Novel features for the detection of bearing faults in railway vehicles

by   Matthias Kreuzer, et al.

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 are usually evaluated on data originating from relatively simple scenarios and a significant performance loss can be observed if more realistic scenarios are considered. To overcome this, we introduce Mel-Frequency Cepstral Coefficients (MFCCs) and features extracted from the Amplitude Modulation Spectrogram (AMS) as features for the detection of bearing faults. Both AMS and MFCCs were originally introduced in the context of audio signal processing but it is demonstrated that a significantly improved classification performance can be obtained by using these features. Furthermore, to tackle the characteristic data imbalance problem in the context of bearing fault detection, i.e., typically much more data from healthy bearings than from damaged bearings is available, we propose to train a One-class SVM with data from healthy bearings only. Bearing faults are then classified by the detection of outliers. Our approach is evaluated with data measured in a highly challenging scenario comprising a state-of-the-art commuter railway engine which is supplied by an industrial power converter and coupled to a load machine.


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 f...

Detection and classification of faults aimed at preventive maintenance of PV systems

Diagnosis in PV systems aims to detect, locate and identify faults. Diag...

Identification of Internal Faults in Indirect Symmetrical Phase Shift Transformers Using Ensemble Learning

This paper proposes methods to identify 40 different types of internal f...

Domain knowledge-informed Synthetic fault sample generation with Health Data Map for cross-domain Planetary Gearbox Fault Diagnosis

Extensive research has been conducted on fault diagnosis of planetary ge...

Addressing Domain Shift via Knowledge Space Sharing for Generalized Zero-Shot Industrial Fault Diagnosis

Fault diagnosis is a critical aspect of industrial safety, and supervise...

A Vision Transformer-Based Approach to Bearing Fault Classification via Vibration Signals

Rolling bearings are the most crucial components of rotating machinery. ...

A reduced-order modeling framework for simulating signatures of faults in a bladed disk

This paper reports a reduced-order modeling framework of bladed disks on...

Please sign up or login with your details

Forgot password? Click here to reset