Chapter 11
Improving Bearing Diagnostic
Performance by Using New
Discriminatory Fault-Feature Evaluation
Viet Tra and Jong-Myon Kim
Abstract Locating different defect types in bearings using the information of the
characteristic frequencies in the envelope power spectrum of analyzed acoustic emission (AE) signals has been widely utilized. However, this approach only shows
effectiveness as the rotational speed of bearing elements is constant. In contrast,
if the bearing speed frequently alters during operation, the value of these characteristic frequencies is not stable, therefore, it is useless for diagnostic purposes. In
order to resolve this issue, this study proposes an approach that (a) adopts heterogeneous feature modes to extract as many statistical features as possible in transformed
domains (i.e., the time domain, the frequency domain, and the wavelet domain); (b)
explores the most discriminatory features using new feature selection scheme. The
scheme is the combination of the genetic algorithm (GA)-based feature analysis and
the k-nearest neighbors (k-NN); (c) the defect types of a typical bearing are categorized by the k-NN-based classifier. The performance of the proposed method is
validated by two datasets of AE samples measured from our bearing testbed.
11.1 Introduction
It is common knowledge that bearings are important components in induction motors.
However, they are the primary roots of collapse in such motors. Without early detection of incipient defects occurring on the surface of bearing elements, more chances
are the equipment is broken down. This leads to the interruption of productions and
exorbitant repair costs. Therefore, operators need to have timely maintenance of such
bearing elements.
Among common bearing diagnostic methodologies, vibration signal-based
approaches have long been used for the applications of bearing diagnosis, using
informative information lying in the moderate frequency band of the characterizing
vibration signal. However, in recent time acoustic emission (AE) signal-based analyses have gradually placed a vital role in the field of bearing fault diagnosis. The
V. Tra (B) · J.-M. Kim
School of IT Convergence, University of Ulsan, Ulsan 680-749, South Korea
e-mail: traviet.vt@gmail.com
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021
G. Shen et al. (eds.), Advances in Acoustic Emission Technology, Springer Proceedings
in Physics 259, https://doi.org/10.1007/978-981-15-9837-1_11
115
Improving Bearing Diagnostic
Performance by Using New
Discriminatory Fault-Feature Evaluation
Viet Tra and Jong-Myon Kim
Abstract Locating different defect types in bearings using the information of the
characteristic frequencies in the envelope power spectrum of analyzed acoustic emission (AE) signals has been widely utilized. However, this approach only shows
effectiveness as the rotational speed of bearing elements is constant. In contrast,
if the bearing speed frequently alters during operation, the value of these characteristic frequencies is not stable, therefore, it is useless for diagnostic purposes. In
order to resolve this issue, this study proposes an approach that (a) adopts heterogeneous feature modes to extract as many statistical features as possible in transformed
domains (i.e., the time domain, the frequency domain, and the wavelet domain); (b)
explores the most discriminatory features using new feature selection scheme. The
scheme is the combination of the genetic algorithm (GA)-based feature analysis and
the k-nearest neighbors (k-NN); (c) the defect types of a typical bearing are categorized by the k-NN-based classifier. The performance of the proposed method is
validated by two datasets of AE samples measured from our bearing testbed.
11.1 Introduction
It is common knowledge that bearings are important components in induction motors.
However, they are the primary roots of collapse in such motors. Without early detection of incipient defects occurring on the surface of bearing elements, more chances
are the equipment is broken down. This leads to the interruption of productions and
exorbitant repair costs. Therefore, operators need to have timely maintenance of such
bearing elements.
Among common bearing diagnostic methodologies, vibration signal-based
approaches have long been used for the applications of bearing diagnosis, using
informative information lying in the moderate frequency band of the characterizing
vibration signal. However, in recent time acoustic emission (AE) signal-based analyses have gradually placed a vital role in the field of bearing fault diagnosis. The
V. Tra (B) · J.-M. Kim
School of IT Convergence, University of Ulsan, Ulsan 680-749, South Korea
e-mail: traviet.vt@gmail.com
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021
G. Shen et al. (eds.), Advances in Acoustic Emission Technology, Springer Proceedings
in Physics 259, https://doi.org/10.1007/978-981-15-9837-1_11
115