Abstract: A method for calculating an SN ratio gain, in which the magnitude level of the SN ratio gain based on an orthogonal array size for each unit space in a multi-MT method is corrected. The method for calculating an SN ratio gain involves creating a plurality of unit spaces by using data detected by a plurality of sensors provided to a plant, and evaluating the state of the plant using the unit spaces by a multi-MT method, and includes: a step for allocating, for each of the unit spaces, one of the sensors used for creating the unit space to a 2-level orthogonal array, and generating orthogonal arrays for the respective unit spaces; a step for calculating, for each of the orthogonal arrays for the respective unit spaces, an SN ratio for each line of the orthogonal array; and a step for calculating an SN ratio gain.

Abstract: A plant monitoring device (20) is provided with: a state quantity acquiring unit (211) which acquires state quantities for each of a plurality of characteristic items relating to a plant; an abnormality degree calculating unit (212) which calculates a degree of abnormality representing a degree of approach toward an abnormal side relative to a limit value that is predetermined for each characteristic item, for the state quantities acquired at a plant monitoring timing; a distance calculating unit (213) which uses a statistical technique to calculate distances representing the degrees of separation, from the normal operating state of the plant, of the state quantity and the degree of abnormality acquired at the monitoring timing; and a determining unit (214) which determines the operating state of the plant on the basis of the calculated distances.

Abstract: A plant monitoring device (20) is provided with: a detection value acquisition unit (211) that acquires a bundle of detection values; a first Mahalanobis distance calculation unit (212) that calculates a first Mahalanobis distance by using as a reference a unit space generated on the basis of a bundle of past detection values; a first SN ratio calculation unit (214) that calculates a first SN ratio for each of a plurality of evaluation items; a second Mahalanobis distance calculation unit (215) that calculates a second Mahalanobis distance by increasing or decreasing each of the detection values; a second SN ratio acquisition unit (216) that converts the first SN ratio for each of the evaluation items and acquires a second SN ratio on the basis of the first and second Mahalanobis distances; and an addition unit (217) that calculates an added value of a plurality of the second SN ratios acquired within a prescribed period for each of the evaluation items.

Abstract: A plant monitoring device for monitoring a plant is provided with: a measured data acquisition unit which, for each prescribed period, acquires measured data of multiple variables indicating the state of the plant; a comparison unit which compares a diagnostic threshold value, and a deviation indicator value, which indicates the deviation between measured data and a reference dataset that relates to the aforementioned multiple variables; a reference data updating unit which updates the reference dataset; and an operation mode switching unit which switches the operation mode of the plant monitoring device between a monitoring mode, in which plant monitoring is performed on the basis of the comparison results by the comparison unit and a learning mode, in which at least the measured data for which the deviation indicator value is greater than the diagnostic threshold value is imported into the reference dataset by the reference data update unit.

Abstract: A method of monitoring a plant by using a Mahalanobis distance calculated from data of a plurality of variables each of which indicates a state of the plant includes: a dividing step of dividing a range of a single variable which indicates a state of the plant into a plurality of first range bands on the basis of a frequency distribution of the single variable; and a unit space creating step of creating a plurality of unit spaces which serve as a basis of calculation of the Mahalanobis distance on the basis of the respective data of the plurality of variables respectively corresponding to a plurality of second range bands of the single variable determined on the basis of the plurality of first range bands.

Abstract: A plant monitoring device (20) is provided with: a detection value acquisition unit (211) that acquires a bundle of detection values; a first Mahalanobis distance calculation unit (212) that calculates a first Mahalanobis distance by using as a reference a unit space generated on the basis of a bundle of past detection values; a first SN ratio calculation unit (214) that calculates a first SN ratio for each of a plurality of evaluation items; a second Mahalanobis distance calculation unit (215) that calculates a second Mahalanobis distance by increasing or decreasing each of the detection values; a second SN ratio acquisition unit (216) that converts the first SN ratio for each of the evaluation items and acquires a second SN ratio on the basis of the first and second Mahalanobis distances; and an addition unit (217) that calculates an added value of a plurality of the second SN ratios acquired within a prescribed period for each of the evaluation items.

