Hitachi-high-technologies Corporation

Hitachi, Ltd.

Hitachi Power Solutions Co., Ltd.

Hitachi Global Life Solutions, Inc.

Tsuru Educational Foundation

Hisae Shibuya

Shunji Maeda

Akira Hamamatsu

Yuji Takagi

Kenji Obara

Hidetoshi Nishiyama

Yuta Urano

Kaoru Sakai

Toshiyuki Nakao

Yoshimasa Oshima

Naoki Hosoya

Shigenobu Maruyama

Tadashi Suzuki

Keiko Oka

Toujirou Noda

Shouzou Miyabe

Yuuji Takagi

Naoki Miyakoshi

Hiroshi Kusumoto

Akio Yazaki

Eiji Ogata

Hiroaki Kasai

Kazuhiro Tsuchihashi

Yoko Kokugan

Yoshio Yoshiwara

Shigeyoshi Chikuma

Tatsuo Horiuchi

Norikazu Sasaki

Masami Kusano

Yoshinao Nozaki

Toshiaki Kobari

Noriyuki Tokura

A refrigerant quantity diagnosis device includes: an evaluation value calculation part that calculates a refrigerant quantity index evaluation value including a ratio of a measured refrigerant quantity index value to a reference refrigerant quantity index value in each of a normal period and a determination period; and a refrigerant quantity diagnosis part that diagnoses a refrigerant quantity based on the refrigerant quantity index evaluation value. The evaluation value calculation part calculates a first refrigerant quantity index evaluation value in the normal period and a second refrigerant quantity index evaluation value in the determination period, based on respective reference refrigerant quantity index values having respective values associated with an operating condition of interest, and respective measured refrigerant quantity index values. The refrigerant quantity diagnosis part compares information on the first refrigerant quantity index evaluation value with that on the second one and thereby diagnoses the refrigerant quantity.

Refrigerant quantity diagnosis device, refrigerant system, and refrigerant quantity diagnosis method

An optical condition determination system includes a simulation execution unit that performs an optical simulation on a surface texture model that models a surface texture of a target object of the appearance inspection, and a defect model that models a defect of the target object, under a plurality of optical conditions to generate a surface texture image and a defect image, an image synthesizing unit that synthesizes the surface texture image and the defect image generated by an optical simulation under the same optical condition to generate a synthetic image, an evaluation value calculating unit that calculates an evaluation value representing easiness of detecting the defect in the synthetic image, a correlation analysis unit that analyzes a correlation between an optical condition and the evaluation value corresponding to the synthetic image, and an optimum condition searching unit that searches for the optical condition suitable for the appearance inspection based on an analysis result of the correlation.

Optical condition determination system and optical condition determination method

A phenomenon pattern extraction unit extracts a phenomenon pattern of a past sensor signal of a facility. A related information correlation unit correlates the sensor signal based on maintenance history information. A phenomenon pattern classification reference creation unit creates a classification reference for classifying a phenomenon pattern based on the extracted phenomenon pattern and a work keyword included in the maintenance history information correlated with the sensor signal as the source of the phenomenon pattern. A phenomenon pattern classification unit classifies the phenomenon pattern based on the classification reference. A diagnosis model creation unit creates a diagnosis model for estimating a work keyword suggested to a maintenance worker based on the classified phenomenon pattern and the work keyword.

Information processing apparatus, diagnosis method, and program

In an anomaly detecting method by using multi-dimensional time series sensor signals including, generating anomaly model by using data of a learning period including neither that period nor any exclusion candidate period, calculating anomaly measurements on the basis of the distance from the normal model and, making a period containing the maximum anomaly measurement value but no exclusion candidate period, learning exclusion periods and anomaly determining thresholds are determined as learned data on the basis of the result in each round, generates anomaly model data in a learning period except learning-exclusion periods regarding acquired data or data in a designated evaluation period, an anomaly measurement at each time point is calculated on the basis of the distance from the normal model, and data at each time point is determined to be anomaly or normal by comparing the anomaly measurements with anomaly determining thresholds.

Anomaly detecting method, and apparatus for the same

An objective is to identify the health state of mechanical equipment and provide information usable for determining maintenance work timing or the like. A health management system includes a time-series data acquisition unit configured to acquire multi-dimensional sensor data and environmental data from mechanical equipment; a first discrimination unit configured to quantify the equipment state of the mechanical equipment by a statistical method using normal data as learning data; a second discrimination unit configured to quantify the health state indicating the performance or quality of the mechanical equipment by a statistical method using normal data; and an output unit configured to display and/or output to the outside the quantified equipment state and health state.

Health management system, fault diagnosis system, health management method, and fault diagnosis method

To sensing an anomaly on the basis of a multi-dimensional time series sensor signal, in order to determine the next action for a countermeasure, survey, or the like, the present invention is configured such that a multi-dimensional feature vector for each time is extracted on the basis of a sensor signal, a reference feature vector for each time is extracted on the basis of a set of characteristic vectors for a predetermined learning period and the characteristic vector of each time, an anomaly measure is calculated on the basis of the difference between the feature vectors for the times and the reference feature vectors, an anomaly is detected by comparing the anomaly measure and a predetermined threshold value, and the anomaly-related sensor for the time the anomaly is detected is identified on the basis of a 2-dimensional distribution density of feature values.

Anomaly diagnosis method and apparatus

In case-based anomaly indication detection in a facility, there are problems such as error generation due to insufficient learning data or execution difficulty due to increased memory capacity and calculation time when the learning data period has been increased to obtain the learning data sufficiently. Provided is a method for monitoring facility state on the basis of a time series signal outputted from the facility, wherein an operation pattern label for each fixed interval is assigned on the basis of the time series signal, learning data is selected on the basis of the operation pattern label for each fixed interval, a normal model is created on the basis of the selected learning data, an anomaly measure is calculated on the basis of the time series signal and the normal model, and the facility state is determined to be anomaly or normal on the basis of the calculated anomaly measure.

