Abstract:
A system analysis method includes: acquiring history information indicating, based on sensor values outputted by sensors, whether one of sensor values outputted by respective sensors indicates abnormality and/or whether individual relationship between sensor values outputted by different sensors indicates abnormality in time-series manner; estimating a change point group of change points, each indicating a time point system state has changed, based on history information; estimating relevance levels, each indicating relevance to the system state between two arbitrary time points included in the change point group; generating groups of change point groups by classifying the change point group into a plurality of groups based on the history information and the relevance levels; and generating and outputting output information, as information relating abnormality per group of the change point groups.
Abstract:
A display device is provided with: a history information generation part that determines, as an abnormal sensor, a sensor having an abnormal value with respect to each of a plurality of sensors provided in a target; a clustering part that clusters the determined abnormal sensor(s) to belong to any of a plurality of groups; a cluster hierarchical structuring part that determines a hierarchy among the plurality of groups; and an output part that associates, with the abnormal sensor(s), symbol(s) capable of differentiating groups to which the abnormal sensor(s) belongs, and uses the symbols to present to a user the abnormal sensor(s) together with information indicating hierarchical relationships among groups.
Abstract:
A system analysis apparatus includes a history information generation part generating, based on sensor values output by a plurality of sensors provided in a system, history information representing in a time series whether or not the sensor value(s) output by each of the plurality of sensors is abnormal, and/or whether or not a relationship between the sensor values output by different sensors is abnormal, a clustering part classifying the plurality of sensors into a plurality of groups, based on the history information, and a cluster hierarchy structuring part structuring a hierarchy of the plurality of groups by using causality information that indicates causality between the sensor values output by the plurality of sensors.
Abstract:
Provided is an optical element that highly efficiently radiates light with high directivity at low etendue. The optical element includes a light emission layer (103) generating an exciton to emit light, a plasmon excitation layer (105) having a higher plasma frequency than a light emission frequency of the light emission layer (103), an output layer (107) converting light or a surface plasmon generated on an upper surface of the plasmon excitation layer (105) into light with a predetermined output angle to output the light, and a dielectric layer (102). In the optical element, a real part of an effective dielectric constant with respect to the surface plasmon is higher in an upper side portion than the plasmon excitation layer (105) than in a lower side portion than the plasmon excitation layer (105); a dielectric constant with respect to the light emission frequency of the light emission layer (103) is higher in a lowest layer than in a layer adjacent to a lower side of the plasmon excitation layer (105); and assuming that a radiation angle of a surface plasmon-derived highly directional radiation from the plasmon excitation layer (105) to the output layer (107) side is θout,spp and a radiation angle of an optical waveguide fundamental mode-derived highly directional radiation is θout,light, an absolute value of a difference between the θout,spp and the θout,light is less than 10 degrees.
Abstract:
A method of learning a neural network includes:
obtaining unlabeled data including maintenance cycle data that are time series operation data of a target device that is a maintenance target, and labeled data including lifecycle data that are time series operation data up to failure occurrence of the target device; and updating a weight parameter of the neural network so as to reduce a prediction error of a difference in the remaining life between two points in the maintenance cycle data and a prediction error of the remaining life in the lifecycle data.
Abstract:
Provided is an analysis system including: an analysis unit including a classifier that performs classification of an event type on input time-series data; a display information generation unit that generates first display information used for displaying, out of the time-series data, first time-series data in which association of an event type is undecided and which is classified by the classifier as a first event type corresponding to a state where a target event is occurring, second time-series data associated with the first event type, and third time-series data associated with a second event type corresponding to a state where the target event is not occurring; and an input unit that accepts first input regarding association of an event type with the first time-series data.
Abstract:
A system analyzing device (100) includes a history information generation unit (14) that generates history information for each of multiple sensors (21) included in a subject system (200) based on sensor values output by the sensors (21), and an output unit (16) that presents, to a user, cluster information obtained by clustering the sensors (21) into one or more groups based on the generated history information.
Abstract:
A model with few false reports and little missing detection is generated even if the number of models is large. A system analysis device 1 includes a feature acquisition unit 1211 and a selection unit 1221. The feature acquisition unit 1211 acquires a feature of a first data item. The selection unit 1221 selects a model for learning a relationship between the first data item and a second data item, based on the feature.
Abstract:
A system analyzing device according to the present invention includes: a collection unit that collects a plurality of pieces of sensor data of a monitored system; a storage unit that stores a correlation modes based on at least one of a plurality of pieces of sensor data; and a standard contribution acquisition unit that acquires, for a predicted value of an objective variable of a regression equation thereof, a standard contribution indicating a ratio of contribution of each of the data included as explanatory variables.
Abstract:
An information processing system predicts a condition of a device from time-series data acquired from the device, by using a trained model. When predicting, the information processing system allows the trained model to extract features that depend on the sequence from pieces of partial time-series data obtained by dividing the time-series data along the time axis, generate first vectors in which the extracted features are embedded, each of the first vectors corresponding to each of the pieces of the partial time-series data one to one, generate a second vector in which the first vectors are embedded, extract features that depend on the sequence from the first vectors, generate a third vector in which the extracted features are embedded, generate a fourth vector in which the second vector and the third vector are embedded, and transform the fourth vector into a first value that represents a condition of the device.