The faulted sections are evaluated by calculating the measuring information resulted from the faults in various positions by the previous fault simulative calculation, introducing the resulting fault simulative measuring information into a self-organizing neural network having output elements of which number being more than that of input elements to permit the self-organizing neural network to learn the classification of the simulative measuring information, preparing an evaluation rule representing the correspondent relation between the output from the classification of the stimulative measuring information and the faulted position, thereafter introducing actual measured information into the self-organizing neural network to permit the self-organizing neural network to classify the introduced actual measured information, and evaluating the faulted section from an output classified by the self-organized neural network on the basis of the evaluation rule.
A telecommunications fault location and diagnostic system employs a remote test unit (RTU) to collect system parameter data. The RTU is operatively coupled to a trained neural network, which receives the system parameter data from the RTU. The neural network is trained using pre-screened historical fault data, which is stored in a database. Once trained, the neural network classifies the RTU data into one of a predetermined number of fault probabilities.
