Diagnosis aims to explain the abnormal behavior of a system based on the symptoms observed. In a discrete-event system (DES), the symptom is a temporal sequence of observations. At the occurrence of each observation, the diagnosis engine generates a set of candidates, a candidate being a set of faults: such a process requires costly model-based reasoning. This is why a variety of knowledge compilation techniques have been proposed; the most notable of them relies on a diagnoser and requires both the diagnosability of the DES and the generation of the whole system space. To avoid both diagnosability and total knowledge compilation, while preserving efficiency, a diagnosis technique is proposed, which is inspired by the two operational modes of the human mind. If the symptom of the DES is part of the knowledge or experience of the diagnosis engine, then Engine 1 allows for efficient diagnosis. If, instead, the symptom is unknown, then Engine 2 comes into play, which is far less efficient than Engine 1. Still, the experience acquired by Engine 2 is then integrated into the temporal dictionary of the DES, which allows for diagnosis in linear time. This way, if the same problem arises anew, then it will be solved by Engine 1 efficiently. The temporal dictionary can also be extended by specialized knowledge coming from scenarios, which are behavioral patterns of the DES that need to be diagnosed quickly. As such, the temporal dictionary is open and relies on dual knowledge compilation.