1. 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 monitoring and judging an abnormality of a state of at least one of the plurality of (K+1) apparatuses are performed, among the plurality of (K+1) apparatuses, one first apparatus being taken as a target for monitoring and judgment, and a plurality of (K) other second apparatuses being taken as targets for obtaining data for producing a first model for monitoring and judging the first apparatus, the method comprising: a first step of performing a process of producing the first model dedicated to the first apparatus based on a plurality of (K) prediction models dedicated to the individual second apparatuses for monitoring and judgment, the prediction models being created based on a plurality of data items at a time of normal condition in each of the plurality of (K) second apparatuses; and a second step of performing a monitoring execution process of inputting a plurality of data items from the first apparatus per predetermined unit time, monitoring and judging an abnormality of the state of the first apparatus by using the first model, and outputting detection information when an abnormality is detected, wherein the first step includes: a step of classifying the plurality of data items of each of the plurality of (K) second apparatuses into an objective variable and other explanatory variables in regression analysis; a step of creating a plurality of (K) regression models as individual prediction models of the plurality of (K) second apparatuses; a step of creating a similar-apparatus common meta prediction model which predicts a coefficient and an intercept of each of the plurality of (K) regression models from a feature item value or an 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 prediction model and producing a coefficient and an intercept of a regression model as the first model, thereby producing the first model, and the second step includes: a step of inputting an explanatory variable in the plurality of data items of the first apparatus to the first model and calculating a predicted value of an objective variable of the first apparatus; a step of calculating a deviance between a measurement value of the objective variable of the first apparatus and the predicted value of the objective variable of the first apparatus; and a step of detecting an abnormality of the first apparatus by comparing the deviance and a threshold.