Estimating the Inner Ring Defect Size and Residual Service Life of Freight Railcar Bearings Using Vibration Signatures
Notice bibliographique
Résumé
Abstract There are currently two primary wayside detection systems for monitoring the health of freight railcar bearings in the railroad industry: The Trackside Acoustic Detection System (TADS™) and the wayside Hot-Box Detector (HBD). TADS™ uses wayside microphones to detect and alert the train operator of high-risk defects. However, many defective bearings may never be detected by TADS™ since a high-risk defect is a spall which spans about 90% of a bearing’s raceway, and there are less than 30 systems in operation throughout the United States and Canada. HBDs sit on the side of the rail-tracks and use non-contact infrared sensors to acquire temperatures of bearings as they roll over the detector. These wayside bearing detection systems are reactive in nature and often require emergency stops in order to replace the wheelset containing the identified defective bearing. Train stoppages are inefficient and can be very costly. Unnecessary train stoppages can be avoided if a proper maintenance schedule can be developed at the onset of a defect initiating within the bearing. Using a proactive approach, railcars with defective bearings could be allowed to remain in service operation safely until reaching scheduled maintenance. The University Transportation Center for Railway Safety (UTCRS) research group at the University of Texas Rio Grande Valley (UTRGV) has been working on developing a proactive bearing condition monitoring system which can reliably detect the onset of bearing failure. Unlike wayside detection systems, the onboard condition monitoring system can continuously assess the railcar bearing health and can provide accurate temperature and vibration profiles to alert of defect initiation. This system has been validated through rigorous laboratory testing at UTRGV and field testing at the Transportation Technology Center, Inc. (TTCI) in Pueblo, CO. The work presented here builds on previously published work that demonstrates the use of the onboard condition monitoring system to identify defective bearings as well as the correlations developed for spall growth rates of defective bearing outer rings (cups). The system first uses the root-mean-square (RMS) value of the bearing’s acceleration to assess its health. Then, an analysis of the frequency domain of the acquired vibration signature determines if the bearing has a defective inner ring (cone) and the RMS value is used to estimate the defect size. This estimated size is then used to predict the residual life of the bearing. The methodology proposed in this paper can assist railroads and railcar owners in the development of a proactive and cost-efficient maintenance cycle for their rolling stock.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
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