Comparison of Support Vector Machine-Based Techniques for Detection of Bearing Faults

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Lijun Wang; Shengfei Ji; Nanyang Ji;
  • Publisher: Hindawi
  • Journal: Shock and Vibration (issn: 1070-9622, eissn: 1875-9203)
  • Publisher copyright policies & self-archiving
  • Related identifiers: doi: 10.1155/2018/8174860
  • Subject: Physics | QC1-999 | Article Subject
    arxiv: Computer Science::Machine Learning | Computer Science::Computer Vision and Pattern Recognition | Computer Science::Sound
    acm: ComputingMethodologies_PATTERNRECOGNITION | ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION

This paper presents a method that combines Shuffled Frog Leaping Algorithm (SFLA) with Support Vector Machine (SVM) method in order to identify the fault types of rolling bearing in the gearbox. The proposed method improves the accuracy of fault diagnosis identification... View more
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