Achtung:

Sie haben Javascript deaktiviert!
Sie haben versucht eine Funktion zu nutzen, die nur mit Javascript möglich ist. Um sämtliche Funktionalitäten unserer Internetseite zu nutzen, aktivieren Sie bitte Javascript in Ihrem Browser.

Experimentelle Untersuchungen am Bondautomaten Bildinformationen anzeigen
Qualitätsbeurteilung von Kupferbondverbindungen am Schertester Bildinformationen anzeigen
Verlässlichkeitsanalyse an einer Reibkupplung Bildinformationen anzeigen
Schwingungsmessung und -analyse in der Lehre Bildinformationen anzeigen
Transport feiner Pulver mittels Ultraschall Bildinformationen anzeigen

Experimentelle Untersuchungen am Bondautomaten

Qualitätsbeurteilung von Kupferbondverbindungen am Schertester

Verlässlichkeitsanalyse an einer Reibkupplung

Schwingungsmessung und -analyse in der Lehre

Transport feiner Pulver mittels Ultraschall

Lehrstuhl für Dynamik und Mechatronik (LDM)

Condition Monitoring of Technical Systems

Voraussetzungen / Empfehlungen:

The course builds systematically on the basics of Measurement techniques and MATLAB & Simulink, offered by LDM.­­
Die Lehrveranstaltung baut systematisch auf den Grundlagenvorlesungen Messtechnik (LDM) und MATLAB/Simulink (LDM) auf.

Ziel der Veranstaltung:

Condition monitoring plays a key role in maintenance planning and reliability control of technical systems. It has the potential of increasing reliability, availability and safety of technical systems as well as reducing operational and maintenance costs. This course is aimed at providing the basic knowledge in maintenance engineering and in particular condition based maintenance - fault diagnostics and prognostics of technical systems. The course will also equip the students with the necessary tools to develop a condition monitoring approach for a specified technical system as well as evaluate cost benefit analysis of implementing Prognostic and Health Management (PHM) methods. 
Condition Monitoring spielt eine entscheidende Rolle  bei der Erhöhung der Zuverlässigkeit, Verfügbarkeit und Sicherheit technischer Systeme. Diese Veranstaltung vermittelt grundlegende Kenntnisse im Bereich Condition Monitoring, Fehler-Diagnose und Prognose technischer Systeme.

Unterrichtssprache:

Englisch

Zielgruppe:

Students undertaking Mechanical/Industrial Engineering with specialization in Product development, Mechatronics or Students undertaking Computer and Information Technology.
Studierende der Studiengänge Maschinenbau und Wirtschaftsingenieurwesen mit den Vertiefungsrichtungen Produktentwicklung, Mechatronik oder Studierende der Ingenieurinformatik.
 

Inhalt:

In „Condition Monitoring of Technical Systems“ sollen den Studierenden im Sinne der unten aufgeführten Schwerpunkte vermittelt werden.

  • Introduction to PHM
    • Overview of failure analysis and reliability analysis
    • Sources of data
    • Objectives of PHM: Reliability, availability, safety
    • Reliability based PHM
  • Cost and value of Prognostics and Health Management (PHM)
    • Elements of a PHM system, cost of PHM implementation, Discrete Event Simulation (DES), PHM costing based on scheduled maintenance
  • Introduction to condition monitoring (CM)
    • Motivation for condition monitoring, diagnostic methods, prognostic methods, remaining useful lifetime (RUL)
  • CM Data Sources and Feature Extraction
    • Condition monitoring data sources and sensors, data processing, feature extraction, feature selection
  • Fault diagnostics using advanced signal processing
    • Processing of measured response signals, cepstrum, Time Synchronous Averaging (TSA), Short-Time Fourier Transform (STFT), Minimum Entropy Deconvolution (MED), Kurtogram/spectral analysis
  • Overview of machine learning (ML) algorithms
    • Introduction to machine learning, unsupervised-clustering, supervised-classification/regression, specialized algorithms: support vector machine (SVM), Extreme Learning Machines (ELM), Random Forests (RF)
  • Data driven PHM
    • Health state estimation, mapping features to RUL, mapping features to health index (HI), virtual HI, similarity based measure
  • PHM based on filtering problem
    • Recursive Bayes filter, Particle filtering, Application of particle filtering in prognostics, integrating uncertainties
  • Model based PHM
    • Diagnosis, characterization of faults, discrete event simulation, continuous systems, model based prognostic, modeling uncertainties
  • Structural Health Monitoring (SHM)
    • Motivation for SHM, monitoring techniques, passive monitoring, active monitoring, integration of prognostics in SHM
  • Prognostic Performance Metrics
    • Uncertainties in prognostics, role of prognostic metrics, selected prognostic metrics, diagnostic metrics
  • Emerging PHM Technologies
    • Ensemble of algorithms, deep learning, application of deep learning in PHM, Big Data Analytics, Predictive Maintenance 4.0 (PdM4.0)

Ergänzende Veranstaltungen:

Übung "Condition Monitoring of Technical Systems"

  • Maintenance planning and costing
    • Cost estimation for scheduled and unscheduled maintenance events
  • Cost and value of Prognostics and Health Management (PHM)
    • PHM cost estimation based on scheduled maintenance
  • Introduction to condition monitoring (CM)
    • RUL estimation based on reliability analysis
  • CM Data Sources and Feature Extraction
    • Data processing, feature extraction, feature selection
  • Fault diagnostics using advanced signal processing
    • Cepstrum, Time Synchronous Averaging (TSA), Short-Time Fourier Transform (STFT), Minimum Entropy Deconvolution (MED), Kurtogram/spectral analysis
  • Overview of machine learning (ML) algorithms
    • Application of machine learning algorithms for clustering, classification, regression
  • Data driven PHM
    • Health state estimating, mapping features to RUL, mapping features to health index (HI), virtual HI
  • PHM based on filtering problem
    • Application of particle filtering in prognostics, integrating uncertainties
  • Model Based PHM
    • Model based diagnostic and prognostic
  • Structural Health Monitoring (SHM)
    • Application of active sensing for prognostics
  • Prognostic Performance Metrics
    • Evaluation of prognostic methods
  • Emerging PHM Technologies
    • Ensemble of algorithms, deep learning

Literatur:

  1. Vachtsevanos, G.; Lewis, F.; Roemer, M; Hess, A; Wu, B. Intelligent Fault Diagnosis and Prognosis for Engineering Systems.  John Wiley & Sons Ltd, 2006
  2. Robert Bond Randall. Vibration-Based Condition Monitoring: Industrial, Aerospace and Automotive Applications.  John Wiley & Sons Ltd, 2011
  3. Dhillon, B.S; Engineering Maintenance: A Modern Approach, CRC Press, 2006
Kontakt

Prof. Dr.-Ing. habil. Walter Sextro

Lehrstuhl für Dynamik und Mechatronik (LDM)

Lehrstuhlinhaber

Walter Sextro
Telefon:
+49 5251 60-1801
Telefon:
(+49) 01520 8958961
Fax:
+49 5251 60-1803
Büro:
P1.3.31.1
Web:

Sprechzeiten:
Nach Vereinbarung

Die Universität der Informationsgesellschaft