1. A diagnosis method for diagnosing abnormal machine operation modes being performed by a machine, comprising the steps of: (a) detecting a plurality of normal data sets that each correspond to a different one of a corresponding plurality of different machine operation modes performed by the machine, with each of the plurality of normal data sets including values of n parameters that each vary in accordance with a different preliminary normal state operation performed relative to a corresponding different one of the different machine operation modes, with the detecting being performed by n individual detectors that individually detects a corresponding one of the values of the n parameters; (b) creating self-organizing maps serving as separation models corresponding to each one of the plurality of different machine operation modes using the plurality of normal data sets representing the normal state of the machine detected in said step (a); (c) creating a multiplicity of abnormal data sets for each one of the plurality of normal data sets, the multiplicity of abnormal data sets representing virtual abnormal states in which each one of the plurality of different normal state operation modes of the object has abnormality created by modifying the values of the n parameters of each of the plurality of normal data sets detected in said step (a) with a number of different deviation vectors each of which has n deviation components; (d) calculating an abnormal operation mode proportion vector for each of the deviation vectors, which abnormal operation mode proportion vector includes a plurality of proportions representing each one of the plurality of different normal state operation modes to all of the plurality of different normal state operation modes after selecting a self-organizing map which has the highest similarity degree to each of the abnormal data sets created in said step (c) the self-organizing maps created in said step (b); (e) providing the deviation vectors of said step (c) and the abnormal operation mode proportion vectors for each of the deviation vectors calculated in said step (d) into a memory associated with the machine in order to determine signs of abnormalities with the machine while performing actual operations in accordance with actual machine operation modes; and (f) diagnosing the object based at least in part on a correlation between the deviation vectors and the abnormal operation mode proportion vectors obtained from the memory after the machine is performing actual operations in accordance with the actual machine operation modes.