基于迁移成分分析和词包模型的变工况轴承诊断方法

针对不同工况下的数据无法直接训练并用于检测的问题，提出一种基于迁移成分分析和词包模型的诊断算法，对于用作训练的有标签源域数据和用作检验诊断的无标签目标域数据。首先使用短时傅里叶变换将两者转换为频域数据，其次通过迁移成分分析将两者的频谱能量映射到同一分布以建立相应的词包模型作为数据的特征，最后在源域数据的词包模型上训练出合适的分类器从而进行诊断。在西门子SQI-MFS平台实验数据集、凯斯西储大学公开数据集及机械故障预防技术协会MFPT(machinery failure prevention technology)数据集下的实验结果表明该算法是有效的。

Bearing fault diagnosis in variable conditions based on transform component analysis and bag of words

To solve the problem that the data under different working conditions cannot be directly trained and used for detection, a diagnosis algorithm based on transform component analysis and bag of words was proposed. For the labeled data to be used for training (called source domain data) and unlabeled data for test diagnosis (called target domain data), firstly, the two types of data were converted into frequency domain data using short-time Fourier transform. Then, the spectrum energy of the two types of data was mapped to the same distribution through transfer component analysis in order to make corresponding bag of words as a feature of the data. Finally, a suitable classifier was trained on the bag of words of the source domain data and diagnosed the target domain data with that. The experimental results under the Siemens SQI-MFS platform experimental data set, Case Western Reserve University public data set and Mechanical Failure Prevention Technology Association MFPT(machinery failure prevention technology) data set show that the algorithm is valuable.