as linear springs without internal damping. These present no difficulty to the modeller. However tracked vehicle running gear also has nonlinear components that require more detailed descriptions. Models for two rubber components, the road wheel tyre and track link bush, and a model for the suspensions rotary damper, are developed here. These three components all have highly nonlinear dynamic responses. Rubber component nonlinearities are caused by the materials nonlinear elastic and viscoelastic characteristics. Stiffness is amplitude dependent and the material exhibits a significant amount of internal damping, which is predominantly Coulombic in nature but also relaxes overtime. In this work, a novel method for measuring the elastic and viscoelastic response of Carbon Black Filled Natural Rubber components has been devised and a ‘general purpose’ mathematical model developed that describes the materials response and is suited to use in multibody dynamic analysis software. The vehicle’s suspension rotary damper model describes three viscous flow regimes (laminar, turbulent and pressure relief), as a continuous curved response that relates angular velocity to damping torque. Hysteresis due to the compression of entrapped gas, compliance of the dampers structure and compression of damper oil is described by a single non-parametric equation. Friction is considered negligible and is omitted from the model. All components are modelled using MSC.ADAMS TM multibody dynamic analysis software. The models are shown to be easily implemented and computationally robust. QinetiQ’s requirement for ‘practical’ track running gear component models has been met.

The design of the freight train truck has gone relatively unchanged over the past 150 years. There has been relatively little change to the fundamental railway truck design because of the challenges of implementing a cost effective and reliable modification to designs that have proven effective in decades of operation. A common U. S. railway truck consists of two sideframes, a bolster, two spring nests, and four friction wedges. The two sideframes sit on the axels. The bolster rides on springs on top of the sideframes. The friction wedges also ride on springs on top of the sideframe, and are positioned between the bolster and sideframe, acting as a damping mechanism. Better understanding the dynamic behavior and forces on the bodies are critical in reducing unnecessary wear on the components, along with potential negative behavior such as loss of productivity and increase in operating costs. This thesis will investigate the dynamic behavior of the truck under warping conditions using a stand-alone model created in Virtual.Lab. This research covers two main areas. First, the full-truck model will be developed and its simulation results will be compared to test data from the Transportation Technology Center, Inc. (TTCI). Data was provided from warp testing performed at the TTCI facilities in the spring of 2008. Once validated, the model will be used to gain a better understanding of the forces and moments that are propagated through the system, and of the dynamics of all bodies. Due to costs and physical constraints, not every bogie component can be instrumented during test, so the computer model will be able to provide valuable information not easily obtained otherwise. Second, full-truck models using different contact geometry between the wedges, sideframes, and bolster will be compared. A model with extremely worn sideframes will allow for investigation into the effects of wear on the damping abilities and warp stiffness of the truck. Another model using split wedges will be compared with the previous model to investigate into the behavior differences in the truck using different types of wedges. By understanding the impact of different geometries on the overall performance of the truck, better decisions on design and maintenance can be made in the future. After creating the models, we found that the full-truck model created in LMS® Virtual.Lab compared well with the test data collected by TTCI. In the comparison with NUCARS® we determined that the stand-alone model, which incorporates the wedges as bodies, captures the warp dynamics of the truck better than NUCARS®, which models the wedges as connections. By creating a model with severely worn sideframes, we were able to determine that the truck loses its abilities to damp bounce in the system as well as to prevent warping when the components become sufficiently worn. The split-wedge model behaved similarly to the standard full-truck model for bounce inputs, but had a significantly different behavior in warp. Further development will be needed on the split-wedge model to be confident that it behaved as expected.
Master of Science

