Fuzzy classification of gear fault using principal component analysis-based fuzzy neural network

Kai Zhou, J. Tang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

Condition assessment of machinery components such as gears is important to maintain their normal operations and thus can bring benefit to their life circle management. Data-driven approaches haven been a promising way for such gear condition monitoring and fault diagnosis. In practical situation, gears generally have a variety of fault types, some of which exhibit continuous severities of fault. Vibration data collected oftentimes are limited to reflect all possible fault types. Therefore, there is practical need to utilize the data with a few discrete fault severities in training and then infer fault severities for the general scenario. To achieve this, we develop a fuzzy neural network (FNN) model to classify the continuous severities of gear faults based on the experimental measurement. Principal component analysis (PCA) is integrated with the FNN model to capture the main features of the time-series vibration signals with dimensional reduction for the sake of computational efficiency. Systematic case studies are carried out to validate the effectiveness of proposed methodology.

Original languageEnglish
Title of host publication2020 International Symposium on Flexible Automation, ISFA 2020
PublisherAmerican Society of Mechanical Engineers(ASME)
ISBN (Electronic)9780791883617
DOIs
Publication statusPublished - 2020
Externally publishedYes
Event2020 International Symposium on Flexible Automation, ISFA 2020 - Virtual, Online
Duration: 8 Jul 20209 Jul 2020

Publication series

Name2020 International Symposium on Flexible Automation, ISFA 2020

Conference

Conference2020 International Symposium on Flexible Automation, ISFA 2020
CityVirtual, Online
Period8/07/209/07/20

ASJC Scopus subject areas

  • Artificial Intelligence
  • Control and Systems Engineering

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