A method for detecting error data of a machine based on a machine-learning technique, by a server, comprising the steps: a) collecting time-sequential operation data of at least one machine; b) dividing the operation data by a predetermined time interval and mapping the divided operation data to be overlapped on a time domain which have a length corresponding the predetermined time interval; c) generating time-sequential threshold data (422, 423) having different values over time corresponding to a pattern of the collected operation data by deriving time-sequential standard data for a set of the operation data based on a machine-learning technique; and d) if operation data collected in real time deviate from the threshold data, determining the operation data collected in real time as an error data and supplying information about the error data to an operator device. characterised by the steps, further comprising: after the step a) and before the step b) detecting event data having a pattern deviating from the pattern of the collected operation data; and matching the event data with information indicative of whether the machine has encountered an error or an object manufactured by the machine has a defect, and storing the matched event data and information, wherein the step b) includes: dividing the collected operation data by designating a single cycle of the operation data as the predetermined time interval, and mapping the divided operation data on the time domain having a length corresponding to the single cycle, wherein the step d) includes: if error data which are detected at a certain time point from the operation data collected in real time are deviating from the threshold data, comparing the pattern of the error data with the pattern of the event data, determining whether the machine has encountered an error or the object has a defect according to the event data having the pattern corresponding to the error data, wherein the error data refers to data about at least one of errors of a machine and defects of an object manufactured by the machine. a) collecting time-sequential operation data of at least one machine; b) dividing the operation data by a predetermined time interval and mapping the divided operation data to be overlapped on a time domain which have a length corresponding the predetermined time interval; c) generating time-sequential threshold data (422, 423) having different values over time corresponding to a pattern of the collected operation data by deriving time-sequential standard data for a set of the operation data based on a machine-learning technique; and d) if operation data collected in real time deviate from the threshold data, determining the operation data collected in real time as an error data and supplying information about the error data to an operator device. after the step a) and before the step b) detecting event data having a pattern deviating from the pattern of the collected operation data; and matching the event data with information indicative of whether the machine has encountered an error or an object manufactured by the machine has a defect, and storing the matched event data and information, dividing the collected operation data by designating a single cycle of the operation data as the predetermined time interval, and mapping the divided operation data on the time domain having a length corresponding to the single cycle, wherein the step d) includes: if error data which are detected at a certain time point from the operation data collected in real time are deviating from the threshold data, comparing the pattern of the error data with the pattern of the event data, determining whether the machine has encountered an error or the object has a defect according to the event data having the pattern corresponding to the error data, wherein the error data refers to data about at least one of errors of a machine and defects of an object manufactured by the machine. if error data which are detected at a certain time point from the operation data collected in real time are deviating from the threshold data, comparing the pattern of the error data with the pattern of the event data, determining whether the machine has encountered an error or the object has a defect according to the event data having the pattern corresponding to the error data, wherein the error data refers to data about at least one of errors of a machine and defects of an object manufactured by the machine.