TY - JOUR
T1 - A new spectral average-based bearing fault diagnostic approach
AU - Van Hecke, Brandon
AU - Qu, Yongzhi
AU - He, David
AU - Bechhoefer, Eric
PY - 2014/6
Y1 - 2014/6
N2 - The diagnosis of bearing health through the quantification of accelerometer data has been an area of interest for many years and has resulted in numerous signal processing methods and algorithms. This paper proposes a new diagnostic approach that combines envelope analysis, time synchronous resampling, and spectral averaging of vibration signals to extract condition indicators (CIs) used for rolling-element bearing fault diagnosis. First, the accelerometer signal is digitized simultaneously with tachometer signal acquisition. Then, the digitized vibration signal is band pass filtered to retain the information associated with the bearing defects. Finally, the tachometer signal is used to time synchronously resample the vibration data which allows the computation of a spectral average and the extraction of the CIs used for bearing fault diagnosis. The proposed technique is validated using the vibration output of seeded fault steel bearings on a bearing test rig. The result is an effective approach validated to diagnose all four bearing fault types: inner race, outer race, ball, and cage.
AB - The diagnosis of bearing health through the quantification of accelerometer data has been an area of interest for many years and has resulted in numerous signal processing methods and algorithms. This paper proposes a new diagnostic approach that combines envelope analysis, time synchronous resampling, and spectral averaging of vibration signals to extract condition indicators (CIs) used for rolling-element bearing fault diagnosis. First, the accelerometer signal is digitized simultaneously with tachometer signal acquisition. Then, the digitized vibration signal is band pass filtered to retain the information associated with the bearing defects. Finally, the tachometer signal is used to time synchronously resample the vibration data which allows the computation of a spectral average and the extraction of the CIs used for bearing fault diagnosis. The proposed technique is validated using the vibration output of seeded fault steel bearings on a bearing test rig. The result is an effective approach validated to diagnose all four bearing fault types: inner race, outer race, ball, and cage.
KW - Bearing failure
KW - Defect analysis
KW - Mechanical component
KW - Non-destructive testing
KW - Rolling-element bearing
UR - http://www.scopus.com/inward/record.url?scp=84901490333&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84901490333&partnerID=8YFLogxK
U2 - 10.1007/s11668-014-9806-6
DO - 10.1007/s11668-014-9806-6
M3 - Review article
AN - SCOPUS:84901490333
SN - 1547-7029
VL - 14
SP - 354
EP - 362
JO - Journal of Failure Analysis and Prevention
JF - Journal of Failure Analysis and Prevention
IS - 3
ER -