Mitsubishi Heavy Industries Limited

Mitsubishi Hitachi Power Systems, Ltd.

Mitsubishi Power, Ltd.

Mayumi Saito

Ichiro Nagano

Kuniaki Aoyama

Keiji Eguchi

Shintaro Kumano

Toru Tanaka

Katsuhiko Abe

Takahiro Yamauchi

Nobuhiko Nishimura

Fumitoshi Sakata

Akihiro Hamasaki

Yoshiyuki Hamaji

Kouji Satake

Masanori Fujioka

Atsushi Sugimoto

A plant monitoring device () is provided with: a state quantity acquiring unit () which acquires state quantities for each of a plurality of characteristic items relating to a plant; an abnormality degree calculating unit () 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 () 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 () which determines the operating state of the plant on the basis of the calculated distances.

Plant monitoring device, method and program using a corrected Mahalanobis distance to determine plant abnormality

A plant monitoring device () is provided with: a detection value acquisition unit () that acquires a bundle of detection values; a first Mahalanobis distance calculation unit () 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 () that calculates a first SN ratio for each of a plurality of evaluation items; a second Mahalanobis distance calculation unit () that calculates a second Mahalanobis distance by increasing or decreasing each of the detection values; a second SN ratio acquisition unit () 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 () that calculates an added value of a plurality of the second SN ratios acquired within a prescribed period for each of the evaluation items.

Industrial plant monitoring device, industrial plant monitoring method, and program

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.

Diagnosing device, diagnosing method, and program

A unit space update device, which generates a unit space, includes: a detection value acquisition unit which acquires a bundle of detection values detected at a constant cycle; and a deletion processing unit which deletes one bundle from the bundles of the detection values constituting the unit space, when adding the bundle of the detection values to the unit space. The deletion processing unit includes at least one among: a first processing unit which randomly deletes one bundle from the bundles of the detection values forming the unit space; and a second processing unit which, with respect to the detection value of the predetermined evaluation item among the plurality of evaluation items, deletes any one bundle of the bundles including the detection value with the highest appearance frequency in the unit space, or one of two bundles having the smallest difference between the detected values in the unit space.

Unit space update device, unit space update method, and program

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.

Monitoring device, method for monitoring target device, and program

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

Plant monitoring device, plant monitoring method, and program

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.

A unit space generating device () which generates a unit space for use when diagnosing an operating state of a plant on the basis of a Mahalanobis distance is provided with: a sampling data acquiring unit (A) which acquires a sampling data group comprising a plurality of state quantities of the plant, measured with a fixed period; an adoption determining unit (C) which, on the basis of an adoption probability calculated each time the sampling data group is acquired, determines whether the sampling data group is to be adopted as a unit space generation data group serving as the basis for a unit space; a unit space generating unit (D) which generates unit spaces on the basis of a plurality of the adopted unit space generation data groups; and an output value acquiring unit (B) which acquires an output value of the plant corresponding to the sampling data group.

Unit space generating device, plant diagnosing system, unit space generating method, plant diagnosing method, and program

A water quality management device is provided with a water quality index value acquisition unit which acquires water quality index values including a makeup water quality index value indicating water quality of makeup water and a circulating water quality index value indicating water quality of circulating water, and a determining unit which determines an amount of concentration control associated with a concentration rate of a circulating water system based on the water quality index values acquired by the water quality index value acquisition unit.

Water quality management device, water treatment system, water quality management method, and program for optimizing water treatment system

A method for trouble managing in equipment is provided, with which optimal timing of repairing the equipment and occurrence of malfunction probable to occur concurrently with present malfunction or later stage can be inferred with sufficient accuracy, and which can be adopted for large-scale equipment used in a plant. The method for trouble managing of equipment by monitoring operation condition of the equipment with a monitoring means and inferring cause of malfunction of the equipment by an inference means which infers the cause of the malfunction using measured data concerning the operation condition obtained by the monitoring means when malfunctions occur as nodes of the inference means, comprises selecting acoustic data most similar to sound emitted from the equipment in which malfunction has occurred from among a plurality of acoustic data provide beforehand, selecting morphologic data most similar to a pattern of operating condition in the equipment from among a plurality of morphologic data provide beforehand, adding the selected acoustic data and the selected morphologic data to the nodes, and performing inference of cause of the malfunction of the equipment by a first Bayesian network base on the nodes.

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Tokyo, Japan

Mayumi Saito