Abstract: A plant monitoring method using a Mahalanobis distance calculated from data of a plurality of variables indicating the state of the plant. The plant monitoring method includes: acquiring data in a first period in the past up to the current point of time as first data; predicting data in a second period from the current point of time as second data; and creating, on the basis of the first data and the second data, a unit space that serves as a base for calculating the Mahalanobis distance. In the prediction step, the second data is predicted on the basis of: data in a third period obtained, as third data, by shifting the first period to the past by a prescribed period; data in a fourth period obtained, as fourth data, by shifting the second period to the past by the prescribed period, and the first data.

Abstract: Provided are a diagnosing device, a diagnosing method, and a program with which it is possible to detect abnormalities accurately even if there is a small amount of data or the number of data points varies. This diagnosing device is provided with a Mahalanobis distance calculating unit which calculates the Mahalanobis distance (referred to as ‘MD value’ hereinbelow) of a detected value, and an abnormality determining unit which determines the presence or absence of an abnormality on the basis of the MD value, wherein the abnormality determining unit determines the presence or absence of an abnormality by arranging that a determination that there is no abnormality is more likely to occur if the number of samples per unit space is small than if the number of samples per unit space is large.

Abstract: A cause estimation device includes: an acquisition unit that acquires a measurement value of a target apparatus; a likelihood calculation unit that calculates, in a case where it is assumed that events occur due to a common cause, a likelihood of an occurrence of each of the events based on the measurement value; a table storage unit that stores a cause table in which a cause of occurrence of the event and a frequency of the cause of occurrence are associated with each other for each of the plurality of events; and an estimation unit that rewrites the frequency registered in the cause table into a frequency in the case where it is assumed that the plurality of events occur due to the common cause, and estimates the cause of occurrence based on the rewritten cause table and the likelihood.

Abstract: An acquisition unit acquires a bundle of detection values for each of a plurality of sensor values pertaining to a plant. A distance calculation unit obtains the Mahalanobis distance of the bundle of detection values acquired by the acquisition unit using, as reference, a unit space constituted by a collection of bundles of detection values for each of the plurality of sensor values. A determining unit determines, based on whether the Mahalanobis distance is at or within a prescribed threshold, whether the operation state of the plant is normal or abnormal. A trend specification unit specifies a trend with regards to at least one sensor value. An abnormality cause estimation unit estimates an abnormality cause based on the trend for the sensor value(s), and a fault site estimation database for holding the relationship between abnormality causes that may occur in the plant and sensor values for each of the trends.

Abstract: An acquisition unit is configured to acquire measurement values of a target device. The measurement values that are acquired include at least a temperature and a flow rate of an input fluid to be input to the target device, and a temperature and a flow rate of an output fluid to be output from the target device. A correction unit is configured to obtain a correction measurement value by which the measurement values are corrected through thermal equilibrium calculations based on the measurement values. A distance calculation unit is configured to calculate a Mahalanobis distance with a factor of the correction measurement value.

Abstract: A plant monitoring device (20) is provided with: a detection value acquisition unit (211) that acquires a bundle of detection values; a first Mahalanobis distance calculation unit (212) that calculates a first Mahalanobis distance; a plant state determination unit (213) that determines whether the operation state of a plant is normal or abnormal; a cause detection value estimation unit (214) that estimates a cause detection value which represents a cause of the abnormality of the plant; a second Mahalanobis distance calculation unit (215) that calculates a second Mahalanobis distance by increasing or decreasing the detection value estimated as the cause detection value; and an identification unit (216) that identifies whether the abnormality can be relieved by increasing or decreasing the detection value estimated as the cause detection value.

Abstract: A state quantity acquisition unit acquires a state quantity of a target apparatus including a temperature of the target apparatus. A load specification unit specifies a load history of the target apparatus, based on the state quantity. A remaining life calculation unit calculates a parameter related to a remaining life of the target apparatus for each of a plurality of degradation types, based on the load history specified by the load specification unit.

