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Reliability Analysis and Assessment for Matrix Torque Component Machine Based on Markov Process

  • Xi'an Jiaotong University
  • Hebei University of Science and Technology

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper, the reliability of high torque density matrix-torque-component-machine (MTCM) is quantitatively evaluated using the Markov model. Based on the structural of MTCM, its reliability diagram exhibits parallel branch characteristics. Furthermore, an MTCM fault simulation model is established to analyze the electromagnetic performance and under faults, and the operating state is judged based on the given standards. Building upon this, a Markov state transition chain is established to reflect the evolution of the system operating state. Subsequently, the time-dependent reliability curve and the mean time to failure (MTTF) of the MTCM are derived. Comparative analysis with a conventional permanent magnet synchronous machine (PMSM) reveals that the MTCM offers significantly higher reliability, thereby highlighting its advantages in applications requiring enhanced fault resilience and operational continuity.

Original languageEnglish
Title of host publicationICEMS 2025 - 28th International Conference on Electrical Machines and Systems
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1279-1284
Number of pages6
ISBN (Electronic)9788986510232
DOIs
StatePublished - 2025
Event28th International Conference on Electrical Machines and Systems, ICEMS 2025 - Busan, Korea, Republic of
Duration: 16 Nov 202519 Nov 2025

Publication series

NameICEMS 2025 - 28th International Conference on Electrical Machines and Systems

Conference

Conference28th International Conference on Electrical Machines and Systems, ICEMS 2025
Country/TerritoryKorea, Republic of
CityBusan
Period16/11/2519/11/25

Keywords

  • Markov model component
  • matrix-torque-component-machine
  • reliability analysis
  • time-dependent reliability curve formatting

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