Hitachi, Ltd.

Hitachi Construction Machinery Co., Ltd.

Shinya Yuda

Hideaki Suzuki

Kozo Nakamura

Munetoshi Unuma

Hiroki Uchiyama

Takayuki Uchida

Takashi Saeki

Toshimi Yokota

Junsuke Fujiwara

Shunsuke Minami

Ryosuke Shigemi

Kenji Araki

Yoshinori Furuno

Norito Watanabe

Shigetoshi Sakimura

Ryota Arai

Hiroyuki Nemoto

Satoshi Kikuchi

Kohji Maki

Katsuaki Tanaka

Takashi Nakahara

Hirotaka Takahashi

Tomoaki Hiruta

Takanori Aono

Hiroshi Ogura

Hiroaki Kojima

Hisashi Endo

Mitsuo Aihara

Teruo Nakamura

Nobuyoshi Hirowatari

Yoshitaka Seki

Kouji Akita

Jyunsuke Fujiwara

Reika Kin

Kesaaki Minemura

Kiyomi Abe

Tetsu Inahara

Yuuji Kanbayashi

To associate condition information, such as (a) a location in a movement section at which fatigue occurred, (b) a type of work or operation during which fatigue occurred, and (c) a type of natural phenomenon experienced when fatigue occurred, a device for displaying the material fatigue of a machine is configured to observe stress variation in a time series using a sensor correlated with the stress variation or stress of a machine. Stress variation in a single cycle and the time when the stress variation occurred are detected, and the stress variation value and time of occurrence are output and displayed.

Method and device displaying material fatigue of machine

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

A degradation cause estimation device is provided with a degradation detector for detecting the amount of degradation undergone by a device, a state observation device for detecting observation values for internal portions, values observed from outside, or device control and operation information, a degradation section detector for detecting a section undergoing degradation using the output values of the degradation detector, and a cause estimator for estimating the cause of the degradation using the device state observations for the degradation section, and outputs the cause for the degradation section.

Device degradation cause estimation method and device

The maintenance management deviceincludes an error status diagnosis unit, a grace period estimation unit, a maintenance cost estimation unit, and a screen display unit. When the error status diagnosis unithas diagnosed the occurrence of an anomaly, the grace period estimation unitestimates a first grace period leading up to the occurrence of a failure. The maintenance cost estimation unitestimates the cost of corrective maintenance incurred when the failure occurs after the elapse of the first grace period. The screen display unitdisplays a combination of the first grace period and the cost of corrective maintenance. Alternatively, the grace period estimation unitestimates a second grace period over which preventive maintenance may be postponed without a failure.

Maintenance management device for operating machinery

The device for searching for similar breakdown cases according to the present invention includes: a first means that detects a signal abnormality from a repair target machine, calculates the correlations between the signal abnormality and past abnormality cases by means of quantification, and regards the past abnormality cases as similar abnormality cases; a second means that obtains the component repair histories of the repair target machine in the similar abnormality cases by means of quantification; and a third means that, in accordance with the quantified correlations and the quantified component repair histories, determines and provides the priority levels of the plural past similar abnormality cases.

Device for searching and method for searching for similar breakdown cases

A learning unit in learning mode generates a cluster from a cluster analysis of data formed from frequency constituent data and state data, obtained from a sensor unit. An abnormality calculation unit computes, as abnormalities, the minimum values among distances to surfaces of the clusters of the data formed from the frequency constituent data and the state data, obtained when in predictive fault indicator sensing mode. A predictive fault indicator determination unit determines a predictive fault indicator of an X-ray tube by comparing the abnormalities with a predetermined threshold.

X-ray tube predictive fault indicator sensing device, X-ray tube predictive fault indicator sensing method, and X-ray imaging device

Provided is a deterioration diagnosis system which diagnoses deterioration of an N-phase rotational machine (N denotes a natural number). The deterioration diagnosis system includes a first current sensor to be attached individually to at least lead wires of (N-)-phases in a rotational machine, the first current sensor being able to detect a current amplitude arising from a plurality of deterioration causes, and a second current sensor to be attached collectively to the lead wires of all phases in the rotational machine, the second current sensor being able to detect a current amplitude arising from a plurality of deterioration causes.

Deterioration diagnosis system

An object of the present invention is to provide an abnormality diagnostic system that can enhance diagnostic precision even if a computer arranged on the machine side does not have sufficient throughput in diagnosing a condition of a machine or equipment based upon time series data generated by a sensor and can reduce communication capacity because communication data volume decreases and industrial machinery provided with the abnormality diagnostic system. A diagnostic device on the machine sidediagnoses time series data generated by a sensor, acquires a primary diagnostic result, extracts time series data related to the primary diagnostic result and outputs it to a diagnostic device on the server sidetogether with the primary diagnostic result, the diagnostic device on the server sidediagnoses the time series data, acquires a secondary diagnostic result, and displays the secondary diagnostic result together with the primary diagnostic result. Besides, the diagnostic device on the server side compares the diagnostic results and updates a diagnostic process of the diagnostic device on the machine sidewhen the diagnostic results are different as a result of the comparison.

