Hitachi, Ltd.

Clarion Co

Hitachi Automotive Systems, Ltd.

Hitachi Construction Machinery Co., Ltd.

Xanavi Informatics Corporation

Hitachi-high-technologies Corporation

Tomoaki Hiruta

Mariko Okude

Masatoshi Kumagai

Takayuki Uchida

Hideaki Suzuki

Koichiro Tanikoshi

Toshiaki Kono

Takayoshi Yokota

Mikio Bando

Junsuke Fujiwara

Toshiyuki Aoki

Shinichi Amaya

Yasuharu Namba

Kazuhiro Okada

Kohji Maki

Munetoshi Unuma

Hiroyuki Kohida

Tetsuji Kato

Kenji Suzuki

Noriyuki Abe

Chetan Kumar Gupta

Toshio Masuda

Yoshinori Endo

Takeshi Shima

Manabu Kato

Jun Yamazaki

Masayoshi Ishikawa

Koichi Kato

Shinya Yuda

Takumi Fushiki

Mitsuo Aihara

Akihiro Kawabata

Zhixing Liu

Hirofumi Komine

Jyunsuke Fujiwara

Akihiro Komasu

Yoshitaka Atarshi

Ken Kikegawa

A condition monitoring system which collects operation data from a machine and monitors a condition of the machine includes: a storage unit that stores information indicating components of a first machine for which a model for a sensor data analysis has been created and components of a second machine for which the model is newly created, information indicating a correspondence relationship between the components of the first machine and the components of the second machine, and information relating to the model; a model creation unit that creates model candidates of the second machine from the model similar to the second machine by using the information stored in the storage unit, and creating information relating to a model candidate selected via an input unit out of the model candidates as the model of the second machine; and a display unit that displays the model candidates.

Condition monitoring system

A fault finding support system is a fault finding support system for instructing a maintenance worker of an appropriate examination location and examination contents using fault knowledge data in which a causal relationship of fault of a target machine is described, the system including: an examination procedure creation unit that creates an examination procedure with respect to the examination location and the examination contents of the target machine using the fault knowledge data, an examination result storage unit that stores an examination history of performing examination using the examination procedure created by the examination procedure creation unit, an update target extraction unit that recommends an update location of the fault knowledge data using the examination history stored in the examination result storage unit, and a user interface that provides a function of displaying a location extracted by the update target extraction unit and updating the fault knowledge data.

Fault finding support system and method

The invention provides a maintenance recommendation system in which an inspection item is presented timely in the halfway of an inspection, accuracy of failure mode identification is improved, a failure mode is identified at an early stage, meanwhile, a time required for investigating a content of the failure is reduced, and a time from device failure to reset is shortened. The maintenance recommendation system includes: a primary storage unit that stores an input inspection result; a failure mode probability calculation unit that is configured to calculate a probability of a failure mode based on the inspection result stored in the primary storage unit; an inspection item search unit that is configured to extract an inspection item with the minimum inspection score from uninspected inspection items; and a main routine operation unit that is configured to narrow down a failure mode candidate and an inspection item candidate from all inspection items.

Maintenance recommendation system

There is provided a failure mode specifying system or the like that suitably specifies a failure mode of an apparatus. A failure mode specifying systemincludes a communication portionthat obtains data including a detection value of a sensorwhich is installed in an apparatus, an apparatus structural parameter estimating portionthat estimates a predetermined apparatus structural parameter based on a structure and properties of the apparatus, based on the data which is obtained by the communication portion, a failure mode specifying portionthat specifies a failure mode indicating a kind of failure or failure sign of the apparatus, based on the apparatus structural parameter, and an input-output portionthat presents the failure mode which is specified by the failure mode specifying portion

Failure mode specifying system, failure mode specifying method, and program

In some examples, a computing device may generate simulated data based on a physical model of equipment. For example, the simulated data may include a plurality of probability distributions of a plurality of degrees of failure, respectively, for at least one failure mode of the equipment. In addition, the computing device may receive sensor data indicating a measured metric of the equipment. The computing device may compare the received sensor data with the simulated data to determine a failure mode and a degree of failure of the equipment. At least partially based on the determined failure mode and degree of failure of the equipment, the computing device may send at least one of a notification or a control signal.

Equipment control based on failure determination

An analysis device for time series data from an apparatus to be diagnosed is provided with an accumulation device which accumulates sensor data, operation data, or control data, obtained from the apparatus, while accumulating time information, an algorithm accumulation unit which accumulates algorithms for recognizing behavior of the apparatus, a behavior recognition unit which recognizes behavior of the apparatus by using a recognition algorithm, and a specification unit which specifies a behavioral item to be recognized. A behavior recognition algorithm corresponding to the specified behavioral item is selected from the algorithm accumulation unit; sensor data, operation data, or control data is selected from the accumulation device; start and end times of a selected behavior are recognized by the behavior recognition unit; and the recognized start and end times are associated with time information about data accumulated in the accumulation device.

Analysis method for time series data and device therefor

An operation mode specifying unit specifies an operation mode of a machine by comparing time-series data of an amplitude and a frequency of measurement data obtained from a sensor with definition data of the operation mode of the machine created in advance by an operation mode data creation unit. In addition, an abnormality diagnosis unit performs processing of cluster analysis for the measurement data obtained from the sensor or the like, and diagnoses abnormality of the machine according to diagnosis procedure information that is set in advance depending on the set operation mode and an abnormality mode of the machine.

