Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Bearing Diagnosis Using Fault Signal Enhancing Teqniques and Data-driven Classification
Linköping University, Department of Electrical Engineering, Vehicular Systems.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Rolling element bearings are a vital part in many rotating machinery, including vehicles. A defective bearing can be a symptom of other problems in the machinery and is due to a high failure rate. Early detection of bearing defects can therefore help to prevent malfunction which ultimately could lead to a total collapse. The thesis is done in collaboration with Scania that wants a better understanding of how external sensors such as accelerometers, can be used for condition monitoring in their gearboxes.

Defective bearings creates vibrations with specific frequencies, known as Bearing Characteristic Frequencies, BCF [23]. A key component in the proposed method is based on identification and extraction of these frequencies from vibration signals from accelerometers mounted near the monitored bearing. Three solutions are proposed for automatic bearing fault detection. Two are based on data-driven classification using a set of machine learning methods called Support Vector Machines and one method using only the computed characteristic frequencies from the considered bearing faults. Two types of features are developed as inputs to the data-driven classifiers. One is based on the extracted amplitudes of the BCF and the other on statistical properties from Intrinsic Mode Functions generated by an improved Empirical Mode Decomposition algorithm. In order to enhance the diagnostic information in the vibration signals two pre-processing steps are proposed. Separation of the bearing signal from masking noise are done with the Cepstral Editing Procedure, which removes discrete frequencies from the raw vibration signal. Enhancement of the bearing signal is achieved by band pass filtering and amplitude demodulation. The frequency band is produced by the band selection algorithms Kurtogram and Autogram.

The proposed methods are evaluated on two large public data sets considering bearing fault classification using accelerometer data, and a smaller data set collected from a Scania gearbox. The produced features achieved significant separation on the public and collected data. Manual detection of the induced defect on the outer race on the bearing from the gearbox was achieved. Due to the small amount of training data the automatic solutions were only tested on the public data sets. Isolation performance of correct bearing and fault mode among multiplebearings were investigated. One of the best trade offs achieved was 76.39 % fault detection rate with 8.33 % false alarm rate. Another was 54.86 % fault detection rate with 0 % false alarm rate.

Place, publisher, year, edition, pages
2019. , p. 69
Keywords [en]
Bearing diagnosis, rolling element bearings, predictive maintenance, signal processing, fault detection, gearbox diagnosis, support vector machine, svm, binary classification, one class classification, machine learning
Keywords [sv]
feldetektion, diagnos, feldiagnos, växellåda, signalbehandling, maskininlärning
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:liu:diva-158240ISRN: LiTH-ISY-EX--19/5240--SEOAI: oai:DiVA.org:liu-158240DiVA, id: diva2:1331487
External cooperation
Scania CV AB
Subject / course
Electrical Engineering
Presentation
2019-06-13, Nollstället, Linköpings universitet, 581 83, Linköping, 13:15 (Swedish)
Available from: 2019-09-04 Created: 2019-06-26 Last updated: 2019-09-04Bibliographically approved

Open Access in DiVA

Bearing_Diagnosis_using_Fault_Signal_Enhancing_Techniques_and_Data-driven_Classification(9241 kB)54 downloads
File information
File name FULLTEXT01.pdfFile size 9241 kBChecksum SHA-512
7611ee5127990ffe3b628804224f1126dc4bd695fbdb374a4ef264b5fe985f01814c743f98e542bb40d14fa881b85a3a1f10c4b504fbb00f5052965373c9b72b
Type fulltextMimetype application/pdf

Search in DiVA

By author/editor
Lembke, Benjamin
By organisation
Vehicular Systems
Signal Processing

Search outside of DiVA

GoogleGoogle Scholar
Total: 54 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 415 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf