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首页> 外文学位 >Fault detection, diagnosis and prognosis of rolling element bearings: Frequency domain methods and hidden Markov modeling.
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Fault detection, diagnosis and prognosis of rolling element bearings: Frequency domain methods and hidden Markov modeling.

机译：滚动轴承的故障检测，诊断和预测：频域方法和隐马尔可夫建模。

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摘要

In this thesis, two frequency domain algorithms are developed for estimating the running speed (revolutions per second of the inner race) and the bearing defect frequencies of rolling element ball bearings from the Fast Fourier Transform (FFT) of the vibration data.; Further, a new hidden Markov modeling (HMM) based fault detection and diagnosis scheme is also developed. Feature matrices extracted from amplitude demodulated vibration signals from both normal and faulty bearings were used to train HMMs to represent various bearing conditions. Features were based on the reflection coefficients of the polynomial transfer function of an auto-regressive (AR) model of the vibration signals. Fault(s) were detected by comparing the probabilities of the feature matrices extracted from the vibration signals given the HMM trained for the normal case with a pre-set threshold. An existing fault was diagnosed through selecting the HMM with the highest probability. The scheme was also adapted to diagnose multiple bearing faults. In this adapted scheme, features were based on the selected node energies of a wavelet packet decomposition of the vibration signal. For each fault, a different set of nodes was chosen. The node set for a fault consisted of the nodes whose energies are affected by the presence of that fault and minimally affected by the presence of other faults. Both schemes were tested with experimental data collected from an accelerometer measuring the vibration from the drive end ball bearing of an induction motor (Reliance Electric 2HP IQPreAlert) driven mechanical system and have proven to be very accurate.; Finally, a new HMM based bearing prognosis scheme is also presented. In this scheme, the magnitude spectra of vibration signals were divided into several equally spaced bands and the mean energies of these bands were used as features. Based on the features extracted from normal bearing vibration signals, an HMM was trained to model the normal condition. The probabilities of this HMM were then used to detect any defects and assess their severity. Experimental data collected from a bearing accelerated life test was used to verify the efficacy of the new scheme.
机译：本文提出了两种频域算法，分别根据振动数据的快速傅立叶变换（FFT）估算滚动轴承的运行速度（内圈每秒转数）和轴承缺陷频率。此外，还开发了一种新的基于隐马尔可夫建模（HMM）的故障检测和诊断方案。从正常轴承和故障轴承的振幅解调振动信号中提取的特征矩阵用于训练HMM，以表示各种轴承状况。特征基于振动信号的自回归（AR）模型的多项式传递函数的反射系数。通过比较从给定针对正常情况训练的HMM的振动信号中提取的特征矩阵的概率与预设阈值，可以检测出故障。通过选择可能性最高的HMM诊断现有故障。该方案还适用于诊断多个轴承故障。在这种改编的方案中，特征基于振动信号的小波包分解的选定节点能量。对于每个故障，选择了不同的节点集。故障的节点集由其能量受该故障的存在影响并且受其他故障的影响最小的节点组成。两种方案均使用从加速度计收集的实验数据进行测试，该加速度计测量感应电机（Reliance Electric 2HP IQPreAlert）驱动的机械系统的驱动端球轴承的振动，并且已被证明非常准确。最后，提出了一种新的基于HMM的轴承预后方案。在该方案中，将振动信号的幅度谱分为几个等距的频带，并将这些频带的平均能量用作特征。根据从正常轴承振动信号中提取的特征，对HMM进行了训练，以对正常状况进行建模。然后将此HMM的概率用于检测任何缺陷并评估其严重性。从轴承加速寿命测试中收集的实验数据用于验证新方案的有效性。

著录项

  • 作者

    Ocak, Hasan.;

  • 作者单位

    Case Western Reserve University.;

  • 授予单位 Case Western Reserve University.;
  • 学科 Engineering Electronics and Electrical.; Engineering Mechanical.
  • 学位 Ph.D.
  • 年度 2004
  • 页码 98 p.
  • 总页数 98
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 无线电电子学、电信技术;机械、仪表工业;
  • 关键词

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