1. Introduction to rotating machinery diagnostics and prognostics – Basic principles and problems
2. Data acquisition
3. Signal and non-parametric processing
4. Time domain rotating machinery diagnostics
5. Frequency domain rotating machinery diagnostics
6. Rotating machinery prognostics
7. Applications and laboratory testing

LEARNING OUTCOMES 

One of the objectives of this course is for the student to develop the following abilities / skills:

a) data and information search, analysis and synthesis, using corresponding technologies,

b) adapting to new environments

c) decision making

d) independent work

e) working in an interdisciplinary environment

f) evaluation and self-evaluation

g) encouraging free, creative and deductive thinking

Το άρθρο Machninery Diagnostics and Prognostics εμφανίστηκε πρώτα στο MEAD.

1. Introduction to stochastic signals and systems
2. Fundamental notions of stochastic signals in the time domain
3. Non-parametric estimation of stochastic signals in the time domain
4. Fundamental notions of stochastic signals in the frequency domain
5. Non-parametric estimation of stochastic signals in the frequency domain
6. Theory of stationary and linear stochastic signals and systems
7. Theory and properties of parametric ARMA models
8. ARMA models originating from the sampling of continuous time models
9. Introduction to non-stationary and seasonal stochastic signals
10. Theory of optimal prediction
11. Identification, estimation, and validation of stochastic parametric models
12. Introductory remarks on vector stochastic signals

LEARNING OUTCOMES

The course constitutes a comprehensive introduction into discrete-time stochastic signals and systems, with reference to random vibration. Upon successful completion of the course the student will be in position to:

• Understand the form and basic notions of stationary stochastic signals and systems in the time and frequency domains
• Appreciate their applications in mechanical & aeronautical engineering, as well as in other scientific disciplines
• Mathematically describe stationary, and certain non-stationary, stochastic signals
• Comprehend the basic notions of estimation, as well as the estimation of mathematical models of stochastic signals in the time and frequency domains
• Thoroughly analyze mathematical models for stationary stochastic signals and systems in the time and frequency domains
• Relate the mathematical models to underlying physical systems and their properties
• Perform stochastic signal prediction
• Validate an estimated model
• Model and analyze stationary stochastic signals and systems from the engineering practice using realizations and proper software (such as MATLAB/SIMULINK, R)

Το άρθρο Stochastic Signals and Systems εμφανίστηκε πρώτα στο MEAD.

The content of the course is not specified due to its nature – depended on the internship placement.   The number of the internship placements are assured by the Department via its synergies with public and private industrial enterprises.

LEARNING OUTCOMES

• Ability for students to get in touch with workplaces, acquire new knowledge, participate actively in teamwork and decision making, develop their skills, participate in the design and completion of projects and generally gain work experience.
• Contribution of internships to strengthen the interconnection of educational institutions with the market and development of networking – partnerships.
• Promote modern methods for developing young entrepreneurship
• Qualitative evaluation of the actions so far and the next actions and contribution to improving the career prospects of the students

Το άρθρο Industrial Internship εμφανίστηκε πρώτα στο MEAD.