Bearing Defect Detection Using On-Board Accelerometer Measurements

Abstract

Vibration signatures of defective roller bearings on railroad freight cars were analyzed in an effort to develop an algorithm for detecting bearing defects. The effort is part of a project to develop an on-board condition monitoring system for freight trains. The Office of Research and Development of the Federal Railroad Administration (FRA) is sponsoring the project. The measurements were made at the Transportation Technology Center (TTC) in Pueblo, CO on July 26 – 29, 1999 during the Phase III Field Test of the Improved Wayside Freight Car Roller Bearing Inspection Research Program sponsored by FRA and the Association of American Railroads (AAR). Wheel sets with specific roller bearing defects were installed on a test train consisting of 8 freight cars designed to simulate revenue service. The consist also contained non-defective roller bearings. Accelerometers were installed on the inboard side of the bearing adapters to measure the vibration signatures during the test. Signatures of both defective and non-defective bearings were recorded. The data were recorded on Sony Digital Audio Tape (DAT) Recorders sampling at a rate of 48 K samples per second. We used both ordinary and envelope spectral analysis to analyze the data in an effort to detect features that could be related to known defects. The spectra of non-defective bearings show no remarkable features at bearing defect frequencies. In general, the ordinary spectra of defective bearings do not exhibit remarkable features at the bearing defect frequencies. In contrast, the envelope spectra of defective bearings contain a number of highly resolved spectral lines at these frequencies. In several cases the spectral lines could be related to specific bearing defects. Based on the analysis performed to date, the envelope spectrum technique provides a promising method for detecting defects in freight car roller bearings using an on-board condition monitoring system.

Self adaptive multi-scale morphology AVG-Hat filter and its application to fault feature extraction for wheel bearing

“…Moreover, the MFs with the adjacent SE scales have the similar filtering effect. In order to improve the multi-scale MF operation efficiency, the SE scales are set to λ = [2,6,10,14,18,22,26,30,34,36,40,44,48,52,56,60,64,68,72,76,80,84,88,92,96,100,104,108,112]. Thus, the multi-scale AVG-Hat MF has a total of 29 SEs.…”

“…Due to the heavy-load and long-term alternating stress operating conditions, wheel bearing can easily happen some of failures, such as abrasion, pitting, spalling, etc [1]. The localized defects of the wheel bearings act as the main failure type of railroad trains, may lead to serious accidents and great costs for the railway industry [2][3]. Therefore, developing the techniques of condition monitoring and fault diagnosis for wheel bearing is an important mission to guarantee the trains for passengers and freight transportation to operate safely and stably, especially for the rapid development of trains' speed over the last decade.…”

Diagnostics of the drive shaft bearing based on vibrations in the high-frequency range as a part of the vehicle's self-diagnostic system

“…Considering the above, the vibroacoustic signal is a very good carrier of machine diagnostic information. It is particularly useful for monitoring the technical condition of bearings, which is confirmed by studies [13,15,19,21,23,40]. Analyses of the vibration signal in the frequency domain (e.g., determining the frequencies of characteristic failures of rolling bearing components [13,21]) are an effective way to detect bearing malfunctions without disassembly.…”

“…It is particularly useful for monitoring the technical condition of bearings, which is confirmed by studies [13,15,19,21,23,40]. Analyses of the vibration signal in the frequency domain (e.g., determining the frequencies of characteristic failures of rolling bearing components [13,21]) are an effective way to detect bearing malfunctions without disassembly. Fourier and Hilbert transforms [21,40], wavelet transforms [15], and statistical tools in the form of various metrics (e.g., kurtosis, RMS, crest factor, etc.)…”

A Data-Collection and Fault Diagnosis System of Train Bearing Based on the C# and Multi-Wireless-Terminal

“…There are hundreds of rolling bearings in a train with a significant relation for the train running [2]. As reported, bearing failure is the most common type of train faults [3], [4]. Therefore, the classification of rolling element bearing fault is an effective and necessary means to judge the bearing state and predict the lifetime so that some measures can be taken to avoid the occurrence of the catastrophic results.…”