Machine diagnostic apparatus and machine diagnostic method

The operation data collection device for work machines includes a plurality of sensors attached to a work machine to receive measurement data as operation data and record the operation data in an operation data storage section. The operation data collection device further includes: a communication processing section that transmits the operation data stored in the operation data storage section to an external server in response to a download request from the external server; an access history management section that manages a download status of the operation data recorded in the operation data storage section, through the download request from the external server and the transmission of the operation data to the external server; and a recorded data change processing section that changes a condition for recording the operation data in the operation data storage section in accordance with the download status of the operation data.

Operation data collection device for work machines

In industrial machine abnormality diagnosis, if the machine is diagnosed to have abnormality, then sensor data from the machine needs to be sent to a management center for causal analysis. However, since machines operated at a remote site cannot always communicate with a management center, it has been found that, in some cases, sensor data that has failed to be sent from a machine remains in the memory of the machine, resulting in lack of available memory capacity. In view of this, the present invention determines beforehand whether the diagnosed machine will run out of available memory capacity before the completion of sending the amount of sensor data required for causal analysis for the machine, and instructs a maintenance person to recover memory. This determination as to whether the machine will run out of available memory capacity before the completion of sending the amount of sensor data required for the causal analysis for the machine, is made as follows: (1) first, the machine predicts the run-out date on which the machine will run out of memory capacity for storing sensor data generated in the machine, and sends a notification of the predicted run-out date to the management center for the machine; and (2) next, from the amount of sensor data required for the causal analysis and the reception rate of sensor data, the management center calculates the number of days required to retrieve the necessary data for the causal analysis and determines whether the management center can retrieve the data by the predicted run-out date.

Abnormality diagnosis method and device therefor

A device for calculating one or more camera parameters of a camera mounted on a traveling vehicle includes a location information acquisition unit, a road width information acquisition unit, a scenery image acquisition unit, and a camera parameter calibration unit. The device calculates a height of a mount position of the camera and a direction of the camera. The device also converts a scenery image captured by the camera of a scenery including the road on which the traveling vehicle moves into an image that is displayed for navigation.

Camera parameter calculation device, navigation system and camera parameter calculation method

An anomaly diagnosis system is provided that includes: a current-value obtaining partthat obtains a value of an AC current inputted to or outputted from a device to be diagnosed; a frequency analyzing partthat converts the current values obtained at intervals into a frequency spectrum which correlates a current intensity with a frequency; an anomaly-judging-value calculating partthat calculates at intervals a frequency width of the frequency spectrum at a current intensity 'As' satisfying As=m&#xd7;At (0&#x3c;m&#x3c;1), where 'At' is a peak current intensity associated with an analysis frequency which is an integer multiple of a frequency of the AC current; and a judging partthat judges whether the frequency width is wider than that in a normal condition and, if it holds true, judges that the device is in an anomalous condition.

Anomaly diagnosis system, method, and apparatus

Selection of sensors requires the knowledge of individual sensors mounted on a target machine and the knowledge of mechanical features of the machine. In the past, experts having mechanical knowledge selected the sensors. The present invention involves analyzing sensor data at e of machine failure, extracting features from the sensor data regarding each failure cause, and comparing the extracted features so as to clarify the difference between the sensor data about the failure causes and present an operator with the clarified difference thereby to support sensor selection. The invention makes it possible to select more simply than before the sensors considered effective for classifying failure causes. For example, the sensors deemed effective for classifying failure causes without mechanical knowledge can be selected.

Failure cause classification apparatus

A condition monitoring system includes a terminal system unit and a condition monitoring system unit connected together via radiocommunication channels. The terminal system unit is mounted on a self-propelled machine, while the monitoring system unit is arranged at a control center. The monitoring system unit is provided with a unit for setting an alarm delivery condition, another unit for setting an aggregation condition, a further unit for transmitting the alarm delivery condition and aggregation condition so set, and a screen display unit for displaying various information. The terminal system unit is provided with an alarm creation unit for creating alarm information according to the alarm delivery condition so transmitted, an aggregate data creation unit for creating aggregate data according to the transmitted aggregation condition, and a data transmitting unit for transmitting the alarm information and aggregate data so created.

Condition monitoring system, condition monitoring system unit, and terminal system unit

A route calculation system includes: a route selection probability calculation unit that calculates a route selection probability of each route; a weighting parameter acquisition unit that acquires at least two kinds of weighting parameters representing degrees of influences of at least a route traveling distance and a route traveling time on the route selection probability; a weighting parameter selection unit that selects at least two kinds of weighting parameters corresponding to a specified set of a starting point and ending point combination related to a combination of a departure point and a destination; and a recommended route calculation unit that calculates a recommended route based on at least two kinds of link costs including a link traveling distance and a link traveling time and based on the at least two kinds of weighting parameters corresponding to the specified set of the starting point and ending point combination.

Route calculation system, navigation device, and route calculation method

Because a route selection model for traffic demand prediction of selecting a route from among travel routes in probe data cannot include other routes than the travel route in the probe data, it is not possible to predict a traffic volume on the route that has not been traveled. A traffic-volume prediction device includes a simple-network creation device that makes a simple network connecting principal intersections extracted from travelling loci in the collected probe data, a model creation device that determines a utility between the principal intersections based on the data of the travelling loci and calculates a selection probability for each principal intersection based on the utility, and a traffic-volume assignment device that distributes a traffic volume to the routes between the principal intersections according to the selection probability and uses the traffic volume for each route candidate to be a traffic demand prediction object.

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Tokyo, Japan

Tomoaki Hiruta