Patent Document ID: 9465387
Application ID: 14982138
Patent Flag: 1

Claim One:
1. An anomaly diagnosis system diagnosing a state of a machine facility, comprising: a time series data receiver acquiring sensor data as time series data from a plurality of sensors installed in the machine facility; a state measure calculator calculating an anomaly measure or a performance measure as a state measure being an index indicating a state of the machine facility, by a statistical method in which the time series data is used as learned data, the anomaly measure being an index indicating a magnitude of deviation from a normal state of the machine facility, and the performance measure being an index indicating a performance of the machine facility; an approximation formula calculator calculating an approximation formula approximating, with a polynomial expression, variation in the state measure calculated based on the time series data acquired from a past to the present, the approximation formula indicating future approximation of variation of the state measure; a state measure estimating unit estimating the state measure until a predetermined future time using the approximation formula; a reference period setting unit setting a reference period being a period for which the time series data corresponding to the state measure is acquired, the approximation formula calculator referring the reference period to calculate the approximation formula; and an output which outputs a diagnose result of the state of the machine facility using the estimated state measure, wherein the reference period setting unit sets, as the reference period, a first period including time when latest time series data is acquired or a second period shorter than the first period and including the time when the latest time series data is acquired, and wherein the approximation formula calculator calculates the approximation formula using the state measure regarding the time series data acquired for the reference period set by the reference period setting unit.