Abstract: A method and apparatus that detects a multiplicity of normal data sets, each of which includes values of n parameters, for each of the operation modes of an object having a plurality of operation modes. Self-organizing maps are provided for each operation mode using the normal data sets. Abnormal data sets representing virtual abnormal states are created by modifying the values of the n parameters of each of the multiple normal data sets so that as many abnormal data sets as the number of deviation vectors are created for each of the multiple normal data sets. Abnormal operation mode proportion vectors are then created by selecting a self-organizing map from the above noted self-organizing maps which has the highest similarity degree to each of the abnormal data sets.

Abstract: There are provided an apparatus and a method for compressing data, an apparatus and a method for analyzing data and a data management system, which are capable of compressing huge data and accurately reproducing the characteristics of the original data from the compressed data. The data compressing apparatus includes detection means for detecting a multiplicity of data sets, each including n parameter values that vary according to an operation of an object, where n is a natural number; and data compressing means for compressing the data sets by inputting the data sets into an n-dimensional space, arranging neurons smaller in number than the data sets in the n-dimensional space, carrying out unsupervised learning for a neural network on the neurons, and converting the data sets into a neuron model parameter characterizing a neuron model obtained by the unsupervised learning.

Abstract: An information processor, a state judging unit, a diagnostic unit, an information processing method, a state judging method and a diagnosing method aim at precisely recognizing each operation carried out by an object that functions in a number of operation modes. For this purpose, a number of combinations of n parameter values, concerning each of the operation modes, that vary with operation are detected by sensors (1a-1d) during the operation of the object, and a Self-Organizing Map creating means (2) creates Self-Organizing Maps, which corresponds one to each of the operation modes and which serve as individual separation models, regarding detection data based on the detected combination of parameter values as learning data.