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  • 标题:Sensor Fault Detection for Salient PMSM based on Parity-Space Residual Generation and Robust Exact Differentiation
  • 本地全文:下载
  • 作者:Benjamin Jahn ; Michael Brückner ; Stanislav Gerber
  • 期刊名称:IFAC PapersOnLine
  • 印刷版ISSN:2405-8963
  • 出版年度:2020
  • 卷号:53
  • 期号:2
  • 页码:86-91
  • DOI:10.1016/j.ifacol.2020.12.099
  • 语种:English
  • 出版社:Elsevier
  • 摘要:AbstractAn online model-based fault detection and isolation method for salient permanent magnet synchronous motors is proposed using the parity-space approach. Given the polynomial model equations, Buchberger’s algorithm is used to eliminate the unknown variables (e.g. states, unmeasured inputs) resulting in analytic redundancy relations for residual generation. Furthermore, in order to obtain the derivatives of measured signals needed by such a residual generator, robust exact differentiators are used. The fault detection and isolation method is demonstrated using simulation of various fault scenarios for a speed controlled salient motor showing the effectiveness of the presented approach.
  • 关键词:KeywordsPermanent Magnet Synchronous MotorSensor Fault DetectionAnalytic Redundancy RelationsRobust Exact Differentiators
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