The large potential of big data and how it has brought value into various industries have been established in research. Since big data has such large potential if handled and analyzed in the right way, revealing information to support decision making in an organization, this thesis is conducted as a case study at an automotive manufacturer with access to large amounts of customer usage data of their vehicles. The reason for performing an analysis of this kind of data is based on the cornerstones of Total Quality Management with the end objective of increasing customer satisfaction of the concerned products or services. The case study includes a data analysis exploring how and if patterns about what affects fuel consumption can be revealed from aggregated customer usage data of trucks linked to truck applications. Based on the case study, conclusions are drawn about how a company can use this type of analysis as well as how to handle the data in order to turn it into business value. The data analysis reveals properties describing truck usage using Factor Analysis and Principal Component Analysis. Especially one property is concluded to be important as it appears in the result of both techniques. Based on these properties the trucks are clustered using k-means and Hierarchical Clustering which shows groups of trucks where the importance of the properties varies. Due to the homogeneity and complexity of the chosen data, the clusters of trucks cannot be linked to truck applications. This would require data that is more easily interpretable. Finally, the importance for fuel consumption in the clusters is explored using model estimation. A comparison of Principal Component Regression (PCR) and the two regularization techniques Lasso and Elastic Net is made. PCR results in poor models difficult to evaluate. The two regularization techniques however outperform PCR, both giving a higher and very similar explained variance. The three techniques do not show obvious similarities in the models and no conclusions can therefore be drawn concerning what is important for fuel consumption. During the data analysis many problems with the data are discovered, which are linked to managerial and technical issues of big data. This leads to for example that some of the parameters interesting for the analysis cannot be used and this is likely to have an impact on the inability to get unanimous results in the model estimations. It is also concluded that the data was not originally intended for this type of analysis of large populations, but rather for testing and engineering purposes. Nevertheless, this type of data still contains valuable information and can be used if managed in the right way. From the case study it can be concluded that in order to use the data for more advanced analysis a big-data plan is needed at a strategic level in the organization. The plan summarizes the suggested solution for the managerial issues of the big data for the organization. This plan describes how to handle the data, how the analytic models revealing the information should be designed and the tools and organizational capabilities needed to support the people using the information.

The hard equation of railway safety versus the high commercial profits can only be achieved through the use of new inspection methods supported by modern technologies. The track and its components can have different types of troubles, such as rail surface defects, broken sleepers, missing fasteners, and irregular ballast levels. Each component of the track infrastructure plays a significant role, where the failure or the absence of any of them can pave the way to undesired situations. The rail is designed to carry and direct the train, the sleepers are meant to maintain the level of the rail, and the ballast mission is to keep all components floating on the surface of the ground. The fasteners are used to fasten the rail to the sleepers, and therefore too many missing fasteners can lead to sever unsteady tracks, which can, in turn, result in derailment. Therefore, there is a high demand for advanced inspection methods to monitor the railway track and its components continuously. The presence of such advanced inspection models would help the railway industry avoid obstacles such as high operation and maintenance costs, dangerous accidents, and uncomfortable passenger's experience.   This master thesis aims to present an efficient method to classify the track and its components by combining image processing techniques and deep learning algorithms. This method was able to detect the missing fasteners in the set of images captured by a line camera, continuously monitoring the rail and its associated fasteners. The experimental results obtained in this thesis showed that the proposed method is efficient and robust for detecting the track and its components in complex environments. The thesis also discusses the idea of building one complete model that can process and classify all track components at once. The image processing technique was employed to extract different components of the track, individually: fasteners, rail, ballast, and sleepers. The model was trained and used to classify the state of the fasteners. Classification of other components of the track will be a part of the future work.

