Railway Axle Early Fatigue Crack Detection through Condition Monitoring Techniques

The detection of cracks in rotating machinery is an unresolved issue today. In this work, a methodology for condition monitoring of railway axles is presented, based on crack detection by means of the automatic selection of patterns from the vibration signal measurement. The time waveforms were proc...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Sensors (Basel, Switzerland) Ročník 23; číslo 13; s. 6143
Hlavní autoři: Gomez, María Jesús, Castejon, Cristina, Corral, Eduardo, Cocconcelli, Marco
Médium: Journal Article
Jazyk:angličtina
Vydáno: Switzerland MDPI AG 04.07.2023
MDPI
Témata:
Algorithms
Analysis
Artificial intelligence
automatic alarm value
change point analysis
Classification
Clothing industry
clustering
crack detection
Cracks
Defects
Fatigue
Fatigue testing machines
Fourier transforms
Genetic algorithms
Machinery
Materials
Methods
Neural networks
Railroads
railway axles
Signal processing
Support vector machines
Vibration
Wavelet Analysis
Wavelet Packets Transform
Wavelet transforms
Italy
automatic alarm value
change point analysis
clustering
Wavelet Packets Transform
crack detection
railway axles
ISSN:1424-8220, 1424-8220
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:The detection of cracks in rotating machinery is an unresolved issue today. In this work, a methodology for condition monitoring of railway axles is presented, based on crack detection by means of the automatic selection of patterns from the vibration signal measurement. The time waveforms were processed using the Wavelet Packet Transform, and appropriate alarm values for diagnosis were calculated automatically using non-supervised learning techniques based on Change Point Analysis algorithms. The validation was performed using vibration signals obtained during fatigue tests of two identical railway axle specimens, one of which cracked during the test while the other did not. During the test in which the axle cracked, the results show trend changes in the energy of the vibration signal associated with theoretical defect frequencies, which were particularly evident in the direction of vibration that was parallel to the track. These results are contrasted with those obtained during the test in which the fatigue limit was not exceeded, and the test therefore ended with the axle intact, verifying that the effects that were related to the crack did not appear in this case. With the results obtained, an adjusted alarm value for a condition monitoring process was established.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:1424-8220
1424-8220
DOI:10.3390/s23136143