Fault signal recovery system and method

A fault signal recovery method comprising:

• from receiving a signal set U comprising data from sensors in a plant for a plurality of tags, where U includes one or more tags that are not normal signals and include a fault signal that is caused by one or an actual fault, a sensor error, and a transmission error, and wherein the one or more tags are to be restored to normal data when the fault signal is caused by a sensor error or a transmission error,
• generating a signal subset U* by removing from the signal set U one or more tags that include a fault signal identified by an Input S comprising information on fault data in the one or more tags to be restored including information on the one or more tags that contain the fault data to be restored, and to generate a first learning signal subset X* by removing from a learning signal set X tags disposed at positions corresponding to the one or more tags and wherein the learning signal set X comprises learning data comprising a plurality of tags when there are all normal signals acquired from the sensors and used to construct a failure prediction model;
• generating feature information F extractable from the first learning signal subset X* and to generate recovery information P using X*, Xs, and F on a plurality of recovery models usable for restoring the fault signal;
• analyzing a feature of the learning signal set X based on the feature information F and selecting an optimum recovery model by matching the feature of the learning signal set X with the recovery models generated through the recovery information P;
• estimating and recovering a normal signal for the some tags by applying an algorithm matching the optimum recovery model and using the normal signal and the first learning signal subset X* as learning data, and
• generating a second learning signal subset Xs containing only signals for the some tags and the first learning signal subset X* containing only signals for the remaining tags other than the some tags by processing the learning signal set X;
• and
• thereby the system provides a fault recovery method with improved accuracy to recover the fault signal when the one or more tags that are not normal are sensor errors or transmission errors and wherein the learning signal set X can be updated with the signal set U and the recovered normal signals.

A method of using data containing one or more fault signals as learning data comprising:

• receiving a plurality data signals from a plurality of sensors in a plant once per a time period, wherein each data signal from each sensor is a tag;
• adding the plurality of data signals to a learning signal set X when each of the tags is a normal signal;
• receiving a signal set U from the sensors in the plant wherein U includes one or more tags that is not a normal signal and include a fault signal that is caused by one of an actual fault, a sensor error, and a transmission error, and wherein the one or more tags are to be restored to normal data when the fault signal is caused by a sensor error or a transmission error;
• generating a signal set U* by removing from the signal set U the one or more tags that contain the fault data;
• generating a first learning signal subset X* by removing from the learning signal set X tags of the normal signal tags corresponding to the one or more tags that are not normal in signal set U;
• generating a second learning subset Xs containing only signals from the one or more signal tags that are not normal;
• using a modeling device to receive X* and Xs to generate recovery information for restoring each fault signal of the one or more tags that are not normal;
• thereby the method recovers normal data for the one or more tags that are not normal and updating the learning signal set X with the signal set U and the recovered normal signals.