Research on a small sample feature transfer method for fault diagnosis of reciprocating compressors

The assumption of a data-driven fault diagnosis model is that both the training data and the test data come from the same probability distribution. In the diagnosis of reciprocating compressors, problems such as sensor failures and changes in installation locations often occur, making it difficult to obtain effective, stable and sufficient data. The sample data is not sufficient to meet the requirements of the fault diagnosis model. We propose a fault diagnosis method for reciprocating compressors based on feature transfer at small sample sizes. First, the sample data is projected to the Regenerated Kernel Hilbert Space (RKHS) using Transfer Component Analysis (TCA). Second, the minimization of distributional differences across domains is measured by the maximum mean difference (MMD). Then, combined with the adaptive feature extraction capability of Deep Belief Network (DBN), the already labeled source domain and unlabeled target domain data are aggregated during the training phase to diagnose faults. This approach uses a novel optimized learning approach, free energy in persistent contrastive divergence, in DBN learning and training. It solves the problem of DBN classification ability degradation in longterm training. Finally, a low-power single-acting reciprocating compressor experimental platform is built to carry out fault diagnosis experiments. The research results show that better diagnostic accuracy can be obtained by reducing the distribution difference of source and target domain data in projected high-dimensional RKHS through the common transfer components learned by TCA. Compared with traditional small-sample machine learning algorithms and DBN models, this approach has strong generalization ability, and the accuracy rate is as high as 92%. This study proves the superiority of both transfer learning (TL) and deep learning fault diagnosis models in solving the problem of less fault data and provides a theoretical reference for accurate fault diagnosis of complex dynamic equipment in the case of small samples.