针对滚动轴承故障振动信号的随机性和非平稳性,提出基于局部保形投影(LPP)特征提取和自适应Boosting算法的滚动轴承故障诊断方法.首先对信号构建原始样本数据集合,提取时域、频域及时频域的相关特征,将该特征作为LPP的输入样本,得到维数降低的新数据集合并能尽可能保持原始局部流形结构.将此降维特征向量作为Adaboost输入,建立故障模型,用以识别滚动轴承故障类型.分析滚动轴承正常状态、内圈故障、外圈故障及滚动体故障特性.通过对比试验表明,基于LPP与Adaboost诊断方法识别率较高,可准确有效地对滚动轴承状态和故障进行分类.%A novel method for roller bearing fault diagnosis was presented based on locality preserving projection (LPP) and adaptive boosting algorithm (Adaboost). The original dataset for vibration signals was constructed, including time domain parameters, frequency domain parameters, and time-frequency domain parameters. Successively, dimension reduced features from the original dataset were extracted by using LPP. And finally, the adaptive boosting algorithm was applied for training and classification. The situations of normal condition, inner race defect, outer race defect, and ball defect of roller bearings were analysed. To verify its advantages, some comparative trials and simulation results show its effectiveness and superiority.
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