While image and audio data often dominate fault diagnosis research, the exploration on fault diagnosis of tabular data remains of paramount significance. In the field of tabular fault diagnosis, prior endeavors primarily focused on traditional supervised learning methods, and the evaluation of cross-condition fault diagnosis tasks was insufficient. In this paper, we introduce a self-supervised learning method customized for cross-condition fault diagnosis in tabular data, which combines contrastive learning strategy and tabular masking modeling strategy with a Transformer-based autoencoder architecture. The results of diagnostic instance on the Case Western Reserve University datasets demonstrate that after proper fine-tuning, our method can generally outperform the diagnostic accuracy of the supervised learning baselines in the target tasks. Compared with the self-supervised learning baselines, the introduction of contrastive learning strategy and tabular masking modeling strategy increases the average diagnostic accuracy of the autoencoder by 0.74% and 3.35% respectively in the three target tasks. Furthermore, our comprehensive analysis and discussion on the fidelity and utility of the proposed method serve to demonstrate its rationality.