Abstract: Provided are: an acquisition unit that acquires physical state information indicating physical states of the people from one or more sensors that detect at least the people; a physical function analysis unit that analyzes a change in physical functions of the people based on a time-series change of the physical state information acquired by the acquisition unit; and a physical function improvement proposing unit that generates and outputs physical function improvement proposal information indicating an improvement proposal of the physical function with respect to the change in the physical functions of the people based on an analysis result of the physical function analysis unit.

Abstract: 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.

Abstract: 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.

Abstract: The maintenance management device 3 includes an error status diagnosis unit 11, a grace period estimation unit 14, a maintenance cost estimation unit 15, and a screen display unit 17. When the error status diagnosis unit 11 has diagnosed the occurrence of an anomaly, the grace period estimation unit 14 estimates a first grace period leading up to the occurrence of a failure. The maintenance cost estimation unit 15 estimates the cost of corrective maintenance incurred when the failure occurs after the elapse of the first grace period. The screen display unit 17 displays a combination of the first grace period and the cost of corrective maintenance. Alternatively, the grace period estimation unit 14 estimates a second grace period over which preventive maintenance may be postponed without a failure.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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-1)-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.

Abstract: 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.

Abstract: 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.

Abstract: 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 side 2 diagnoses 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 side 3 together with the primary diagnostic result, the diagnostic device on the server side 3 diagnoses the time series data, acquires a secondary diagnostic result, and displays the secondary diagnostic result together with the primary diagnostic result.

Abstract: In the conventional technique, network performances of a machine and a server become different due to the diagnosis target machine when a fault symptom diagnosis function is provided to a plurality of different diagnosis target machines. Therefore, the plurality of different machines are not able to be handled using one system. The present invention is a fault symptom diagnosis system including: a diagnosis execution unit; an arrangement unit; a diagnosis target machine; a diagnosis server; and a network, wherein the diagnosis execution unit includes processing modules of a sensor input processing, a preprocessing, a diagnosis processing, and a postprocessing, and a common interface that connects the processing modules, and wherein the arrangement unit arranges and executes the processing modules in the diagnosis target machine or the diagnosis server.

Abstract: An objective of the present invention is 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, with machine fatigue occurring when a machine is moved or placed in a work environment, natural environment, or other condition and to visualize and display the machine fatigue. To achieve this objective, this device for displaying the material fatigue of a machine is characterized by being provided with a means for observing stress variation in a time series using a sensor correlated with the stress variation or stress of a machine, a means for detecting stress variation in a single cycle, and a means for detecting the time when the stress variation occurred, and by outputting or displaying the stress variation value and time of occurrence.

Abstract: Times in association with a portion of data designated by an input device from a graph indicating distribution of data of a certain item are divided into plural groups based on the respective time intervals, data relating to desired items included in time ranges respectively regulated by the plural groups are searched from a storage device, and time waveforms of data relating to the desired items are created for each of the groups based on the search results. With this configuration, around the time of generating a portion of data included in a set of data relating to a certain item, aspects of temporal changes of data of a certain item or other items can be easily recognized.

Abstract: An analysis device for time series data from an apparatus to be diagnosed according to the present invention is characterized by being provided with an accumulation device which accumulates sensor data, operation data, or control data, obtained from the apparatus to be diagnosed, while accumulating time information, an algorithm accumulation unit which accumulates algorithms for recognizing behavior of the apparatus to be diagnosed, a behavior recognition unit which recognizes behavior of the apparatus to be diagnosed by use a recognition algorithm, and a specification unit which specifies a behavioral item to be recognized, wherein: a behavior recognition algorithm corresponding to the specified behavioral item to be recognized is selected from the algorithm accumulation unit; sensor data, operation data, or control data to be used by the behavior recognition algorithm is selected from the accumulation device; the start and end times of a selected behavior are recognized by the behavior recognition unit; and

Abstract: 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.

Abstract: 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-1)-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.

Abstract: 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.

Abstract: The maintenance management device 3 includes an error status diagnosis unit 11, a grace period estimation unit 14, a maintenance cost estimation unit 15, and a screen display unit 17. When the error status diagnosis unit 11 has diagnosed the occurrence of an anomaly, the grace period estimation unit 14 estimates a first grace period leading up to the occurrence of a failure. The maintenance cost estimation unit 15 estimates the cost of corrective maintenance incurred when the failure occurs after the elapse of the first grace period. The screen display unit 17 displays a combination of the first grace period and the cost of corrective maintenance. Alternatively, the grace period estimation unit 14 estimates a second grace period over which preventive maintenance may be postponed without a failure.

Abstract: 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 device 201 sorts inputted sensor data 101a into one under a steady operating state and one under a transient operating state, and generates, through learning, steady state data 102a and intermediate state data 103a, each including a permissible error. A state diagnostic device 202 uses the steady state data 102a to 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.