Learning objectives
Knowledge and ability to understand: through the lectures held during the course, the student will acquire the methods and knowledge necessary to understand and understand the fundamentals of classification and analysis of measured signals.
Ability to apply knowledge and understanding:
Through practical exercises carried out in the classroom and in the computer lab, students learn how to apply the knowledge acquired in a real design context, as well as in multidisciplinary or unfamiliar contexts.
The possibility of carrying out an interdisciplinary and optional year's work in a group allows the student to extend and apply with a practical activity, on a reduced scale, the knowledge acquired theoretically relating to the analysis of a real case and the presentation of the results obtained. .
Autonomy of judgment
The student must be able to understand and critically evaluate the data analysis processes and algorithms in order to be able to identify the most suitable application solutions
Communication skills
Through lectures, discussions with the teacher and the eventual year project, the student acquires the specific vocabulary inherent in the world of diagnostics. At the end of the course, the student is expected to be able to transmit, in oral and written form, the main contents of the course, such as ideas, engineering problems and related solutions. The student must communicate their knowledge with adequate means, therefore for the resolution of numerical problems the use of tools commonly used in the sector, such as tables, plant diagrams, flow charts, numerical spreadsheets, is expected.
Learning ability
The student who has attended the course will be able to deepen their knowledge of diagnostics and monitoring through the independent consultation of specialized texts, scientific or popular magazines, even outside the topics strictly covered in class, in order to effectively address entering the world of work or undertaking subsequent training courses.
Prerequisites
There are no mandatory prerequisites. Metrology and Devices and Vibration Mechanics exams are recommended.
Course unit content
The course aims to provide the student with the general classification and analysis criteria of the measured signals. Time and frequency domain analysis, modal analysis and basic diagnostic concepts will be addressed.
Full programme
Time domain signals.
Classification of signals and their characteristics: logic and analog signals, stationary and non-stationary, random and deterministic.
Time domain analysis: statistical parameters and correlation.
Fundamental statistical parameters for the description of signals over time.
Correlation function.
Advanced signal acquisition techniques.
Recall and in-depth study of concepts dealt with in the course of Mechanical and Thermal Measurements; acquisition strategies and advanced sampling techniques.
The convolution integral and the convolution theorem.
Dirac delta function, impulse response; convolution integral, convolution theorem, frequency response.
Analysis of signals in the frequency domain.
Recall on Fourier algorithms, direct and inverse transform; aliasing, frequency resolution, leakage and fenestration as applications of the convolution integral.
Characterization of a system in the frequency domain: spectra, self-spectra, cross-spectra, coherence; estimate of the frequency response function.
Hilbert transform, cepstrum, time-frequency transforms
Bibliography
- J. Bendat and A.G. Piersol: Engineering applications of correlation and spectral analysis, John Wiley and Sons
- J. Bendat and A.G. Piersol: Random Data, John Wiley and Sons
- G. D'Antona, Alessandro Ferrero Digital signal processing for Measurement systems, Springer
- A. Brandt, Noise and Vibration Analysis, signal analysis and experimental procedures, Wiley
Teaching methods
The course has a weight of 6 credits, which correspond to 48 hours of lessons. The didactic activities will be conducted by favoring lectures in the classroom alternated with exercises in the computer lab. During the lectures the topics of the course are dealt with from a theoretical-planning point of view, in order to favor a deep understanding of the issues and to bring out any foreknowledge on the topics in question by the trainees.
During the exercises carried out in the classroom, during which it is possible to use personal calculation tools such as computers, students will be required to apply the theory to an exercise, a real case study or a project developed according to the methodological criteria illustrated in the lessons and in the bibliographic and didactic material.
The possibility of carrying out in a group (3 or 4 people maximum) an interdisciplinary and optional year's work, allows the student to extend and apply with a practical activity, on a reduced scale, the knowledge acquired theoretically relating to analysis and identification of real systems.
The slides and notes used to support the lessons will be uploaded at the beginning of the course on the Elly platform.
Notes, transparencies, spreadsheets, tables and all shared material are considered an integral part of the teaching material. Non-attending students are reminded to check the teaching material available and the information provided by the teacher through the Elly platform, the only communication tool used for direct teacher / student contact.
On this platform, day by day, the topics covered in class are indicated which will then constitute the index of contents in view of preparation for the final exam.
Assessment methods and criteria
Verification of learning involves a written test based on open-ended and non-open-ended questions lasting about 1 hour. The test normally consists of 5-7 questions that may concern theoretical contents, demonstrations, exercises dealt with during the course; The weight of each question is assessed at each written test and if different from 1 communicated to the students. The test is passed if it reaches a score of at least 18 points. Honors are awarded in the case of achieving the maximum score on each item to which the mastery of the disciplinary vocabulary is added.
Other information
Additional materials and links will be given during the course
2030 agenda goals for sustainable development
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