Kabushiki Kaisha Toshiba

Akihiro Suyama

Makoto Sato

Shigeaki Sakurai

Ryohei Orihara

Tomoko Murakami

Minoru Yonezawa

Yuuichi Hanada

Ken Ueno

Yoshiaki Hasegawa

According to one embodiment, an apparatus includes an first storage unit to store a value output by a communication unit, a second storage unit to store positional data indicating a place the modules, an third storage unit to store an output model indicating the relationship between a sunshine condition and an electrical output, a estimation unit to estimate a sunshine condition for each module based on the value and the output model, a forth storage unit to store the sunshine condition estimated, a correction unit to correct the sunshine condition, and a detection unit to calculate an expected electrical output for each module based on the corrected sunshine condition and the output model, and to detect a fault in the modules.

Fault detection apparatus and fault detection method

Provided is an apparatus determining values of N and K for an abnormality diagnostic logic which makes a diagnosis N times for each diagnosis target by using observation values collected therefrom, and generates a diagnosis result showing that the diagnosis target is abnormal if the diagnosis target is judged to be abnormal K or more times. A calculator calculates average false detection rate P, average overlooking rate P, bias level of false detection M, and bias level of overlooking M, based on diagnosis result data and inspection result data. A determiner calculates an optimization metric for each combination of values N and K by using the average false detection rate, the bias level of false detection, the average overlooking rate, and the bias level of overlooking, and selects a pair of N and K by which the optimization metric becomes minimum or a threshold value or less.

Abnormality diagnosis filter generator

There is provided an apparatus, including a first storage storing first time-series data including instruction values given to first to K-th control devices and measured values from first to K-th sensors during a first period, a model optimizer, for each of combinations of two of the control devices, generating a diagnostic model instance of a predetermined target model and obtaining an optimized diagnostic model instance in which parameters of the diagnostic mode instance are identified, a second storage storing second time-series data acquired during a second period, a calculator, for each of the optimized diagnostic model instances, calculating a determination score for each of the control devices and sensors using the first and second time-series data, and a determiner determining presence or absence of an abnormality for each of the control devices and the sensors based on each determination score.

Anomaly detecting apparatus

There is provided with a method including: acquiring string data made up of a plurality of monitoring items from an apparatus to be monitored at predetermined or arbitrary time intervals; storing each acquired string data in a storage temporarily; calculating an average and variation for each of the monitoring items and correlation information indicating a correlation between the monitoring items by using each string data stored in the storage; normalizing the acquired string data by using the average and the variation of each monitoring item; calculating a distance from the correlation information for normalized string data by carrying out a computation using the normalized string data and the correlation information; and deciding whether or not there is an abnormal sign in the apparatus to be monitored depending on whether or not calculated distance falls within a confidence interval set in advance for a certain probability distribution.

Apparatus and method for detecting abnormal sign

A time series pattern generation unit extracts a key concept corresponding to a text of each data element of a plurality of time series data by referring to an extraction rule memory, and generates a plurality of time series patterns. A colony generation unit unifies a plurality of data elements having the same key concept and substantially the same time duration, and generates colonies of unified elements. In response to a selection of a first colony, a colony extraction unit specifies a second colony having a key concept contrary to the first colony, and extracts colonies earlier than the first colony and the second colony. A time reason extraction unit extracts a time reason as a divergence condition of the first colony from the second colony from the extracted colonies.

Time series pattern extraction apparatus and method

A method for displaying multidimensional data on a screen of a display device, includes assigning a first dimension element, having been selected by a user from among the multidimensional data, to an axis; giving weights to a plurality of member regions using a function of number of data belonging to respective members of the first dimension element; defining the weighted member regions as a plurality of regions on the axis, which are partitioned for the respective members of the first dimension element; arranging the weighted member regions within the screen to form a table; and displaying, in each cell region corresponding to the weighted member region, data belonging to each cell region and belonging to a second dimension element having been selected by the user from among the multidimensional data. The second dimension element is different from the first dimension element.

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Tokyo, Japan

Akihiro Suyama