62P30 Applications in engineering and industry
Refine
Document Type
- Doctoral Thesis (4)
Has Fulltext
- yes (4)
Keywords
Faculty / Organisational entity
Gängige Füllstandmesssysteme für mobile Schüttgutsilos werden i. d. R. invasiv an der Innenseite des Behälters angebracht. Hierdurch sind die Sensoren abrasiven Mechanismen und einem entsprechend hohen Verschleiß ausgesetzt. Dies führt zu einer geringen Wirtschaftlichkeit der bisherigen Füllstandüber-wachung von mobilen Schüttgutsilos. Im Rahmen dieser Arbeit wird die Umsetzbarkeit eines alternativen, nichtinvasiven Sensorkonzeptes untersucht, welches auf der Auswertung der füllstandabhängigen Impulsantwort des Silos basiert.
Hierzu werden anhand einer analytischen Modellierung des Messsystems die potentiellen Einflussgrößen des Sensorkonzeptes identifiziert. Anschließend werden die potentiellen Einflussgrößen im Rahmen numerischer Untersuchungen (FEM/DEM) näher analysiert und bewertet. Die ermittelten, scheinbar kritischen Einflussgrößen werden weiterhin experimentell untersucht. Es werden zwölf Füllstandkennwerte definiert, anhand derer eine Support Vector Machine trainiert und anschließend zur robusten Ermittlung des Füllstandes verwendet wird.
Many loads acting on a vehicle depend on the condition and quality of roads
traveled as well as on the driving style of the motorist. Thus, during vehicle development,
good knowledge on these further operations conditions is advantageous.
For that purpose, usage models for different kinds of vehicles are considered. Based
on these mathematical descriptions, representative routes for multiple user
types can be simulated in a predefined geographical region. The obtained individual
driving schedules consist of coordinates of starting and target points and can
thus be routed on the true road network. Additionally, different factors, like the
topography, can be evaluated along the track.
Available statistics resulting from travel survey are integrated to guarantee reasonable
trip length. Population figures are used to estimate the number of vehicles in
contained administrative units. The creation of thousands of those geo-referenced
trips then allows the determination of realistic measures of the durability loads.
Private as well as commercial use of vehicles is modeled. For the former, commuters
are modeled as the main user group conducting daily drives to work and
additional leisure time a shopping trip during workweek. For the latter, taxis as
example for users of passenger cars are considered. The model of light-duty commercial
vehicles is split into two types of driving patterns, stars and tours, and in
the common traffic classes of long-distance, local and city traffic.
Algorithms to simulate reasonable target points based on geographical and statistical
data are presented in detail. Examples for the evaluation of routes based
on topographical factors and speed profiles comparing the influence of the driving
style are included.
In der Automobilindustrie muss der Nachweis von Bauteilzuverlässigkeiten auf statistischen Verfahren basieren, da die Bauteilfestigkeit und Kundenbeanspruchung streuen. Die bisherigen Vorgehensweisen der Tests führen häufig Fehlentscheidungen bzgl. der Freigabe, was unnötige Design-Änderungen und somit hohe Kosten bedeuten kann. In vorliegender Arbeit wird der Ansatz der partiellen Durchläuferzählung entwickelt, welche die statische Güte der bisherigen Testverfahren (Success Runs) erhöht.
Grey-box modelling deals with models which are able to integrate the following two kinds of information: qualitative (expert) knowledge and quantitative (data) knowledge, with equal importance. The doctoral thesis has two aims: the improvement of an existing neuro-fuzzy approach (LOLIMOT algorithm), and the development of a new model class with corresponding identification algorithm, based on multiresolution analysis (wavelets) and statistical methods. The identification algorithm is able to identify both hidden differential dynamics and hysteretic components. After the presentation of some improvements of the LOLIMOT algorithm based on readily normalized weight functions derived from decision trees, we investigate several mathematical theories, i.e. the theory of nonlinear dynamical systems and hysteresis, statistical decision theory, and approximation theory, in view of their applicability for grey-box modelling. These theories show us directly the way onto a new model class and its identification algorithm. The new model class will be derived from the local model networks through the following modifications: Inclusion of non-Gaussian noise sources; allowance of internal nonlinear differential dynamics represented by multi-dimensional real functions; introduction of internal hysteresis models through two-dimensional "primitive functions"; replacement respectively approximation of the weight functions and of the mentioned multi-dimensional functions by wavelets; usage of the sparseness of the matrix of the wavelet coefficients; and identification of the wavelet coefficients with Sequential Monte Carlo methods. We also apply this modelling scheme to the identification of a shock absorber.