Abstract: Provided are a diagnosing device, a diagnosing method, and a program with which it is possible to detect abnormalities accurately even if there is a small amount of data or the number of data points varies. This diagnosing device is provided with a Mahalanobis distance calculating unit which calculates the Mahalanobis distance (referred to as ‘MD value’ hereinbelow) of a detected value, and an abnormality determining unit which determines the presence or absence of an abnormality on the basis of the MD value, wherein the abnormality determining unit determines the presence or absence of an abnormality by arranging that a determination that there is no abnormality is more likely to occur if the number of samples per unit space is small than if the number of samples per unit space is large.

Abstract: An operation control device for a single shaft gas turbine selects an operation mode based on a load state of a power generator, and controls the turbine based on the operation mode. In a first operation mode, a rotational speed of the turbine is maintained within a first rotational speed range, and in a second operation mode, the rotational speed is maintained within a second rotational speed range set on a lower rotational speed side than the first rotational speed range. The second rotational speed range is set on the lower rotational speed side than the first rotational speed range with a first non-selection rotational speed range set therebetween.

Abstract: A plant monitoring device (20) is provided with: a state quantity acquiring unit (211) which acquires state quantities for each of a plurality of characteristic items relating to a plant; an abnormality degree calculating unit (212) which calculates a degree of abnormality representing a degree of approach toward an abnormal side relative to a limit value that is predetermined for each characteristic item, for the state quantities acquired at a plant monitoring timing; a distance calculating unit (213) which uses a statistical technique to calculate distances representing the degrees of separation, from the normal operating state of the plant, of the state quantity and the degree of abnormality acquired at the monitoring timing; and a determining unit (214) which determines the operating state of the plant on the basis of the calculated distances.

Abstract: An acquisition unit is configured to acquire measurement values of a target device. A likelihood calculation unit is configured to calculate an occurrence likelihood for each of a plurality of phenomena that are liable to occur to the target device based on the measurement values acquired by the acquisition unit. A table storage unit is configured to store a table in which the plurality of phenomena and occurrence causes of abnormalities of the target device are associated to each other. As estimation unit is configured to estimate the occurrence causes based on the occurrence likelihood and the table.

Abstract: A plant monitoring device (20) is provided with: a detection value acquisition unit (211) that acquires a bundle of detection values; a first Mahalanobis distance calculation unit (212) that calculates a first Mahalanobis distance; a plant state determination unit (213) that determines whether the operation state of a plant is normal or abnormal; a cause detection value estimation unit (214) that estimates a cause detection value which represents a cause of the abnormality of the plant; a second Mahalanobis distance calculation unit (215) that calculates a second Mahalanobis distance by increasing or decreasing the detection value estimated as the cause detection value; and an identification unit (216) that identifies whether the abnormality can be relieved by increasing or decreasing the detection value estimated as the cause detection value.

Abstract: A diagnostic device includes a storage unit that stores first information including a first abnormal event which occurred in the past in a plant, a first attribution event that is a cause of the first abnormal event, and a first occurrence probability of the first attribution event, in which a causal relationship between the first abnormal and attribution events is indicated by a tree structure, and second information including a second abnormal event which is supposed to occur in the plant but has not yet occurred, a second attribution event that is a cause of the second abnormal event, and a second occurrence probability of the second attribution event, in which a causal relationship between the second abnormal and attribution events is indicated by a tree structure; and an estimation unit that estimates the cause of the sign of the abnormality, based on the first and second information.

Abstract: A turbine-cooling system of a gas turbine system includes a first intra-vane flow passage defined in a first stator vane so as to penetrate the first stator vane in a radial direction, a second intra-vane flow passage defined in a second stator vane so as to penetrate the second stator vane in the radial direction, an intra-rotation-shaft flow passage connecting the first intra-vane flow passage and the second intra-vane flow passage in a rotation shaft, an extra-turbine flow passage connecting the first intra-vane flow passage and the second intra-vane flow passage, a boost compressor configured to make cooling air flow sequentially through the first intra-vane flow passage, the intra-rotation-shaft flow passage, the second intra-vane flow passage, and the extra-turbine flow passage, and a cooling unit configured to cool the cooling air.