Airborne Sound Analysis for the Detection of Bearing Faults in Railway Vehicles with Real-World Data
In this paper, we address the challenging problem of detecting bearing faults in railway vehicles by analyzing acoustic signals recorded during regular operation. For this, we introduce Mel Frequency Cepstral Coefficients (MFCCs) as features, which form the input to a simple Multi-Layer Perceptron classifier. The proposed method is evaluated with real-world data that was obtained for state-of-the-art commuter railway vehicles in a measurement campaign. The experiments show that with the chosen MFCC features bearing faults can be reliably detected even for bearing damages that were not included in training.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Novel features for the detection of bearing faults in railway vehicles
{In this paper, we address the challenging problem of detecting bearing faults from vibration signals. For this, several time- and frequency-domain features have been proposed already in the past. However, these features…
Audio Signal ProcessingFault DetectionQuadratic Time-Frequency Analysis of Vibration Signals for Diagnosing Bearing Faults
Diagnosis of bearing faults is paramount to reducing maintenance costs and operational breakdowns. Bearing faults are primary contributors to machine vibrations, and analyzing their signal morphology offers insights into…
Exploring Sound vs Vibration for Robust Fault Detection on Rotating Machinery
Robust and real-time detection of faults on rotating machinery has become an ultimate objective for predictive maintenance in various industries. Vibration-based Deep Learning (DL) methodologies have become the de facto …
Fault DetectionReal-Time Vibration-Based Bearing Fault Diagnosis Under Time-Varying Speed Conditions
Detection of rolling-element bearing faults is crucial for implementing proactive maintenance strategies and for minimizing the economic and operational consequences of unexpected failures. However, many existing techniq…
Fault DiagnosisStatistical Batch-Based Bearing Fault Detection
In the domain of rotating machinery, bearings are vulnerable to different mechanical faults, including ball, inner, and outer race faults. Various techniques can be used in condition-based monitoring, from classical sign…
Defect DetectionFault Detection