Abnormality diagnostic system and industrial machinery

A hydraulic shovel includes: a storage unit which stores an operation history of a target component of the shovel targeted for life span estimation, a discrimination threshold value used for classifying usage of the target component into a plurality of usage modes based on the operation history, and a usage mode-specific estimated life span indicative of an estimated life span of the target component in each of the usage modes; and an arithmetic and control unit which calculates an operating time of the target component in each of the usage modes in accordance with the operation history and the discrimination threshold value, and estimates the life span of the target component based on the operating time calculated for each of the usage modes and on the usage mode-specific estimated life span.

Operating machine and maintenance and inspection information generating apparatus

The invention is related to a system and a method to determine whether a target equipment deviates from a normal state. If it is determined that the target equipment to be diagnosed deviates from the normal state, the degree of deviation of each parameter from the normal state as the reference is calculated as an abnormal contribution ratio. A failure cause is estimated from a similarity ratio between the calculated abnormal contribution ratio and the abnormal contribution ratio of each of the failure causes collected in the past and including failure phenomena and failure parts.

Failure cause diagnosis system and method

A learning diagnostic system for a working machine is capable of making a fault diagnosis of the working machine universally relative to various types of sensor information, and preventing a failure in the working machine by enabling a fault diagnosis even in a transient operating state that represents a transitional state between operating states. A state learning devicesorts inputted sensor datainto one under a steady operating state and one under a transient operating state, and generates, through learning, steady state dataand intermediate state data, each including a permissible error. A state diagnostic deviceuses the steady state datato determine whether an operating state of the working machine related to the inputted sensor data is the steady operating state or the transient operating state, and make a fault determination in the steady operating state. The state diagnostic devicealso uses the intermediate state datato make a fault determination in the determined transient operating state. The intermediate state dataalso includes intermediate point information.

Learning diagnostic system, state diagnostic device, and state learning device for working machine

A maintenance operation instance collection apparatus includes: a storage unit that includes a maintenance operation instance database in which operation information obtained from a device via a sensor and maintenance information on a measure to deal with the operation information corresponding thereto are stored in association with each other; and a control unit that receives an input of new operation information, receives an input of new maintenance information, searches the maintenance operation instance database using the newly-received maintenance information as a search key, acquires searched operation information, compares the newly-received operation information to the acquired operation information, determines whether or not the newly-received operation information is close to the acquired operation information in such a degree of satisfying a prescribed criterion, and, if the newly-received operation information is not determined to be close to the acquired operation information, prompts a re-input of the newly-received maintenance information.

Maintenance operation instance collection apparatus, maintenance operation instance collection method, and maintenance operation instance collection program

Monitoring and diagnosing device including: a classification information storage section; frequency information storage section: a first data classifier section reading out reference classification information from the classification information storage section, comparing operational data, detected by a plurality of sensors and inputted in time sequence, with the reference classification information to classify the operational data, and then generating operational data classification information; a frequency comparator section compiling the operational data classification information, generating operational data frequency information by adding, to the operational data classification information, appearance frequency information for each classification of operational data, reading out reference frequency information from the frequency information storage section, and then generating operational data frequency comparison information by comparing operational data frequency information with the reference frequency information; and an abnormality diagnosing section performing an abnormality diagnosis upon the working machine by use of the operational data classification information and operational data frequency comparison information.

Monitoring and diagnosing device for working machine

In the cabling work aiding system, the RFID reader reads the RFID attached to at least one of the terminal, its terminal block, and cable member. A cabling work terminal device is connected to the RFID reader. The device is comprised of: a data base that records a template connection diagram containing the terminal-side RFID region constituted by RFID data as to the terminal and a cable-side RFID region constituted by RFID data as to the cable member; a relating part that relates the RFID read by the RFID reader to the terminal-side RIFD region and the cable-side RIFD region defined in the template connection diagram; a terminal-cable member correspondence table preparing part that prepares a correspondence table for leading correspondence between the RFID data as to the terminal and RFID data as to cable member from correspondence between the terminal-side RIFD region and the cable-side RFID related by the RFID read by the RFID reader.

Cabling work aiding system and work aiding method

A RFID registration system is provided with a reader for reading an ID of an object to be identified by an RFID, an RFID existence position indicating the position of the RFID, and the RFID; an associating apparatus for associating the read RFID with the ID of the object to be identified by the RFID, a position information generation apparatus for obtaining the read position at the same time the position has been read, and a collation apparatus for obtaining an ID of the object to be identified by the RFID based on the read position and the RFID existence position. The RFID registration system can be avoid making the mistaken association between the RFID and the object to which the identification ID is attached.

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Hitachi, Japan

Shinya Yuda