Abstract: A control unit 20 calculates a search word representation vector and a search document representation vector of a search word on the basis of a first knowledge graph 16B, a second knowledge graph 16C, and first representation vector management information 16D, and searches, on the basis of a combination of the search word representation vector and a plurality of search document representation vectors, a document represented by the search document representation vector included in the combination.

Abstract: An information processing device includes processing circuitry configured to classify a plurality of partial waveform patterns that characterize a plurality of time series data into a plurality of classes based on the plurality of time series data classified into the plurality of classes, update shapes of the partial waveform patterns by fitting the partial waveform patterns to the time series data of the corresponding class, and reclassify the plurality of time series data into the plurality of classes based on the updated partial waveform patterns and difficulty levels that represent degrees of difficulty of classification and interpretation of the time series data.

Abstract: A time-series feature extraction apparatus has a coefficient outputter to output a coefficient to be used in calculation for classifying time series data into a plurality of segments, a segment position outputter to perform calculation for classifying the time series data into the plurality of segments based on the coefficient to output information on boundary positions of the plurality of segments, a cluster classifier to classify the plurality of segments into a certain number of plurality of clusters equal to or smaller than a certain number of the plurality of segments, a representative element outputter to output a representative element which represents a local feature of each of the plurality of clusters and is set for each of the plurality of segments, a feature degree calculator to calculate a feature degree of the representative element, and a representative element updater to update the representative element based on the feature degree.

Abstract: A method and system for detecting anomalies in waveforms in an industrial plant. During a learning stage, one or more training waveforms are received from sensors monitoring a plurality of equipment in the industrial plant. The one or more training waveforms are used to generate a representative waveform and deviations of the one or more training waveforms from the representative waveform are determined. Based on the deviations, groups are created. A model may be associated with each group for building an expected waveform pattern. When test waveforms are received, based on the electrical and physical properties of the test waveforms, each test waveform is classified into one of the groups. Thereafter, each waveform is compared with the expected waveform pattern associated with the group to which the respective test waveform belongs, to detect the anomaly.

Abstract: An information processing device includes processing circuitry configured to classify a plurality of partial waveform patterns that characterize a plurality of time series data into a plurality of classes based on the plurality of time series data classified into the plurality of classes, update shapes of the partial waveform patterns by fitting the partial waveform patterns to the time series data of the corresponding class, and reclassify the plurality of time series data into the plurality of classes based on the updated partial waveform patterns and difficulty levels that represent degrees of difficulty of classification and interpretation of the time series data

Abstract: An information processing device includes a memory and one or more processors coupled to the memory. The one or more processors are configured to: generate a plurality of segments segmented by respective operating states of a target device based on time-series sensor data detected by a sensor that observes the target device; extract target data included in a segment having the same operating state as the operating state at certain first time out of the segments; and generate estimated data estimated to be output from the sensor at specified time different from the first time based on the target data.

Abstract: One embodiment of the present invention provides an information processing apparatus for precisely estimating a state of equipment. An information processing apparatus as one embodiment of the present invention includes: an acquirer; a calculator; and a determiner. The acquirer is configured to acquire first data about a predetermined target. The calculator is configured to calculate distribution of magnitude of a value included in the first data. The determiner is configured to determine a portion of a width of the distribution as a specific range used for estimating a state of the target based on the distribution.

Abstract: An information processing device has a data generator that generates pseudo data of normal data and pseudo data of abnormal data, on the basis of the normal data, similar data of the normal data, the abnormal data, and similar data of the abnormal data, first update circuitry that updates a first parameter multiplied by the pseudo data of the normal data to obtain an abnormality degree of the pseudo data of the normal data and multiplied by the pseudo data of the abnormal data to obtain an abnormality degree of the pseudo data of the abnormal data, and second update circuitry that updates a second parameter multiplied by the normal data to obtain the similar data of the normal data and a third parameter multiplied by the abnormal data to obtain the similar data of the abnormal data.

