基于贝叶斯函数数据深度的声学信号换向器电机故障识别

2022 26th International Conference on Methods and Models in Automation and Robotics (MMAR) Pub Date : 2022-08-22 DOI:10.1109/MMAR55195.2022.9874262

W. Bauer, A. Dudek, J. Baranowski

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引用次数: 0

摘要

换向电机的监测与诊断是保证换向电机可靠性的重要手段。它们通常用于家用和工业设备。故障检测和隔离算法可以延长系统寿命，减少系统中断，并可以节省大量费用。本文讨论了如何利用贝叶斯函数数据深度来检测故障。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Recognizing Commutator Motors Fault from Acoustics Signals Using Bayesian Functional Data Depth

Monitoring and diagnostics of commutator motors are very important in order to ensure their reliability. They are often used in domestic and industrial devices. Algorithms for fault detection and isolation allow for the extension of system life, the reduction of system interruption, and can lead to significant savings. In this paper we discuss how Bayesian functional data depth can be used to detect faults.

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来源期刊

2022 26th International Conference on Methods and Models in Automation and Robotics (MMAR)

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