Checking date: 28/11/2025 13:19:50


Course: 2025/2026

Advanced Technologies in Analysis and diagnostic of machinery
(16166)
Master in Industrial Mechanical (Plan: 274 - Estudio: 265)
EPI


Coordinating teacher: GOMEZ GARCIA, MARIA JESUS

Department assigned to the subject: Mechanical Engineering Department

Type: Compulsory
ECTS Credits: 4.0 ECTS

Course:
Semester:




Requirements (Subjects that are assumed to be known)
Machine Mechanics Machine Theory Mechanical Enginering fundamentals.
Objectives
Diagnosis of mechanical mechanisms using data analysis of mechanical sensors masurements (Matlab). The student will learn: -Advanced techniques for mechanical vibration analysis -Mechanical Systems identification based on data analysis
Learning Outcomes
Description of contents: programme
1. Introduction to mechanical signals in the time and frequency domains and industrial maintenance. Classification of signals. o Introduction to industrial maintenance and predictive maintenance. o Recognize signal types: deterministic or random, in power or energy. o Analyze a real experimental case of a periodic signal from a Fourier series representation. o Synthesize a transient (non-periodic) mechanical signal using harmonic components. o Engineering units for ¿one side¿ and ¿two sided¿ spectra, depending on the signal type. o Interpret the response of a resonating mechanical system to both periodic and transient excitation in the time and frequency domains. o Investigate the variability of the parameters or functions used to describe random signals. o Introduction to programming with Matlab: signal processing 2. Linear systems: filtering, TDA, and spectral analysis. o Describe continuous linear mechanical systems and show their responses to typical excitation signals. o Explore the response of an accelerometer to a transient with noise. Compare the performance of two accelerometers and define criteria for choosing one. o Advanced aspects of filtering. Linear phase filters. Application to the case of transient mechanical signals. o Signal extraction capability using the TDA (time domain averaging) technique. o Explore how to eliminate harmonic interference using the TDA technique. o Use the FFT (Fourier Transform Function) algorithm to analyze a spectrum with EU (engineering units). o Investigate the existence of discretization errors and the effect of window filtering on a harmonic signal. 3. Sampling theory. o See the relationships between ¿t, NFFT, and ¿f for DTF calculations via FFT. o Demonstrate quantization error. o Show the effect of associating the dynamic range of data acquisition with the measured signal. o Anti-aliasing filters. o Demonstrate the periodicity inherent in DFT. o Model-based signal processing. 4. Applications to the diagnosis of rotating machines. o Study the application of pre-filtering to a signal showing the impact pulses generated by a bearing failure. o Analyze gear vibrations and compare theoretical frequencies with measured values. o Analysis of the spectrum of signals from bearings and gears measured in mechanical structures. o Proposals for a physical explanation of the measured vibration signal coming from a rotating machine.
Learning activities and methodology
Classroom (65% of ETCS) + homework (30% of ETCS) + conferences and seminars (5% of ETCS).
Assessment System
  • % end-of-term-examination/test 50
  • % of continuous assessment (assigments, laboratory, practicals...) 50

Calendar of Continuous assessment


Basic Bibliography
  • S. BRAUN. DISCOVER SIGNAL PROCESSING. An interactive guide for engineers.. willey. 2008
Additional Bibliography
  • John G. Proakis y Dimitris G. Manolakis. Digital Signal Processing (4th Edition). Prentice Hall. 2006
  • Robert B. Randall. Vibration-based Condition Monitoring: Industrial, Aerospace and Automotive Applications. John Wiley & Sons, Ltd. 2010

The course syllabus may change due academic events or other reasons.