OVERVIEW

The Measurement Systems module addresses signal analysis as a key element of measurement systems used in mechanical engineering. Quantities measured on machines, structures, and plants are often dynamic signals affected by noise, associated with transient phenomena, and characterized by complex dynamics. Their interpretation is essential for extracting information from experimental data and is crucial in structural monitoring, diagnostics, vibration analysis, system characterization, and model validation. The course provides the foundations for correctly acquiring signals from a measurement system and processing to extract information from raw data for the analysis and design of mechanical systems.

AIMS AND CONTENT

LEARNING OUTCOMES

The objective of the course is to provide students with the necessary skills and knowledge to design measurement systems. The primary objectives of this programme are threefold: firstly, to provide students with a comprehensive understanding of the methodologies employed in the measurement of mechanical and thermal quantities; secondly, to empower students to define the general specifications of a measurement system; thirdly, to facilitate the adept planning of experimental tests on a mechanical system, the adept execution of digital data acquisition, the selection of the most suitable pre-processing methodology, and the ability to perform analyses aimed at identifying the characteristic parameters of the systems themselves. The course encompasses the teaching and application of techniques for numerical processing in the time and frequency domains, in addition to concepts relating to analogue/digital signal conditioning techniques.

AIMS AND LEARNING OUTCOMES

In terms of acquired knowledge and understanding, students will be able to:

• describe and discuss data acquisition strategies;
• identify the most appropriate strategies for data analysis in the time and frequency domains in relation to specific engineering problems;
• describe procedures for implementing signal analysis techniques;
• describe the main theorems associated with the analysis of linear time-invariant systems;
• describe advanced signal analysis techniques;
• describe the use of data analysis techniques for system identification and diagnostics.

With regard to the ability to apply the acquired knowledge and understanding, students will be able to:

• identify the most appropriate data acquisition parameters for a specific mechanical system;
• analyse a signal in the time and frequency domains;
• avoid leakage and aliasing errors;
• assess the need for data filtering and select the most appropriate procedure;
• estimate the transfer function in SISO systems;
• assess the quality of measured data;
• identify system parameters and characteristics.

Through laboratory activities, students will also acquire the ability to make independent judgements, meaning to:

• implement Matlab code to analyse previously acquired signals;
• select the most suitable signal processing technique to extract specific information about the system;
• infer the state of a mechanical system from the analysis of measured signals;
• compare different signal processing techniques for the solution of specific engineering problems.

PREREQUISITES

There are no specific requirements.

TEACHING METHODS

Teaching activities will mainly consist of classroom lectures alternated with practical sessions in a computer laboratory. During the lectures, the course topics will be addressed from a theoretical and design-oriented perspective, in order to foster a deep understanding of the subject matter and to bring out any prior knowledge students may have of the topics covered.

During the computer laboratory sessions, students will be required to apply the theory to an exercise or to a real case study, developed according to the methodological criteria presented in the lectures and in the bibliographic and teaching material.

Slides, lecture notes, and all material used to support lectures and laboratory sessions will be uploaded during the course to the Aulaweb platform and will constitute an integral part of the teaching material. Non-attending students are reminded to check the available teaching material and the instructions provided by the lecturer through the Aulaweb platform.

Students with certification of specific learning disorders, disabilities, or other special educational needs are advised to contact the lecturer at the beginning of the course in order to agree on teaching and examination arrangements that, while respecting the learning objectives of the course, take individual learning needs into account.

SYLLABUS/CONTENT

Introductory concepts

• Classification of signals and their characteristics: stationary and non-stationary signals, random and deterministic signals.
• Fundamentals of signal acquisition. Synchronous and asynchronous sampling.

Time-domain analysis: statistical parameters and correlation

• Basic and time-varying statistical parameters.
• Correlation functions.

Frequency-domain signal analysis

• Band analysis, Fourier series, direct and inverse Fourier transform, discrete Fourier transform, frequency resolution.
• Leakage and aliasing.
• Windowing techniques.
• Spectra and cross-spectra.
• Stochastic processes, power spectra, and smoothing techniques (Welch’s method).

Analysis of linear time-invariant systems

• Dirac delta function, impulse response, convolution integral, and convolution theorem.
• Frequency response function of mechanical systems and its estimation.
• Coherence function.

Advanced signal analysi techniques

• Hilbert transform.
• Cepstrum.
• Time-frequency transform.

RECOMMENDED READING/BIBLIOGRAPHY

• J. S. Bendat and A. G. Piersol, Engineering Applications of Correlation and Spectral Analysis, John Wiley & Sons.
• J. S. Bendat and A. G. Piersol, Random Data, John Wiley & Sons.
• G. D’Antona and A. Ferrero, Digital Signal Processing for Measurement Systems, Springer.
• A. Brandt, Noise and Vibration Analysis: Signal Analysis and Experimental Procedures, Wiley.

TEACHERS AND EXAM BOARD

LESSONS

LESSONS START

https://corsi.unige.it/en/corsi/11959/studenti-orario

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

The examination consists of a written test.

ASSESSMENT METHODS

The written test consists of a series of open-ended questions and exercises.

Students are expected to demonstrate a solid theoretical knowledge of the topics covered in the course. They must also show a strong ability to select and correctly apply methods and algorithms in assigned scenarios, both through exercises and through theoretical questions based on simulated scenarios.

FURTHER INFORMATION

Please ask the professor for any additional information not included in the teaching schedule.