Facility state monitoring method and device for same

Provided is an anomaly detection method and system capable of constructing determination condition rules of anomaly detection from case-based anomaly detection by way of multivariate analysis of a multi-dimensional sensor signal, applying the rules to design-based anomaly detection of individual sensor signals, and also appropriately executing setting and control of threshold values for highly sensitive, early, and clearly visible detection of anomalies. Anomaly detection on the basis of a case base by way of multivariate analysis controls design-based anomaly detection. That is to say, (1) anomaly detection on the basis of a case base performs selection of sensor signals and anomaly detection according to various types of anomalies. Specifically, anomaly detection (characteristic conversion), evaluation of level of effect of each signal, construction of determination conditions (rules), and display and selection of sensor signals corresponding to the anomaly are performed. (2) Design-based anomaly detection for individual sensor signals performs anomaly detection after the above have been performed. Specifically, setting and control of thresholds, display of thresholds, and anomaly detection and display are performed.

Anomaly detection and diagnosis/prognosis method, anomaly detection and diagnosis/prognosis system, and anomaly detection and diagnosis/prognosis program

The anomaly diagnosis system includes the state measure calculator acquiring sensor data from sensors in a machine facility as time series data; an approximation formula calculator calculating a state measure being an index indicating a state of the machine facility, such as anomaly and a performance by a statistical method in which the time series data is used as learned data; and a state measure estimating unit estimating the state measures until future time using the approximation formula. Whenever the latest time series data is acquired, the reference period in which the time series data corresponding to the state measure referred to calculate the approximation formula by the reference period setting unit, is successively extended by addition of time when the latest time series data is acquired. The approximation formula calculator calculates the approximation formula using the state measure of the time series data acquired in the reference period.

Anomaly diagnosis system and anomaly diagnosis method

The pattern inspection apparatus of the present invention performs comparison between images of regions corresponding to patterns formed to be same patterns, thereby determining mismatch portions across the images to be defects. The apparatus includes multiple sensors that synchronously acquire images of shiftable multiple detection systems different from one another, and an image comparator section corresponding thereto. In addition, the apparatus includes a means for detecting a statistical offset value from the feature amount to be a defect, thereby properly detecting the defect even when a brightness difference is occurring in association with film a thickness difference in a wafer.

Method and apparatus for inspecting patterns formed on a substrate

This invention provides method for detecting advance signs of anomalies, event signals outputted from the facility are used to create a separate mode for each operating state, a normal model is created for each mode, the sufficiency of learning data for each mode is checked, a threshold is set according to the results of said check, and anomaly identification is performed using said threshold. Also, for diagnosis, a frequency matrix is created in advance, with result events on the horizontal axis and cause events on the vertical axis, and the frequency matrix is used to predict malfunctions. Malfunction events are inputted as result events, and quantized sensor signals having anomaly measures over the threshold are inputted as cause events.

Method and device for monitoring the state of a facility

A defect classification method to classify defects by using a classifier having a binary tree structure based on features of defects extracted from detected signals acquired from a defect inspection apparatus includes a classifier construction process for constructing the classifier by setting a branch condition including defect classes respectively belonging to groups located on both sides of the branch point, a feature to be used for branching, and a discriminant reference, for each branch point in the structure based on instruction of defect classes and feature data respectively associated therewith beforehand. The process includes a priority order specification process for previously specifying target classification performance of purity and accuracy for each defect class, whole and in worst case, with priority order, and an evaluation process for evaluating whether the specified target classification performance under the branching condition is satisfied and displaying a result of evaluation, every item.

Defect classification method and apparatus, and defect inspection apparatus

An inspecting method and apparatus for inspecting a substrate surface includes illuminating a light to the substrate surface having a film, detection of a scattered light or reflected light from a plurality of positions of the substrate surface to obtain a plurality of electrical signals, comparison of the plurality of electrical signals and a database which indicates a relationship between the electrical signals and surface roughness, and calculation of a surface roughness value based on the result of comparison.

Inspecting method and inspecting apparatus for substrate surface

When size of a defect on an increasingly miniaturized pattern is obtained by defect inspection apparatus in the related art, a value is inconveniently given, which is different from a measured value of the same defect by SEM. Thus, a dimension value of a defect detected by defect inspection apparatus needs to be accurately calculated to be approximated to a value measured by SEM. To this end, size of the defect detected by the defect inspection apparatus is corrected depending on feature quantity or type of the defect, thereby defect size can be accurately calculated.

Apparatus of inspecting defect in semiconductor and method of the same

Provided are a method which permits complete training data and data with added errors, and enables the early and accurate discovery of errors in facilities such as a plant, and a system thereof. To achieve the objectives, (1) the behavior of temporal data is observed over time, and the trace is divided into clusters; (2) the divided cluster groups are modeled in sub spaces, and the discrepancy values are calculated as errors candidates; (3) the training data are used (compare, reference, etc.) for reference to determine the state transitions caused by the changes over time, the environmental changes, the maintenance (parts replacement), and the operation states; and (4) the modeling is a sub space method such as regression analysis or projection distance method of every N data removing N data items, (N=0, 1, 2, . . . ) (for example, when N=1, one error data item is considered to have been added, this data is removed, then the modeling is performed), or a local sub space method. Linear fitting in regression analysis is equivalent to the lowest order regression analysis.

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Chigasaki, Japan

Hisae Shibuya