Die besten Autoren und Fachleute aus der Industrie (von BMW, MAN B&W Diesel AG, DEUTZMOTOR, Mercedes-Benz AG, Volkswagen AG u. a. großen Firmen) schreiben in diesem Handbuch.
Die strömungstechnischen Phänomene sind nicht nur beschrieben und - soweit möglich - mathematisch exakt oder näherungsweise dargestellt, sondern auch weitgehend physikalisch begründet und erklärt. Zum besseren Verständnis werden die Erscheinungen der Fluidmechanik ausgehend von der Festkörpermechanik veranschaulicht, dabei werden Analogien zu anderen Fachgebieten aufgezeigt. Dargestellt sind die Statik und Dynamik sowohl der Flüssigkeiten als auch der Gase und Dämpfe. Bei der Gasdynamik sind Unterschall- und Überschallströmungen einbezogen. Eine Einführung in die moderne numerische Strömungsmechanik, die Computational Fluid Dynamics (CFD), ergänzt den Stoff.
Mit über 100 Übungsbeispielen und kompletten Lösungen. Der Anhang enthält technisch wichtige Tabellen sowie Diagramme für Stoffgrößen und Beiwerte der Strömungstechnik.
Das Buch richtet sich sowohl an Studierende von Fachhochschulen und Technischen Universitäten als auch an Ingenieure in den Bereichen Strömungstechnik und Strömungsmaschinen.
This book offers first a short introduction to advanced supervision, fault detection and diagnosis methods. It then describes model-based methods of fault detection and diagnosis for the main components of gasoline and diesel engines, such as the intake system, fuel supply, fuel injection, combustion process, turbocharger, exhaust system and exhaust gas aftertreatment. Additionally, model-based fault diagnosis of electrical motors, electric, pneumatic and hydraulic actuators and fault-tolerant systems is treated. In general series production sensors are used. It includes abundant experimental results showing the detection and diagnosis quality of implemented faults.
Written for automotive engineers in practice, it is also of interest to graduate students of mechanical and electrical engineering and computer science.
This book treats physically-based as well as experimentally-refined engine models for gasoline and diesel engines and uses them to exemplify the design of various advanced control systems. The procedures, from measurements through simulation to calibration on test benches, are systematically described and demonstrated. The treatment spans not only the stationary but also the dynamic behavior of engines. Several new control regimens are detailed, such as multivariable feedforward and feedback control based on nonlinear net models, combustion pressure and HCCI control. Many new results with signal and process model-based fault diagnosis are used to show how on-board fault diagnosis can be considerably improved.
The book is directed at advanced students working in control, electrical, mechanical and mechatronic engineering and will also be useful for practicing engineers in the field of engine and automotive engineering.
This book is a sequel of the book “Fault-Diagnosis Systems” published in 2006, where the basic methods were described. After a short introduction into fault-detection and fault-diagnosis methods the book shows how these methods can be applied for a selection of 20 real technical components and processes as examples, such as:
Electrical drives (DC, AC)
Fluidic actuators (hydraulic, pneumatic)
Centrifugal and reciprocating pumps
Pipelines (leak detection)
Machine tools (main and feed drive, drilling, milling, grinding)
Also realized fault-tolerant systems for electrical drives, actuators and sensors are presented.
The book describes why and how the various signal-model-based and process-model-based methods were applied and which experimental results could be achieved. In several cases a combination of different methods was most successful.
The book is dedicated to graduate students of electrical, mechanical, chemical engineering and computer science and for engineers.
Mechatronic Systems introduces these developments by considering the dynamic modelling of components together with their interactions. The whole range of elements is presented from actuators, through different kinds of processes, to sensors.
Structured tutorial style takes learning from the basics of unified theoretical modelling, through information processing to examples of system development.
End-of-chapter exercises provide ready-made homework or self-tests.
Offers practical advice for engineering derived from experience with real systems and application-oriented research.
Written by one of the World's leading experts in this progressive field, Mechatronic Systems will be of great value to advanced students working in control, electrical and mechanical engineering and in areas where such definitions are being superseded. It will also prove useful to practising engineers wanting an in-depth understanding of how these disciplines and that of information processing are becoming interlinked.
The book gives an introduction into advanced methods of fault detection and diagnosis (FDD). After definitions of important terms, it considers the reliability, availability, safety and systems integrity of technical processes. Then fault-detection methods for single signals without models such as limit and trend checking and with harmonic and stochastic models, such as Fourier analysis, correlation and wavelets are treated. This is followed by fault detection with process models using the relationships between signals such as parameter estimation, parity equations, observers and principal component analysis. The treated fault-diagnosis methods include classification methods from Bayes classification to neural networks with decision trees and inference methods from approximate reasoning with fuzzy logic to hybrid fuzzy-neuro systems.
Several practical examples for fault detection and diagnosis of DC motor drives, a centrifugal pump, automotive suspension and tire demonstrate applications.
After a short introduction into the required methodology of continuous-time and discrete-time linear systems, the focus is first on the identification of non-parametric models with continuous-time signals employing methods such as Fourier transform, measurement of the frequency response and correlation analysis. Then, the parameter estimation for parametric models is presented with a focus on the method of Least Squares, followed by some of its most prominent modifications. Issues such as parameter estimation for time-variant processes, parameter estimation in closed-loop, parameter estimation for differential equations, continuous time processes and efficient implementations of the algorithms are discussed. The different methods are compared and an outlook is given on non-linear system identification methods, such as neural networks and look-up tables.
Powerpoint slides for a 12-14 week graduate level course can be made available to teachers