- Tytuł:
- Diagnosis of air compressor condition using minimum redunduncy maximum relevance (MRMR) algorithim and distance metric based classification
- Autorzy:
-
Al-Bugharbee, Hussein
Samaka, Hatem
Zubaidi, Salah L. - Powiązania:
- https://bibliotekanauki.pl/articles/1955226.pdf
- Data publikacji:
- 2021
- Wydawca:
- Polska Akademia Nauk. Polskie Towarzystwo Diagnostyki Technicznej PAN
- Tematy:
-
condition monitoring
Mahalanobis distance
signal processing
monitorowanie stanu
przetwarzanie sygnałów
odległość Mahalanobisa - Opis:
- Finding a reliable machines condition monitoring technique has been attracted many researchers to avoid the sudden failure in machines and the unexpected consequences. This work proposes a fault diagnosis of air compressors using frequency-based features and distance metric-based classification. The analyzed experimental datasets contain one healthy condition and seven different fault conditions. Features are extracted from the frequency spectrum, then the best feature sets are selected using MRMR algorithm and eventually the classification is conducted using a distance metric classifier. The results demonstrated the automatic classification with more than 97% correct classification rate. The effect of selected feature set size, training sample size on the classification accuracy is also investigated. From the results, this method of analysis can be used for early detection of faults with very great accuracy.
- Źródło:
-
Diagnostyka; 2021, 22, 4; 25-32
1641-6414
2449-5220 - Pojawia się w:
- Diagnostyka
- Dostawca treści:
- Biblioteka Nauki
Artykuł