Abstract: A time series data analysis method, includes: generating a plurality of first feature vectors including feature amounts of a plurality of feature waveforms, based on distances from a plurality of first time series data sequences to the plurality of feature waveforms, the first time series data sequences belonging to a first class; generating a plurality of second feature vectors including feature amounts of the plurality of feature waveforms, based on distances from a plurality of second time series data sequences to the plurality of feature waveforms, the plurality of second time series data sequences belonging to a second class; and updating the plurality of feature waveforms, based on the plurality of first feature vectors, the plurality of second feature vectors, a performance indicator parameter related to a performance indicator for a classification model and a model parameter including weights on the plurality of feature waveforms.

Abstract: Present disclosure discloses a method and system for detecting anomalies in waveforms in an industrial plant. During a learning stage, one or more training waveforms are received from a plurality of sensors monitoring a plurality of equipment in the industrial plant. The one or more training waveforms are used to generate a representative waveform and deviations of the one or more training waveforms from the representative waveform is determined. Based on the deviations, a plurality of groups is created. A model may be associated with each group for building an expected waveform pattern (reference waveform pattern). In real-time a plurality of test waveforms is received. Based on the electrical and physical properties of the plurality of test waveforms, each test waveform is classified into one of the plurality of groups. Thereafter, each waveform is compared with the reference waveform pattern associated with the group to which the respective test waveform belongs, to detect the anomaly.

Abstract: A time-series feature extraction apparatus has a coefficient outputter to output a coefficient to be used in calculation for classifying time series data into a plurality of segments, a segment position outputter to perform calculation for classifying the time series data into the plurality of segments based on the coefficient to output information on boundary positions of the plurality of segments, a cluster classifier to classify the plurality of segments into a certain number of plurality of clusters equal to or smaller than a certain number of the plurality of segments, a representative element outputter to output a representative element which represents a local feature of each of the plurality of clusters and is set for each of the plurality of segments, a feature degree calculator to calculate a feature degree of the representative element, and a representative element updater to update the representative element based on the feature degree.

Abstract: An apparatus according to one embodiment of the present invention detects an abnormality of a monitoring target on the basis of state data of the target and includes an estimated data calculator, a deviation degree calculator, an abnormality degree calculator, and an abnormality determiner. The estimated data calculator calculates estimated data of a second period on the basis of the state data of the first period. The deviation degree calculator calculates a degree of deviation of the second period on the basis of the state data and the estimated data of the second period. The abnormality degree calculator calculates a degree of abnormality of the second period on the basis of the degree of deviation of the second period. The abnormality determiner determines presence or absence of an abnormality of the target in the second period on the basis of the degree of abnormality in the second period.

Abstract: An information processing device has a data generator that generates pseudo data of normal data and pseudo data of abnormal data, on the basis of the normal data, similar data of the normal data, the abnormal data, and similar data of the abnormal data, first update circuitry that updates a first parameter multiplied by the pseudo data of the normal data to obtain an abnormality degree of the pseudo data of the normal data and multiplied by the pseudo data of the abnormal data to obtain an abnormality degree of the pseudo data of the abnormal data, and second update circuitry that updates a second parameter multiplied by the normal data to obtain the similar data of the normal data and a third parameter multiplied by the abnormal data to obtain the similar data of the abnormal data.

Abstract: An apparatus according to one embodiment of the present invention detects an abnormality of a monitoring target on the basis of state data of the target and includes an estimated data calculator, a deviation degree calculator, an abnormality degree calculator; and an abnormality determiner. The estimated data calculator calculates estimated data of a second period on the basis of the state data of the first period. The deviation degree calculator calculates a degree of deviation of the second period on the basis of the state data and the estimated data of the second period. The abnormality degree calculator calculates a degree of abnormality of the second period on the basis of the degree of deviation of the second period. The abnormality determiner determines presence or absence of an abnormality of the target in the second period on the basis of the degree of abnormality in the second period.