Under small distribution discrepancies between source and target domains (TDs), the existing domain adaptation-based fault diagnosis methods can achieve encouraging results. However, in practical industrial applications, various machines and working conditions change unpredictably, and significant interdomain distribution discrepancies occur, seriously affecting the performance of domain adaptation-based methods. To tackle this problem, most of the existing domain adaptation scenarios for fault diagnosis of different machines only focus on single-level domain adaptation, which cannot comprehensively consider discrepancy variations from the TD. In this article, a multilevel domain alignment adaptation-based transfer fault diagnosis method for different machines was proposed, where the source domain (SD) and TD originate from different machinery with significant distribution discrepancies. In the proposed method, intraclass spatial reconstitution is explored to excavate the potential category information hidden in the TD for improving the performance and generalization of domain adaptation. Moreover, a multilevel domain information alignment strategy is employed to address the shortcomings caused by single-level domain alignment that does not consider the influences of multilevel distribution discrepancy fluctuation. Two well-known bearing datasets and a real industrial application dataset are used to verify the proposed method. The results show that the proposed method can achieve better performance than the SOTA methods.