Bearing defect diagnosis based on semi-supervised kernel Local Fisher Discriminant Analysis using pseudo labels

In bearings defect diagnosis applications, information fusion has been widely used to improve identification accuracy for different types of faults, which may lead to high-dimensionality and information redundancy of the data and thus degenerate the classification performance. Therefore, it is a major challenge for machinery fault diagnosis to extract optimal features from high-dimensional and redundant data for classification. In addition, in order to guarantee the performance of fault diagnosis, conventional supervised methods usually require a large amount of labeled data available for learning. However, it is extremely difficult, costly and time-consuming to collect faulty labeled samples with class information, especially for expensive and critical machines, which often results in only a few labeled data available with a large amount of unlabeled data redundant. In this paper, we propose a novel bearing defect diagnosis model based on semi-supervised kernel local Fisher Discriminant Analysis (SSKLFDA) using pseudo labels, which can effectively extract optimal features for classification and simultaneously utilize unlabeled data for regularizing the supervised dimensionality reduction. The proposed SSKLFDA first adopts Density Peak Clustering technique to generate pseudo cluster labels for the labeled and unlabeled data and then regularizes the between-class scatter and within-class scatter according to two corresponding regularization strategies associated with the generated pseudo cluster labels. This regularization can further improve the discriminant performance of the extracted features and also make it suitable for the cases with the multimodality and noises. In order to accommodate for non-linear feature extraction, the kernel version of the proposed method is also provided with the introduction of kernel trick. The experimental results under different feature dimensions, numbers of labeled data, and subsequent classifiers scenarios demonstrate that the proposed SSKLFDA based bearings fault diagnosis model achieves higher classification performance than other existing dimensionality reduction methods-based models.