A Scania S-series front chassis module feasibility study is carried out to investigate the potential gains and losses by changing to a composite material system. The existing front chassis module comprises multiple steel, sheet metal and plastic components. The design space is fixed by the location of adjacent components in the current design. A new methodology is put together on the basis of "The GAP Methodology: A new way to design composite structures" by F. Neveu et al. (2019)[1] for facilitating the complex nature of multivariable composite structures design. By applying the methodology a set of hand sketches based on vari-ous geometry classes and applicable manufacturing processes can be created for a technical screening, where one concept is brought forward for detailed analysis. The concept design is refined by the use of the surface modeller tool in CATIA V5 and a structural analysis is undertaken using the finite element method software for composites ANSYS ACP. The composite laminate layup is designed by using aerospace design rules as guidelines for the given material system. The proposed design solution satisfies the design requirements and improves the benchmark Scania Chassis module by lowering the amount of components with about 30%, has a recommended metal to composite joining method, reduces the mass by around 40% (53.5% excluded the suggested joining method) and has a safety factor to material failure strains. The feasibility study demonstrates that the proposed methodology and design of the new composite chassis component is plausible using a simplified analysis.
Detta arbete är en genomförbarhetsstudie för undersöka för- och nackdelar med att byta ut nuvarande material till fiberkomposit, i ett av de främre chassihörnen i en Scania S-serie bil. Det nuvarande chassihörnet består av flera komponenter i stål, plåt och plast. Desingutrymmet är bestämt av placeringen av angränsande komponenter i dagens bil. En ny metodik är sammansatt baserat på ”The GAP Methodology: A new way to design composite structures” av F. Neveu et al. (2019) [1] för att underlätta den komplexa karaktären av produktframtagning av komponenter i fiberkomposit. Genom att använda metodiken kan en uppsättning handskisser genereras baserade på olika geometriklasser och tillämpliga tillverkningsprocesser. Detta tas sedan vidare till en teknisk screening, där ett koncept väljs för fortsatt mer detaljerad analys. Konceptdesignen förfinas med användning av ytmodellverktyget i CATIA V5 och en strukturanalys utförs med användning av ANSYS ACP för finit elementmetod för kompositer. Kompositlaminatdesignen med det givna materialsystemet är gjord med hjälp av konstruktionsregler från flygindustrin.Den framtagna designen tillfredställer designkraven och förbättrar den nuvarande Scania designen för chassihörnet. Detta genom att minska antalet komponenter med ungefär 30%, har en rekommenderad fästmetod för metall till fiberkomposit, minskar vikten med ungefär 40% (53.5% exkluderat den föreslagna fästmetoden) och har en säkerhetsmarginal till maxtöjningar för materialsystemet. Genomförbarhetsstudien visar att den föreslagna metodiken och designen av den nya chassikomponenten i fiberkomposit är möjlig med en förenklad analys.

A method for numerical simulation of train induced track vibration and wave propagation in subgrade has been proposed. The method uses a mass to simulate the bogie of a train and considers the effect of rail roughness. For this method, rail roughness is considered as a randomly generated signal and a filter is used to block the undesired components. The method predicts the particle velocity around the track and can be applied to many kinds of railroad trackbeds including traditional ballast trackbed and modern Hot mix asphalt (HMA) trackbed. Results from ballast and HMA trackbeds are compared and effects of HMA layer on damping track vibration and dissipating wave propagation are presented. To verify the credibility of the method, in-track measurements were also conducted. Site measurements included performing geophysical tests such as spectral analysis of surface wave test and seismic refraction test to determine the subsurface conditions at the test site. Ballast and HMA samples were tested in the laboratory by resonant column test to obtain the material properties. Particle velocities were measured and analyzed in the frequency domain. Results from in-track tests confirm the applicability of the numerical method. The findings and conclusions are summarized and future research topics are suggested.

