摘要:
Provided is a system by which a loading machine (202) guides a hauling machine (201) on the weight and the position of materials loaded on the hauling machine (202). The system includes: loaded state detection means (5, 6) for detecting the weight “p” and position “x” of materials already loaded on the hauling machine; hauling basic information storage means (7) for storing hauling basic information concerning the loaded state of the hauling machine; loading performance information storage means (11) for storing the loading performance information concerning the loading capacity of the loading machine; next operation calculation means (10) for calculating the next loading weight and the next loading position at which the loading machine should load materials in the next operation based on the hauling basic information, the loading performance information, and the weight and the position of materials on the hauling machine; and a display unit (12) that guides the operator of the loading machine on the next loading weight and the next loading position. This system enables the loading machine to load materials onto the hauling machine with high working efficiency.
摘要:
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.
摘要:
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.
摘要:
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. The state diagnostic device 202 also uses the intermediate state data 103a to make a fault determination in the determined transient operating state. The intermediate state data 103a also includes intermediate point information.
摘要:
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. The state diagnostic device 202 also uses the intermediate state data 103a to make a fault determination in the determined transient operating state. The intermediate state data 103a also includes intermediate point information.
摘要:
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.
摘要:
A hydraulic shovel (1) constituted by a plurality of components includes: a storage unit (20) which stores an operation history of a target component included in the plurality of components and 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 (10) which performs a process of calculating an operating time of the target component in each of the usage modes in accordance with the operation history of the target component and the discrimination threshold value, and a process of estimating the life span of the target component based on the operating time of the target component calculated for each of the usage modes in the aforementioned process and on the usage mode-specific estimated life span. This makes it possible to improve the accuracy in estimating the life spans of components constituting an operating machine.
摘要:
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.
摘要:
Conditional base maintenance has been gaining widespread acceptance, with numerous sensors attached to equipment for constant monitoring of its operational state, the resulting sensor data being compared with those about the equipment in the normal state for a diagnosis to determine whether the equipment is currently operating normally, the result of the diagnosis being used to conduct maintenance. Conditional base maintenance can rapidly detect aging deterioration of the equipment, so that abnormal sates that were not detected before in time base maintenance can now be detected. However, although conventional diagnosis technology can distinguish between the normal state and anomaly, it has been difficult with such technology to identify causes including abnormal phenomena and parts. 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.
摘要:
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. Besides, the diagnostic device on the server side compares the diagnostic results and updates a diagnostic process of the diagnostic device on the machine side 2 when the diagnostic results are different as a result of the comparison.