基于真实世界数据的轨道车辆轴承故障检测的空气声学分析
Airborne Sound Analysis for the Detection of Bearing Faults in Railway Vehicles with Real-World Data
AI总结：
本文提出利用MFCC特征和多层感知机分类器分析常规运营中的声学信号，以检测轨道车辆轴承故障，实验证明该方法能可靠识别训练中未包含的轴承损坏。
摘要：
本文解决了通过分析常规运营中录制的声学信号来检测轨道车辆轴承故障的挑战。为此，我们引入梅尔频率倒谱系数（MFCCs）作为特征，构成简单多层感知机分类器的输入。所提方法用真实世界数据评估，该数据通过针对最先进通勤轨道车辆的测量活动获得。实验表明，使用所选MFCC特征，即使训练中未包含的轴承损坏也能可靠检测。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
