Diagnosability is an important property that determines at the design stage how accurate any diagnosis algorithm can be on a partially observable system and thus has significant economic impact on the improvement of performance and reliability of complex systems. Very recently distributed approaches for diagnosability began to be investigated since centralized approaches are not realistic for large systems due to the combinatorial explosion of the search space. In this paper, we propose a new optimized algorithm for pattern diagnosability analysis of distributed systems with modular structure, where we obtain the original pattern diagnosability information from the relative components before abstracting the sufficient and necessary information to be propagated to other connected components. Then, the diagnosability decision can be made after global consistency checking at the proper level of the subsystem involved. Our experimental results and complexity analysis illustrate the correctness and efficiency of our approach both in a practical and theoretical way. Finally, we distinguish our work by presenting related works before the conclusion.