2013
DOI: 10.3390/e15020416
|View full text |Cite
|
Sign up to set email alerts
|

Multi-Scale Analysis Based Ball Bearing Defect Diagnostics Using Mahalanobis Distance and Support Vector Machine

Abstract: Abstract:The objective of this research is to investigate the feasibility of utilizing the multi-scale analysis and support vector machine (SVM) classification scheme to diagnose the bearing faults in rotating machinery. For complicated signals, the characteristics of dynamic systems may not be apparently observed in a scale, particularly for the fault-related features of rotating machinery. In this research, the multi-scale analysis is employed to extract the possible fault-related features in different scale… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1
1

Citation Types

0
48
0

Year Published

2013
2013
2023
2023

Publication Types

Select...
9
1

Relationship

1
9

Authors

Journals

citations
Cited by 89 publications
(48 citation statements)
references
References 43 publications
0
48
0
Order By: Relevance
“…Mahalanobis distance (MD) [50] is another popular feature selection method. MD measures the distance between various datasets of two different classes.…”
Section: Feature Selectionmentioning
confidence: 99%
“…Mahalanobis distance (MD) [50] is another popular feature selection method. MD measures the distance between various datasets of two different classes.…”
Section: Feature Selectionmentioning
confidence: 99%
“…Therefore, this particular technique by Equations (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20) …”
Section: Individual Sample Learning Entropy (Isle)mentioning
confidence: 99%
“…As the rapid development of sensing and computing technology, numerous process data can be collected to reflect the variation of different process parameters, in which a large number of various waveform signals are included. Examples of these waveform signals include tonnage signals in the stamping process [1], acoustic data for squirrel cage induction motor fault diagnosis [2] and vibration signals for ball bearing defect diagnostics [3]. These waveform signals contain much process information of the process conditions.…”
Section: Introductionmentioning
confidence: 99%