Learning device, anomaly indication detection device, anomaly indication detection system, learning method, and storage medium

An anomaly indication detector to perform anomaly indication detection using learned data, the anomaly indication detector comprising:

• a preprocessing circuitry to subtract, from a value at each of points in one cycle of detection target data, an average of values at corresponding points in the detection target data from one to N cycles ago in computing difference values with respect to the preceding N cycles, the one cycle corresponding to a specified time length, N being an integer greater than or equal to 2, the detection target data being data as an anomaly indication detection target;
• and
• a first waveform analysis circuitry to determine whether or not there is an anomaly indication through waveform similarity analysis using the difference values with respect to the preceding N cycles and the learned data, wherein
• the learned data includes normal waveforms and a normality determination threshold that have been generated through waveform similarity analysis using difference values with respect to preceding N cycles based on normal data that is data from a normal state, the normality determination threshold being used in determination of normality.

An anomaly indication device detector to perform anomaly indication detection using learned data, the anomaly indication detector comprising:

• a difference analysis circuitry to compute a first-order difference value between measured root-mean-square value data of at least one of voltage or current as a measurement target and determine whether or not there is an anomaly indication, using a first-order difference value computed and a threshold;
• and
• an anomaly indication waveform analysis circuitry to determine whether or not an anomaly indication is present, using measured instantaneous value data of the measurement target, anomaly indication waveforms, and an anomaly indication determination threshold when the difference analysis circuitry determines that an anomaly indication is present, wherein
• the threshold is the learned data computed using first-order difference values for normal measured root-mean-square value data of the measurement target, and
• the anomaly indication waveforms and the anomaly indication determination threshold are included in the learned data and have been generated through waveform similarity analysis using anomaly indication data of the measurement target, the anomaly indication data being measured instantaneous value data with anomaly indications.

An anomaly indication detection system comprising:

• a learner to generate learned data to be used for anomaly indication detection;
• and
• an anomaly indication detector to perform anomaly indication detection using the learned data, wherein
• the learner includes
• a preprocessing circuitry to subtract, from a value at each of points in one cycle of normal data, an average of values at corresponding points in the normal data from one to N cycles ago in computing difference values with respect to the preceding N cycles, the one cycle corresponding to a specified time length, N being an integer greater than or equal to 2, the normal data being data from a normal state and
• a first waveform analysis circuitry to generate, through waveform similarity analysis using the difference values with respect to the preceding N cycles, normal waveforms and a normality determination threshold to be used in determining whether or not there is normality as the learned data, and
• the anomaly indication detector includes
• a preprocessing circuitry to subtract, from a value at each of points in one cycle of detection target data, an average of values at corresponding points in the detection target data from one to N cycles ago in computing difference values with respect to the preceding N cycles, the detection target data being data as an anomaly indication detection target and
• a first waveform analysis circuitry to determine whether or not there is an anomaly indication through waveform similarity analysis using the difference values with respect to the preceding N cycles computed by the preprocessing circuitry of the anomaly indication detector and the learned data.

An anomaly indication detection system comprising:

• a learner to generate learned data to be used for anomaly indication detection;
• and
• an anomaly indication detector to perform anomaly indication detection using the learned data, wherein
• the learner performs normal waveform learning using normal data and anomaly indication waveform learning using anomaly indication data in generating the learned data, the normal data being data from a normal state, the anomaly indication data being data with anomaly indications.

A learning method for a learner to generate learned data to be used for anomaly indication detection, the learning method comprising:

• performing, by the learner, normal waveform learning using normal data, the normal data being data from a normal state;
• and
• performing, by the learner, anomaly indication waveform learning using anomaly indication data, the anomaly indication data being data with anomaly indications, wherein
• the learned data is generated through the normal waveform learning and the anomaly indication waveform learning.

A non-transitory computer-readable storage medium having a program stored therein, the program to cause a computer system that generates learned data to be used for anomaly indication detection to execute:

• performing normal waveform learning using normal data, the normal data being data from a normal state;
• and
• performing anomaly indication waveform learning using anomaly indication data, the anomaly indication data being data with anomaly indications, wherein
• the learned data is generated through the normal waveform learning and the anomaly indication waveform learning.