Kabushiki Kaisha Toshiba

Takuro Moriyama

Hideyuki Aisu

Hisaaki Hatano

Gaku Ishii

Tomoshi Otsuki

Mikito Iwamasa

Kenichi Fujiwara

Hisashi Hayashi

Shuichiro Imahara

Yoshiyuki Sakamoto

A bias estimation apparatus according to an embodiment estimates a bias included in a measured values by each sensor. The bias estimation apparatus includes a reference model builder, a temporary bias generator, a corrected measured value calculator, a similarity calculator, a similarity selector, a score calculator, and an estimated bias determiner. The reference model builder builds a reference model of the measured value packs. The temporary bias generator generates a temporary bias pack. The corrected measured value calculator calculates corrected measured value packs. The similarity calculator calculates a similarity of each corrected measured value pack. The similarity selector selects a part of the similarities according to their values from among the similarities. The score calculator calculates a score based on the selected similarities. The estimated bias determiner determines an estimated bias which is an estimated value of the bias based on the score.

Bias estimation apparatus and method and failure diagnosis apparatus and method

A characteristic value estimation device has a sensor data input unit to input sensor data detected by one or more sensors, a model input unit to input a first calculation model, a model learning unit to perform learning on a second calculation model, a model switch to select any one of the first calculation model and the second calculation model, a predictive value calculation unit to calculate an error of the calculation model, a probability distribution correction unit to correct the probability distribution of the uncertain parameter, a virtual sensor value estimation unit to estimate sensor data of a virtual sensor arranged virtually, a characteristic value distribution estimation unit to estimate a detailed distribution of the characteristic value, the sensor data of the virtual sensor, and the sensor data of the sensor, and a reliability calculation unit to calculate a reliability of the precise characteristic value distribution.

Characteristic value estimation device and characteristic value estimation method

According to one embodiment, a monitoring device includes a variable selector and an anomaly detector. The variable selector is configured to select context variables which indicate conditions when content variables were obtained based on values of the content variables and values of the context variables included in base data, and values of the content variables and values of the context variables included in target data. The anomaly detector is configured to detect anomalies in the target data using the context variables which were selected by the variable selector.

Monitoring device, monitoring method and non-transitory storage medium

According to one embodiment, an information processing device includes an anomaly detector and an integration unit. The anomaly detector is configured to estimate a degree of drift anomaly based on measured values of a sensor during a sub-period which is a part of a monitored period. The integration unit is configured to estimate the degree of drift anomaly accumulated within the monitored period based on the estimated degrees of drift anomaly accumulated within the sub-periods. Symptoms of a drift anomaly include mismatches between the actual values and measured values.

Information processing device, information processing method and non-transitory computer readable medium

According to one embodiment, a sensor failure diagnosis device includes a drift estimator and a global rank determiner. The drift estimator estimates, based on data on measured values from sensors contained in a plurality of sensor groups including a sensor under inspection, whether or not drift failure that is steady deviation of a measured value from a true value has occurred in the sensor under inspection or a degree of the drift failure. The global rank determiner determines global ranks that are priorities of the plurality of sensor groups in the inspection based on a plurality of estimated results by the drift estimator.

Growing community of inventors

Kanagawa, Japan

Takuro Moriyama