The strive for cost reduction of services and repairs combined with a desire for increased vehicle reliability has led to the development of predictive maintenance programs. In maintenance plans, accurate forecasts and predictions regarding which components in a vehicle is in risk of a breakdown is bene_cial to obtain since this enables components to be predictively exchanged or serviced before they break down and cause unnecessary downtime. Previous works in data driven predictive maintenance models typically utilize customer and operational data to predict component wear trough regressive or classi_er models. In this thesis the possibilities and bene_ts associated with utilizing vehicle repair and service history data for trucks in a predictive model is investigated. The repair and service data is a time series of irregularly sampled visits to a service centre and is used in conjunction with operational data and chassis con_guration data collected by a truck manufacturer. To tackle the problem a Random Forest, a Neural Network as well as a Recurrent Neural Network model was tested on the various datasets. The Recurrent Neural Network model made it possible to utilize the entire vehicle repair time series data whereas the Random Forest model used a condensed form of the repair data. The Recurrent model proved to perform signi_cantly better than the Neural Network model trained on operational data however it was not proven signi_cantly better than a Random Forest model trained on the condensed form of repair data. A conclusion that can be drawn is that repair history data can increase the performance of a predictive model, however it is unclear if the time sequence plays a part or if a list of previously exchanged parts works equally well.
Strävan efter att reducera kostnader av reparationer och service samt att öka fordons pålitlighet har lett till utvecklingen av prediktiva underhållsprogram. Träffsäkra förutsägeleser och prediktioner kring vilka delar som riskerar att fallera möjliggör prediktiva utbytelser eller service av delar innan de går sönder. Tidigare arbeten i prediktivt underhåll använder sig vanligen av kunddata och operationell data för att generera en prediktion genom regressions eller klassificeringsmetoder. I det här examensarbetet utforskas möjligheterna och fördelarna med att använda verkstadsdata från lastbilar i en prediktiv modell. Verkstadsdatan består av en oregelbundet genererad tidsserie av besök till en serviceanläggning och används i kombination med operationell data samt chassiutförandedata. För att angripa problemet användes en Random Forest, en Neuronnäts samt en Recurrent (Återkommande) Neuronnätsmodell på de olika datakällorna. Recurrent Neuronnätsmodellen möjliggjorde användandet av kompletta tidserieverkstadsdatan och denna modell visade sig ge bäst resultat men kunde inte påvisas  vara signifikant bättre än en Random Forest modell som tränades på en komprimerad variant av verkstadsdatan.  En slutsats som kan dras av arbetet är att verkstadsdatan kan öka prestandan i en prediktiv model men att det är oklart om det är tidssekvensen av datat som ger ökningen eller om det fungerar lika bra med en lista över tidigare utbytta delar.

I have developed time-varying coefficient models for recurrent event data to evaluate the temporal profiles for recurrence rate and covariate effects. There are three major parts in this dissertation. The first two parts propose a mixed Poisson process model with gamma frailties for single type recurrent events. The third part proposes a Bayesian joint model based on multivariate log-normal frailties for multi-type recurrent events. In the first part, I propose an approach based on penalized B-splines to obtain smooth estimation for both time-varying coefficients and the log baseline intensity. An EM algorithm is developed for parameter estimation. One issue with this approach is that the estimating procedure is conditional on smoothing parameters, which have to be selected by cross-validation or optimizing certain performance criterion. The procedure can be computationally demanding with a large number of time-varying coefficients. To achieve objective estimation of smoothing parameters, I propose a mixed-model representation approach for penalized splines. Spline coefficients are treated as random effects and smoothing parameters are to be estimated as variance components. An EM algorithm embedded with penalized quasi-likelihood approximation is developed to estimate the model parameters. The third part proposes a Bayesian joint model with time-varying coefficients for multi-type recurrent events. Bayesian penalized splines are used to estimate time-varying coefficients and the log baseline intensity. One challenge in Bayesian penalized splines is that the smoothness of a spline fit is considerably sensitive to the subjective choice of hyperparameters. I establish a procedure to objectively determine the hyperparameters through a robust prior specification. A Markov chain Monte Carlo procedure based on Metropolis-adjusted Langevin algorithms is developed to sample from the high-dimensional distribution of spline coefficients. The procedure includes a joint sampling scheme to achieve better convergence and mixing properties. Simulation studies in the second and third part have confirmed satisfactory model performance in estimating time-varying coefficients under different curvature and event rate conditions. The models in the second and third part were applied to data from a commercial truck driver naturalistic driving study. The application results reveal that drivers with 7-hours-or-less sleep prior to a shift have a significantly higher intensity after 8 hours of on-duty driving and that their intensity remains higher after taking a break. In addition, the results also show drivers' self-selection on sleep time, total driving hours in a shift, and breaks. These applications provide crucial insight into the impact of sleep time on driving performance for commercial truck drivers and highlights the on-road safety implications of insufficient sleep and breaks while driving. This dissertation provides flexible and robust tools to evaluate the temporal profile of intensity for recurrent events.
PHD

