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首页> 外文会议>International Conference on Reliability and Safety Engineering;National Conference on Reliability and Safety Engineering >A Review of Fault Detection, Diagnosis, and Prognosis of Rolling Element Bearing Using Advanced Approaches and Vibration Signature Analysis
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A Review of Fault Detection, Diagnosis, and Prognosis of Rolling Element Bearing Using Advanced Approaches and Vibration Signature Analysis

机译：采用先进方法及振动特征分析综述滚动元件轴承的故障检测，诊断和预后

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摘要

Rolling element bearing is the most significant component of the rotating machines. In most of the cases, machines failed due to the failure of rolling element bearing. The aim of the present paper is to provide a brief review of the advanced approaches like condition-based monitoring, wavelet transform, energy entropy ratio, artificial neural network, support vector machine, fuzzy logic, and vibration signature analysis techniques used to find the fault detection and diagnosis of rolling element bearing. The several techniques of vibration signature analysis have been given by several researchers. It is the most reliable non-destructive health monitoring technique used to detect the location and severity of the faults. Bearing faults and its classifications have already been reviewed. Time, frequency, time-frequency domain, and wavelet transform types signal processing methods have also been developed for fault detection. Term prognosis is used to estimate the remaining useful life of the faulty bearing and has been reviewed by various investigators. Advance approaches like energy, entropy, and energy entropy ratio criterion have been used for the classification of different types of signal waves. Higher energy and lower entropy criteria are favorable for the selection of mother wavelets in signal processing. Soft computing techniques like ANN, SVM, fuzzy logic, and ANFIS are used as decision-making tools and classifiers.
机译：滚动元件轴承是旋转机器最重要的部件。在大多数情况下，由于滚动元件轴承的故障，机器失效。本文的目的是简要介绍一种基于条件的监测，小波变换，能源熵比，人工神经网络，支持向量机，模糊逻辑和振动特征分析技术的先进方法，用于找到故障滚动元件轴承的检测与诊断。几个研究人员给出了几种振动特征分析技术。它是最可靠的无损健康监测技术，用于检测故障的位置和严重程度。轴承故障及其分类已经过审查。还开发了时间，频率，时频域和小波变换类型信号处理方法用于故障检测。术语预后用于估计有缺陷轴承的剩余使用寿命，并已被各种调查人员审查。能量，熵和能量熵和能量熵率标准的前进方法已被用于不同类型的信号波的分类。更高的能量和更低的熵标准是有利于在信号处理中选择母小波的。像Ann，SVM，模糊逻辑和ANFIS等软计算技术用作决策工具和分类器。

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