滚动轴承故障特征提取与分类识别技术研究.docx

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摘要：滚动轴承是机械设备中常用的关键零部件，其故障会导致设备的性能下降甚至故障停机。因此，滚动轴承故障的提取和分类识别技术对设备的正常运行和维护具有重要意义。本论文主要研究滚动轴承故障特征提取和分类识别技术，通过对滚动轴承故障的特征进行提取和分析，并构建适合的分类模型，实现对滚动轴承故障的准确识别。 本论文首先介绍滚动轴承的基本原理和常见的故障类型，然后分析滚动轴承故障的振动信号特征，包括时域特征、频域特征和时频域特征等。此外，还介绍了滚动轴承故障诊断的常用方法，如振动分析、波形分析和能量分析等。接下来，本论文提出一种基于机器学习的滚动轴承故障分类识别方法，首先使用特征提取算法对滚动轴承的振动信号进行特征提取，然后通过训练分类模型实现对滚动轴承故障的分类识别。最后，本论文通过实验验证了提出的滚动轴承故障分类识别方法的有效性和准确性，表明这种方法能够实现对滚动轴承故障的可靠识别，并可以为设备的运行和维护提供参考依据。 关键词：滚动轴承；故障特征提取；分类识别；机器学习
Abstract: Rolling bearings are commonly used key components in mechanical equipment, and their failures can cause performance degradation or even equipment breakdown. Therefore, the extraction and classification recognition technology of rolling bearing faults are of great significance for the normal operation and maintenance of the equipment. This paper mainly studies the fault feature extraction and classification recognition technology of rolling bearings, conducts feature extraction and analysis on the fault characteristics of rolling bearings, and constructs suitable classification models to achieve accurate recognition of rolling bearing faults. This paper first introduces the basic principles of rolling bearings and common fault types, then analyzes the vibration signal features of rolling bearing faults, including time domain features, frequency domain features, and time-frequency domain features, etc. In addition, common methods for rolling bearing fault diagnosis, such as vibration analysis, waveform analysis, and energy analysis, are also introduced. Next, this paper proposes a machine learning-based rolling bearing fault classification recognition method. First, a feature extraction algorithm is used to extract features from the vibration signals of rolling bearings, and then the classification model is trained to achieve classification recognition of rolling bearing faults. Finally, this paper verifies the effectiveness and accuracy of the proposed rolling bearing fault classification recognition method through experiments, indicating that this method can reliably identify rolling bearing faults and provide reference for equipment operation and maintenance. Keywords: Rolling bearing; Fault feature extraction; Classification recognition; Machine learning
1. 引言
滚动轴承广泛应用于机械设备中，其主要作用是传递轴的旋转力和承受轴向和径向负荷。然而，由于长期使用、装配不良或外界环境等原因，滚动轴承易受损或产生故障。滚动轴承的故障会导致设备的性能下降、振动增大甚至故障停机，给设备的正常运行和维护带来困难。因此，研究滚动轴承故障的提取和分类识别技术对设备的正常运行和维护具有重要意义。
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7. 实验验证
为了验证提出的滚动轴承故障分类识别方法的有效性和准确性，本实验选取了一种常见的滚动轴承故障类型，采集了滚动轴承的振动信号，并进行分类识别实验。
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8. 结论
本研究基于机器学习的滚动轴承故障分类识别方法，实现了滚动轴承故障的准确识别。通过对滚动轴承的振动信号进行特征提取和分析，并构建适合的分类模型，该方法能够可靠地识别滚动轴承的故障类型，为设备的运行和维护提供参考依据。实验结果表明，提出的方法具有较高的效果和准确率。未来的研究可以进一步优化特征提取算法和分类模型，提升滚动轴承故障的分类识别性能。
参考文献：
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期刊名称：机械工程学报
DOI：-eng-2021-0123