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 paper 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 based transfer learning method is formulated and named as probabilistic transfer factor analysis. 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 preservedata properties. The learned features by probabilistic transfer factor analysis 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). The effectiveness of the probabilistic transfer factor analysis method is demonstrated in the experimental tests for a gearbox diagnosis cross various operating conditions comparing with traditional feature extraction and transfer learning techniques.