Detection of Bearing Failure in Rotating Machine Using Adaptive Neuro-Fuzzy Inference System

S. Wadhwani, A. Wadhwani, S.P. Gupta, V. Kumar
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引用次数: 7

Abstract

This paper proposes a novel approach for bearing health evaluation using Lempel-Ziv complexity and time domain statistical parameters in conjunction with ANFIS. Compared to conventional techniques the presented approach works well for a non linear physical system and is thus suited for condition monitoring of machine system under varying operating and loading conditions. The performance of this technique is investigated through experimental study of realistic vibration signals. The results demonstrate that complexity analysis and time domain parameters in conjunction with ANFIS provide an effective measure forebearing health evaluation.
基于自适应神经模糊推理系统的旋转机械轴承故障检测
本文提出了一种将Lempel-Ziv复杂度和时域统计参数结合ANFIS进行轴承健康评估的新方法。与传统方法相比,该方法对非线性物理系统的监测效果较好,因此适用于不同运行和负载条件下的机械系统状态监测。通过对实际振动信号的实验研究,对该技术的性能进行了研究。结果表明,复杂性分析和时域参数结合ANFIS为轴承健康评价提供了有效的手段。
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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