Probabilistic Latent Semantic Analysis-Based Gear Fault Diagnosis Under Variable Working Conditions

TLDR: Experimental results prove that the proposed latent feature-based transfer learning (TL) strategy has a significant advantage over gear fault diagnosis, especially under varying working conditions.

Abstract: Gears are often operated under various working conditions, which may cause the training and testing data have different but related distributions when conducting gear fault diagnosis. To address this issue, a latent feature-based transfer learning (TL) strategy is proposed in this paper. First, the bag-of-fault-words (BOFW) model combined with the continuous wavelet transform (CWT) method is developed to extract and represent every fault feature parameter as a histogram. Before identifying the gear fault, the latent feature-based TL strategy is carried out, which adopts the joint dual-probabilistic latent semantic analysis (JD-PLSA) to model the shared and domain-specific latent features. After that, a mapping matrix between two domains can be constructed by using Pearson’s correlation coefficients (PCCs) to effectively transfer shared and mapped domain specific latent knowledge and to reduce the gap between two domains. Then, a Fisher kernel-based support vector machine (FSVM) is used to identify the gear fault types. To verify the effectiveness of the proposed approach, gear data sets gathered from Spectra Quest’s drivetrain dynamics simulator (DDS) are analyzed. Experimental results prove that the proposed approach has a significant advantage over gear fault diagnosis, especially under varying working conditions.