Article
Pre-Processing Method to Improve Cross-Domain Fault
Diagnosis for Bearing
Taeyun Kim and Jangbom Chai *
Citation: Kim, T.; Chai, J.
Pre-Processing Method to Improve
Cross-Domain Fault Diagnosis for
Bearing. Sensors 2021, 21, 4970.
https://doi.org/10.3390/s21154970
Academic Editors: Hamed Badihi,
Tao Chen and Ningyun Lu
Received: 23 June 2021
Accepted: 17 July 2021
Published: 21 July 2021
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Machine Diagnosis Laboratory, Department of Mechanical Engineering, Ajou University, Suwon 16499, Korea;
lonoa91@ajou.ac.kr
* Correspondence: jbchai@ajou.ac.kr
Abstract:
Models trained with one system fail to identify other systems accurately because of domain
shifts. To perform domain adaptation, numerous studies have been conducted in many fields and
have successfully aligned different domains into one domain. The domain shift problem is caused by
the difference of distributions between two domains, which is solved by reducing this difference.
Source domain data are labeled and used for training the models to extract the features while the
target domain data are unlabeled or partially labeled and only used for aligning. Bearings play
important roles in rotating machines, so many artificial intelligent models have been developed to
diagnose bearings. Bearing diagnosis has also faced a domain shift problem due to various operating
conditions such as experimental environment, number of balls, degree of defects, and rotational
speed. Cross-domain fault diagnosis has been successfully performed when the systems are the
same but operating conditions are different. However, the results are poor when diagnosing different
bearing systems because the characteristics of the signals such as specific frequencies depend on
the specifications. In this paper, the pre-processing method was used for improving the diagnosis
without prior knowledge such as fault frequencies. The signals were first transformed to a common
pattern space before entering the models. To develop and to validate the proposed method for
different domains, vibration signals measured from two ball-bearing systems (Case Western Reserve
University datasets and Paderborn University datasets) were used. One dimensional CNN models
were utilized for verification of the proposed method and the results of the models using raw datasets
and pre-processed datasets were compared. Even though each of the ball-bearing systems have
their own specifications, using the proposed method was very helpful for domain adaptation, and
cross-domain fault diagnosis was performed with high accuracy.
Keywords:
bearing fault diagnosis; cross-domain fault diagnosis; domain adaptation; signal
processing; transfer learning
1. Introduction
Rotating machines play a very important role in manufacturing plants. Among the
many parts of rotating machines, bearings have a significant impact on the operation of
rotating machines. Failures of electro-mechanical drive systems and motors are caused
by rolling bearings with high probability [
1
]. Therefore, bearing diagnosis is important
in order to use rotating machines safely and studies on this has been actively conducted.
There are several open datasets which are conducted in various operating conditions
such as Paderborn University datasets (PU) [
1
] and Case Western Reserve University
datasets (CWRU) [
2
]. Smith et al. proposed some signal processing methods that make
the characteristics of faults show more clearly using CWRU datasets and interpreted the
results using the fault frequencies [
3
]. However, as the processing speed of computers and
the size of data that can be stored increase, diagnostic studies using data-driven methods
have rapidly increased. Artificial intelligence algorithms for bearing diagnosis such as
random forest, Bayesian network, support vector machine, neuro-fuzzy, and artificial
Sensors 2021, 21, 4970. https://doi.org/10.3390/s21154970 https://www.mdpi.com/journal/sensors
