An on-line monitoring and diagnostic method of rolling element bearing with AI

Y. Shao, K. Nezu
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引用次数: 9

Abstract

A new concept of the degree of creditability of parameter value variations (DCPV factor) is proposed in this paper to solve problem that on-line monitoring and failure diagnosis of rolling element bearings are affected by monitoring parameter value variations caused by the intrusive vibration signals. Using the factor of the degree of creditability and the basic principle of expert systems, an on-line monitoring and diagnostic method of rolling element bearings with AI is developed. The technique enhances traditional vibration analysis and provides a means of automating the monitoring and diagnosis of a vibrating device.
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基于人工智能的滚动轴承在线监测与诊断方法
针对侵入式振动信号引起的监测参数值变化影响滚动轴承在线监测和故障诊断的问题,提出了参数值变化可信度因子(DCPV因子)的概念。利用专家系统的可信度因素和基本原理,提出了一种基于人工智能的滚动轴承在线监测诊断方法。该技术增强了传统的振动分析,为振动装置的自动化监测和诊断提供了一种手段。
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