Abstract: An adaptive fault identification system identifies repeat faults by capturing data representative of a fault. The captured data is subjected to a search tool and compared to data that is known to represent faults, and if possible, the unknown fault is identified. The search tool does not need to exactly match the data representing the unknown fault to the stored data. Instead, if the recall data pattern is similar enough to some of the stored data, a list of labels is returned where each label points to a recommended corrective action. By employing a learning algorithm, the similarity comparisons can be adapted to reflect greater knowledge of faults so that more accurate similarity comparisons can be made.

Abstract: A parallel associative memory provides a way of recognizing or identifying observed data patterns. Each of a plurality of memories stores a plurality of recognition patterns. In response to receipt of a recall pattern to be identified, the recall pattern is contemporaneously compared to the recognition patterns stored in the memories and an exact or best match recognition pattern is selected. In a preferred embodiment, the memories may store multiple data bases each of which includes patterns having different lengths and different radii of attraction. The comparison process is controlled by masks which specify respective portions of the patterns which may include the radii of attraction, bits which must identically match, bits which are ignored, bits which are compared in a bit-wise fashion, and bytes which are compared by multiplication. A correlation is computed and selectively adjusted by the respective radii of attraction.