Nos últimos anos, tem-se presenciado um contínuo aumento na demanda por transporte de carga ferroviário no Brasil, o que tem se refletido em maior volume de projetos para expansão ou revitalização da malha existente no país. Diante deste quadro atual, torna-se essencial que o profissional envolvido com a elaboração de projetos da via permanente tenha em mãos informações atualizadas a respeito da variedade de componentes da grade ferroviária existente no mercado, a fim de que os projetos atendam plenamente às necessidades do cliente. Assim sendo, o presente trabalho tem como principal objetivo pesquisar e analisar os tipos de componentes existentes no mercado usados na composição da grade ferroviária (trilhos, dormentes e fixações), com foco principal naqueles que podem ser aplicados às ferrovias de transporte de carga. Primeiramente, a partir de pesquisa em material bibliográfico nacional e internacional, serão descritos os principais aspectos técnicos que devem ser considerados no processo de avaliação destes componentes, tendo como base os parâmetros de projeto da via estabelecidos previamente de acordo com as exigências da operação de carga, tais como: carga por eixo, velocidade de operação, rampa máxima, etc. Em seguida, serão apresentados os tipos de trilhos, dormentes (madeira, concreto monobloco e bi-bloco, aço e plástico) e fixações (rígidas e elásticas) existentes atualmente no mercado e suas características de desempenho e condições de uso estabelecidas pelos respectivos fabricantes. Os dados obtidos nas duas etapas descritas anteriormente serão analisados e confrontados, de forma que seja possível explicitar as relações de interdependência que existem entre os diferentes tipos de componentes. Ao final do trabalho, será apresentado um modelo de fluxograma que reúne os dados de entrada para o projeto da ferrovia de transporte de carga, as diretrizes técnicas que devem ser atendidas em seu projeto e, por fim, os tipos de componentes da grade ferroviária que podem ser empregados de acordo com o caso estudado. Com isso, espera-se que o produto final gerado possa ser uma ferramenta útil na etapa de concepção de novos projetos ferroviários, visto que apresentará de maneira clara e objetiva um conjunto de informações bastante abrangente e atualizado a respeito dos critérios técnicos e das opções de componentes disponíveis no mercado, fornecendo uma orientação inicial consistente ao profissional envolvido sobre os materiais que podem ser aplicados à via em estudo.
Over the last years, a continuous increase on demand for railroad heavy haul transport has been witnessed in Brazil, reflecting in bigger volume of projects for expansion or improvement of the existing lines in the country. With this current situation, it becomes essential that the professional involved with the elaboration of projects of the permanent way should have information brought up to date regarding the variety of components of the existing railroad materials in the market, so that the projects take care of fully to the necessities of the customer. Thus being, the present work has as main objective to search and to analyze the types of existing components in the market used in the composition of the railroad superstructure (rails, ties and fastenings), with main focus in that which can be applied to the heavy haul railways. First, from research in national and international bibliographical material, the main aspects related to the performance that must be considered in the process of evaluation of these components will be describes, having as base the design parameters of the way established previously in accordance with the requirements of the load operation, such as: position for axle, speed of operation, maximum slope, and others. After that, the types of rails, ties (wood ties, monoblock and bi-block concrete ties, steel ties and plastic ties) and fastenings (rigid and elastic) currently available in the market and its characteristics of performance and conditions of use will be presented, according to the information provided by the its respective manufacturers. All the reports showed in the two previous described stages will be analyzed and compared, so that is possible to highlight the interdependence relations that exist between the different types of components. At the end of the work, a flowchart model which gathers the input data about the heavy haul railroad, the performance guidelines that should be followed and the types of components of the railroad superstructure that can be used in accordance to the studied case, will be presented. With that, it is expected that the produced flowchart could be a useful tool in the stage of design of a new railroad, once it will present in a clear and objective way an embracing and up-to-date set of information regarding the performance criteria and the options of components available in the market, supplying a consistent initial orientation to the designer about the materials that could be applied to the railway.

