Apparatus and method for detecting anomaly in plant pipe using multiple meta-learning

An apparatus for detecting an anomaly in a plant pipe using multiple meta-learning, the apparatus comprising:

• a plurality of meta-learning modules configured to separately receive multi-sensor data streams from a plurality of sensors for processing sensing of a plant pipe and to process meta-learning with sensor data of packet section ranges set according to trend from an arbitrary reception time point;
• and
• a multi-trend meta-evaluator configured to detect an anomaly in the plant pipe by aggregating processing results of the meta-learning modules and to output a detection result,
• wherein the plurality of meta-learning modules process multi-sensor data of packet sections having different lengths from the arbitrary reception time point,
• each of the meta-learning modules generates two-dimensional (2D) image features by extracting one or more preset types of features from multi-sensor data collected from a set packet section range, generates three-dimensional (3D) volume features by accumulating the 2D image features in a depth direction according to the plurality of sensors, and learns the 3D volume features in parallel through learning modules separately corresponding to the sensors, and
• the multi-trend meta-evaluator determines whether there is an anomaly in the plant pipe by aggregating results learned based on optimal combinations of multiple features, multiple sensors, and multiple packet sections according to the plurality of meta-learning modules.