CoChat
Sign Up
Cited by 44Machine Fault Diagnosis TechniquesFault Detection and Control SystemsGear and Bearing Dynamics Analysis

The DOI matches its Crossref record; 45 of the 47 references it cites were found in a registry or an index.

Novel Bearing Fault Diagnosis Using Gaussian Mixture Model-Based Fault Band Selection

Open access
Maliuk, Andrei S., Prosvirin, Alexander E., Ahmad, Zahoor, Kim, Cheol Hong, Kim, Jong-Myon·2021·Sensors
DOI 10.3390/s21196579PMC PMC8512720
Open this paper in CoChat — free accountOpen PDF

Abstract

This paper proposes a Gaussian mixture model-based (GMM) bearing fault band selection (GMM-WBBS) method for signal processing. The proposed method benefits reliable feature extraction using fault frequency oriented Gaussian mixture model (GMM) window series. Selecting exclusively bearing fault frequency harmonics, it eliminates the interference of bearing normal vibrations in the lower frequencies, bearing natural frequencies, and the higher frequency contents that prove to be useful only for anomaly detection but do not provide any insight into the bearing fault location. The features are extracted from time- and frequency- domain signals that exclusively contain the bearing fault frequency harmonics. Classification is done using the Weighted KNN algorithm. The experiments performed with the data containing the vibrations recorded from artificially damaged bearings show the positive effect of utilizing the proposed GMM-WBBS signal processing to filter out the discriminative data of uncertain origin. All comparison methods retrofitted with the proposed method demonstrated classification performance improvements when provided with vibration data with suppressed bearing natural frequencies and higher frequency contents.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3390/s21196579

Cites

45 of 45 references verified0 citation errors · 0 not checkable

Statistics

Figures

Figure 1 Electric motor fault occurrence.
Figure 1 Electric motor fault occurrence.CC BY · PubMed Central
Figure 2 Examples of bearing damage visual representation: (a) Indentation at the raceway of the outer ring; (b) Small pitting at the raceway of the inner ring.
Figure 2 Examples of bearing damage visual representation: (a) Indentation at the raceway of the outer ring; (b) Small pitting at the raceway of the inner ring.CC BY · PubMed Central
Figure 3 Stages of bearing life.
Figure 3 Stages of bearing life.CC BY · PubMed Central
Figure 4 Modular test rig used for data collection.
Figure 4 Modular test rig used for data collection.CC BY · PubMed Central
Figure 5 Artificially induced bearing faults: (a): EDM trench, (b): drilled hole, (c): electrically engraved pitting.
Figure 5 Artificially induced bearing faults: (a): EDM trench, (b): drilled hole, (c): electrically engraved pitting.CC BY · PubMed Central
Figure 6 Time-domain raw vibration signal plots of: (a) Healthy bearing; (b) Bearing with inner ring fault; (c) Bearing with outer ring fault.
Figure 6 Time-domain raw vibration signal plots of: (a) Healthy bearing; (b) Bearing with inner ring fault; (c) Bearing with outer ring fault.CC BY · PubMed Central
Figure 7 Raw vibration signal spectra of: (a) Healthy bearing; (b) Bearing with inner ring fault; (c) Bearing with outer ring fault.
Figure 7 Raw vibration signal spectra of: (a) Healthy bearing; (b) Bearing with inner ring fault; (c) Bearing with outer ring fault.CC BY · PubMed Central
Figure 8 The pipeline of the proposed methodology.
Figure 8 The pipeline of the proposed methodology.CC BY · PubMed Central
Figure 9 Envelope analysis flowchart.
Figure 9 Envelope analysis flowchart.CC BY · PubMed Central
Figure 10 Squared envelope vibration signal spectra of: (a) Healthy bearing; (b) Bearing with inner ring fault; (c) Bearing with outer ring fault.
Figure 10 Squared envelope vibration signal spectra of: (a) Healthy bearing; (b) Bearing with inner ring fault; (c) Bearing with outer ring fault.CC BY · PubMed Central
Figure 11 Defect frequency harmonics for: (a) Outer ring faults; (b) Inner ring faults.
Figure 11 Defect frequency harmonics for: (a) Outer ring faults; (b) Inner ring faults.CC BY · PubMed Central
Figure 12 Merged OR and IR signal envelope power spectra after multiplication with OR and IR window sets.
Figure 12 Merged OR and IR signal envelope power spectra after multiplication with OR and IR window sets.CC BY · PubMed Central
Figure 13 Classification accuracy and NSD value plot; (a) Mean value and variance of the classification accuracy for each 0.05 iteration of NSD; (b) Accuracy means, variances, and performance scores.
Figure 13 Classification accuracy and NSD value plot; (a) Mean value and variance of the classification accuracy for each 0.05 iteration of NSD; (b) Accuracy means, variances, and performance scores.CC BY · PubMed Central
Figure 14 Weighted KNN.
Figure 14 Weighted KNN.CC BY · PubMed Central
Figure 15 Number of neighbors vs Error rate for the Weighted KNN.
Figure 15 Number of neighbors vs Error rate for the Weighted KNN.CC BY · PubMed Central
Figure 16 Method comparison pipeline.
Figure 16 Method comparison pipeline.CC BY · PubMed Central
Figure 17 The confusion matrix was obtained using the results achieved by the proposed method.
Figure 17 The confusion matrix was obtained using the results achieved by the proposed method.CC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 47 references

Research works printed but not linked: 2

  1. Fitzgerald, A.E., Kingsley, C., and Umans, S.D. (2003). Electric Machinery, McGraw-Hill. [6th ed.].
  2. Esmael, 2013, A Statistical Feature-Based Approach for Operations Recognition in Drilling Time Series, Int. J. Comput. Inf. Syst. Ind. Manag. Appl., 5, 454

Cited by

Sorted by citations

See all 44

Related

See all

Metadata

Authors
Maliuk, A. S., Prosvirin, A. E., Ahmad, Z., Kim, C. H., & Kim, J.-M.
Affiliations
  • University of Ulsan (KR)
  • Soongsil University (KR)
Corresponding author
Kim, Jong-Myon
ORCID
Venue
Sensors
Publisher
MDPI AG
Published
2021-10-01
Volume
21
Issue
19
Pages
6579
ISSN
1424-8220 (ISSN-L)
DOI
10.3390/s21196579
PMC
PMC8512720
OpenAlex
W3202586029
Cited by
44
References
47
Citations by year
2026: 4 · 2025: 9 · 2024: 9 · 2023: 9 · 2022: 10 · 2021: 3
FWCI
3.45
Type
journal article
Language
en
Open access
Licence
Locations
  • Sensors (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Ministry of SMEs and Startups (S3106236)
Topics
  • Physical Sciences › Engineering › Control and Systems Engineering › Machine Fault Diagnosis Techniques
  • Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
  • Physical Sciences › Engineering › Mechanical Engineering › Gear and Bearing Dynamics Analysis
Keywords
bearing fault diagnosis, Gaussian mixture model, feature extraction, vibration signal processing, fault classification

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Novel Bearing Fault Diagnosis Using Gaussian Mixture Model-Based Fault Band Selection. https://app.cochat.ai/papers/10.3390/s21196579
The paper
Maliuk, A. S., Prosvirin, A. E., Ahmad, Z., Kim, C. H., & Kim, J.-M. (2021). Novel Bearing Fault Diagnosis Using Gaussian Mixture Model-Based Fault Band Selection. Sensors. https://doi.org/10.3390/s21196579