Diagnosability is an important system property that determines at design stage how accurate any diagnostic reasoning can be on a partially observed system. A fault in a discrete-event system is diagnosable iff its occurrence can always be deduced from enough observations. It is well known that centralized diagnosability approaches lead to combinatorial explosion of the search space since they assume the existence of a monolithicmodel of the system. This is why very recently the distributed approaches for diagnosability began to be investigated, relying on local objects. On the other hand, diagnosis objectives are generalized from fault event to fault pattern that can represent multiple faults, repeating fault, sequences of significant events, repair of faults, etc. For pattern case, most existing approaches are centralized. In this paper, we propose a new distributed framework for pattern diagnosability. We first show how to recognize patterns by incrementally constructing local pattern recognizers through extended subsystems. Then we propose a structure called regional pattern verifier that is constructed from the subsystem where the pattern is completely recognized before showing how to abstract just the necessary and sufficient diagnosability information to further save the search space. Then the global consistency checking is based on another local structure called abstracted local twin checker to analyze pattern diagnosability. In this way, we avoid constructing global objects both for pattern recognition and for pattern diagnosability. The correctness of our distributed algorithm is theoretically proved and its efficiency experimentally demonstrated by the results of the implementation.