3. An apparatus abnormality monitoring method in which, with using information processing of a computer and taking a plurality of (K+1) similar apparatuses as process targets, based on a plurality of data items of each of the apparatuses obtained by measuring states of the apparatuses with sensors, processes for diagnosing a cause of an abnormality of at least one of the plurality of apparatuses are performed, among the plurality of (K+1) apparatuses, one first apparatus being taken as a target for cause diagnosis, and a plurality of (K) other second apparatuses being taken as targets for obtaining data for producing a second model for the cause diagnosis of the first apparatus, the method comprising: a first step of creating and accumulating individual diagnostic models of the plurality of (K) apparatuses based on an abnormality cause diagnosis of the plurality of (K) apparatuses, and producing the second model for the cause diagnosis of the first apparatus based on the plurality of (K) individual diagnostic models; and a second step of inputting a plurality of data items of the first apparatus per predetermined time unit, monitoring and judging an abnormality of the state of the first apparatus, and performing a cause diagnosing process by using the second model when an abnormality is detected, wherein the first step includes: a step of creating individual diagnostic models of the plurality of (K) second apparatuses and accumulating the diagnostic models together with a feature item value or an installation environment measurement value of each of the second apparatuses; a step of creating a similar-apparatus common meta diagnostic model obtained by classifying the plurality of (K) accumulated individual diagnostic models based on the feature item value or the installation environment measurement value of each of the second apparatuses; and a step of inputting a feature item value or an installation environment measurement value of the first apparatus to the meta diagnostic model and producing the second model dedicated to the first apparatus, and the second step includes: a step of, when the abnormality is detected, inputting data items from the first apparatus to the second model and performing matching with each of the models constituting the second model; and a step of outputting information about a cause based on the matched models.
3. An apparatus abnormality monitoring method in which, with using information processing of a computer and taking a plurality of (K+1) similar apparatuses as process targets, based on a plurality of data items of each of the apparatuses obtained by measuring states of the apparatuses with sensors, processes for diagnosing a cause of an abnormality of at least one of the plurality of apparatuses are performed, among the plurality of (K+1) apparatuses, one first apparatus being taken as a target for cause diagnosis, and a plurality of (K) other second apparatuses being taken as targets for obtaining data for producing a second model for the cause diagnosis of the first apparatus, the method comprising: a first step of creating and accumulating individual diagnostic models of the plurality of (K) apparatuses based on an abnormality cause diagnosis of the plurality of (K) apparatuses, and producing the second model for the cause diagnosis of the first apparatus based on the plurality of (K) individual diagnostic models; and a second step of inputting a plurality of data items of the first apparatus per predetermined time unit, monitoring and judging an abnormality of the state of the first apparatus, and performing a cause diagnosing process by using the second model when an abnormality is detected, wherein the first step includes: a step of creating individual diagnostic models of the plurality of (K) second apparatuses and accumulating the diagnostic models together with a feature item value or an installation environment measurement value of each of the second apparatuses; a step of creating a similar-apparatus common meta diagnostic model obtained by classifying the plurality of (K) accumulated individual diagnostic models based on the feature item value or the installation environment measurement value of each of the second apparatuses; and a step of inputting a feature item value or an installation environment measurement value of the first apparatus to the meta diagnostic model and producing the second model dedicated to the first apparatus, and the second step includes: a step of, when the abnormality is detected, inputting data items from the first apparatus to the second model and performing matching with each of the models constituting the second model; and a step of outputting information about a cause based on the matched models. 4. The apparatus abnormality monitoring method according to claim 3 , wherein the second model is taken as a graphical model in which a relation among the plurality of data items is represented in a directed graph, and in the first step, a process of determining presence or absence or a strength of a link of the graphical model by a correlation strength, a partial correlation strength, or a product of the partial correlation strength and a signal strength and a process of determining a direction of the link from time precedence information among the data items are performed.