Caricamento in corso...
Gearbox failure analysis and monitoring
02TAORO
A.A. 2024/25
Course Language
Inglese
Degree programme(s)
Doctorate Research in Ingegneria Meccanica - Torino
Course structure
| Teaching | Hours |
|---|---|
| Lezioni | 20 |
Lecturers
| Teacher | Status | SSD | h.Les | h.Ex | h.Lab | h.Tut | Years teaching |
|---|---|---|---|---|---|---|---|
| Daga Alessandro Paolo | Ricercatore a tempo det. L.240/10 art.24-B | IIND-02/A | 6 | 0 | 0 | 0 | 3 |
Co-lectures
Context
| SSD | CFU | Activities | Area context | *** N/A *** |
|---|
Il corso, offerto in lingua inglese, si propone di fornire una visione analitica, numerica e sperimentale delle problematiche legate all'usura e alla manutenzione di scatole del cambio. Il corso si concentrerà sulle tecniche di elaborazione del segnale per sistemi integrati di monitoraggio delle vibrazioni.
The course, offered in English, aims to provide an analytical, numerical, and experimental vision of the problems related to the wear and maintenance of gearboxes. The course will focus on signal processing techniques for integrated vibration monitoring systems.
Conoscenze di base in matematica, statistica e programmazione
Basic math, statistics, and programming/coding knowledge
1. GEARBOX FAILURE
1.1. Introduction to gear failures: how to recognize them, what causes them, how to avoid them.
1.2. Typologies of failure, AGMA classification: wear, scuffing, Hertzian fatigue (macropitting, micropitting, surface initiated failure, subcase fatigue), plastic deformation, fracture, bending fatigue, cracking.
1.3. Typologies of failure (pitting, bending) analyzed by Standards (AGMA, ISO,…) that may be taken into account in the design phase.
1.4. Criteria for prediction of scuffing and micropitting risk, effect of lubrication conditions.
1.5. Crack paths identification (in the design phase) for light weight gears; effect of centrifugal loads.
2. VIBRATION MONITORING: an introduction
2.1. Introduction to gear monitoring techniques
2.2. Different technologies and tools available nowadays
2.3. Measurement chain for vibration signature
2.4. Fundamental differences between gears and bearings signals
2.5. Meshing frequencies (regular and satellite gearboxes), bearing signature
2.6. Literature classical analysis : RMS, Crest, Kurtosis, Peaks, Cepstrum and specific indicators (NA4 and NA4*, M6A, M8A, and FM4…. )
3. ADVANCED SIGNAL PROCESSING for DIAGNOSTICS
3.1. Time Varying Fourier Transform (TVFT)
3.2. Spectrum evolution in time
3.3. Transmission Error
3.4. Computed Order Tracking & Synchronous Average
3.5. Linear Prediction
3.6. Discrete Random Separation
3.7. Spectral Kurtosis
3.8. EMD and EEMD
4. DAMAGE DETECTION andCLASSIFICATION: a STATISTICAL and MACHINE LEARNING POINT OF VIEW
4.1. Statistical analysis and detection
4.2. Outlier analysis
4.3. Principal Components Analysis
4.4. Density Estimation
4.5. Classification
1. GEARBOX FAILURE
1.1. Introduction to gear failures: how to recognize them, what causes them, how to avoid them.
1.2. Typologies of failure, AGMA classification: wear, scuffing, Hertzian fatigue (macropitting, micropitting, surface initiated failure, subcase fatigue), plastic deformation, fracture, bending fatigue, cracking.
1.3. Typologies of failure (pitting, bending) analyzed by Standards (AGMA, ISO,…) that may be taken into account in the design phase.
1.4. Criteria for prediction of scuffing and micropitting risk, effect of lubrication conditions.
1.5. Crack paths identification (in the design phase) for light weight gears; effect of centrifugal loads.
2. VIBRATION MONITORING: an introduction
2.1. Introduction to gear monitoring techniques
2.2. Different technologies and tools available nowadays
2.3. Measurement chain for vibration signature
2.4. Fundamental differences between gears and bearings signals
2.5. Meshing frequencies (regular and satellite gearboxes), bearing signature
2.6. Literature classical analysis : RMS, Crest, Kurtosis, Peaks, Cepstrum and specific indicators (NA4 and NA4*, M6A, M8A, and FM4…. )
3. ADVANCED SIGNAL PROCESSING for DIAGNOSTICS
3.1. Time Varying Fourier Transform (TVFT)
3.2. Spectrum evolution in time
3.3. Transmission Error
3.4. Computed Order Tracking & Synchronous Average
3.5. Linear Prediction
3.6. Discrete Random Separation
3.7. Spectral Kurtosis
3.8. EMD and EEMD
4. DAMAGE DETECTION and CLASSIFICATION: a STATISTICAL and MACHINE LEARNING POINT OF VIEW
4.1. Statistical analysis and detection
4.2. Outlier analysis
4.3. Principal Components Analysis
4.4. Density Estimation
4.5. Classification
In presenza
On site
Presentazione report scritto
Written report presentation
P.D.1-1 - Febbraio
P.D.1-1 - February