Healthy aging is associated with an increased incidence of injuries such as low back pain and falling. Age-related changes in trunk neuromuscular activation patterns could potentially explain uncontrolled trunk motion, a risk factor for these injuries. The purpose of this study was two-fold: i) to establish if trunk neuromuscular activation patterns differ between older and younger adults, and ii) to identify if increased cognitive load can alter the activation patterns used by older adults. Participants performed a controlled dynamic transfer task known to challenge trunk musculature by continuously changing the external moment experienced by the spine. In this thesis, the transfer task was altered by increasing the external moment and cognitive load to address purpose i) and ii) respectively. Three dimensional trunk and pelvis motion and surface electromyograms for 24 trunk muscle sites were collected in two experiments. In the first experiment 26 younger adults, and 17 older adults, performed the transfer task at two physical task intensities. In a second experiment, eight of these older adults performed the dynamic transfer task while performing a simultaneous cognitive dual-task. An analysis of amplitude and temporal characteristics of EMG waveforms, using principal component analysis, showed that older adults had altered trunk neuromuscular activation patterns compared to younger adults. In particular, after accounting for physical task intensity, older adults have increased antagonist co-activation, less temporal variation to changing external moments, and changes in the synergistic relationships between trunk muscle sites. Increasing the cognitive load had no influence on the trunk neuromuscular activation patterns used by healthy active older adults. However, as cognitive performance was not measured in the dual-task it cannot be excluded that i) the cognitive task did not represent enough challenge to result in competition of cortical resources, or ii) the cognitive resources necessary for the successful completion of the transfer task were prioritized in older adults.

This research was motivated by the use of multi-track feeders in the printed circuit board (PCB) assembly. In a low volume, high mix production environment, setup time is usually considered more important than processing time. Implementation of multi-track feeders not only increases the capacity of the surface mount machines but also reduces feeder changeovers. However, improper planning could diminish these benefits. The objective of this research is to develop a process plan to minimize the feeder setups in multi-track feeder systems. Two problems have been identified: component to multi-track feeder assignment problem and PCB sequencing problem. The assignment problem is formulated as a multi-dimension symmetric assignment problem with an integer-programming model. The objective is to maximize the total similarity of the component assignment. This optimization model is implemented for small-sized problems using a commercial solver package. Due to NP-complete characteristics, heuristic algorithms are developed for solving large-scale problems and industrial cases. The Hungarian algorithm, designed for asymmetric assignment problems, is used to reduce problem size in the double feeder case. The PCB sequencing problem is solved in three stages: component and PCB grouping, intra- and inter-group PCB sequencing, and feeder setup planning. An optimal tool switch policy called Keep Tool Needed Soonest is adapted for planning the multi-track feeder setup. This research also identifies the interrelationship of the assignment problem and PCB sequencing problem. An optimal component to feeder assignment will show real advantages only when working with a well-planned PCB sequence. Data obtained from literature are used to verify the heuristic developments. The methods are also applied to industrial data for evaluation of performance of real-world problems. Experimentation is conducted with simulation data to investigate the performance of methodology for different production situations. The results show that savings of up to 85% in feeder setups can be realized with a double feeder system compared to a single feeder system, with the use of the developed methodology. The approach is also robust and efficient for different production environments.
Graduation date: 1999

A top-down approach is presented to achieve cost reduction and optimization of the fuel cell system for a medium duty truck. Key inputs are customer usage and drive cycles leading to optimization of power and torque needs, followed by a three-way optimization between fuel cell system, hydrogen storage and battery size. This is supported by technological developments to reduce component costs
Ein Top-Down-Ansatz zur Reduzierung der Kosten des Brennstoffzellensystems eines Lastkraftwagens wird vorgestellt. Haupteingangsdaten, wie Kundenfahrzyklen und Verbrauch werden benutzt erforderliche Leistung und Drehmoment zu ermitteln und optimieren. Ein Optimierungsverfahren zwischen dem BZ System, dem Wasserstoffspeicher und der Batterie folgt um einen optimierten E-Antriebsstrang für das Fahrzeug darzustellen. Der Prozess wird von technologischen Fortschritten untermauert, um weitere Kostenreduzierung auf Komponentenebene zu erreichen.