Probabilistic Transfer Factor Analysis for Machinery Autonomous Diagnosis Cross Various Operating Conditions
Jinjiang Wang, Rui Zhao, Robert X. Gao
Abstract
The variability of machinery fault signatures causes the data samples to follow different distributions under various operating conditions, which poses significant challenges on autonomous diagnosis based on machine learning techniques. This article presents a new transfer learning method for cross-domain feature learning by mitigating the domain difference caused by various operating conditions for machinery autonomous diagnosis. More specifically, a factor analysis (FA)-based transfer learning method is formulated and named as probabilistic transfer FA (PTFA). It seeks a new feature space across different domains corresponding to various operating conditions and then transfers the original features into a low-dimensional latent space via feature extraction to minimize domain difference and preserve data properties. The learned features by PTFA minimize the domain difference and are then used to construct the machinery autonomous diagnosis model based on machine learning techniques [e.g., support vector machine (SVM)]. The effectiveness of the PTFA method is demonstrated in the experimental tests for a gearbox diagnosis under various operating conditions comparing with traditional feature extraction and transfer learning techniques.