A method of performing fault isolation for an aircraft comprises identifying a fault that occurs during a flight of the aircraft; identifying a first set of parameters associated with the aircraft based on the identification of the fault; automatically determining values of the first set of parameters to obtain a first set of measured values; determining whether the first set of measured values are within acceptable ranges; and identifying a source of the fault based on the determination of whether the first set of measured values are within the acceptable ranges.

Systems, methods, and processor-readable storage media for AI-assisted electrical power grid fault analysis predict the cause of a fault and cause the fault to be remedied by receiving an indication that a first fault has occurred, identifying a plurality of additional fault records associated with the first fault, obtaining a first prediction of the cause of the fault based on the first fault record and the plurality of fault records by applying the fault records to a machine learning model, obtaining a second prediction of the cause of the fault by applying the fault records to a rules-based model, and obtaining a final prediction of the cause of the first fault based on the first prediction and the second prediction. The final prediction of the cause of the first fault is used to cause the predicted cause of the first fault to be remedied.

An operation plan creation device includes: an operation information creation unit configured to receive an input of a simple setting including information with which an operation period of a device is identifiable and to create operation information including an inspection period and a replacement scheduled component in the operation period of the device; a component information acquisition unit configured to acquire component information including lifespan information and stock quantity information about each of the components, based on use history information about each of the plurality of components; a dummy information creation unit configured to create, when the acquired component information includes missing information, dummy information to be used instead of the missing information; and a plan creation unit configured to create the operation plan for the components related to the device based on the operation information and the component information or the dummy information.

An operation plan creation device includes: an operation information creation unit configured to receive an input of a simple setting including information with which an operation period of a device is identifiable, information with which an inspection interval is identifiable, and an inspection pattern with which a component to be replaced in an inspection is defined in advance and to create operation information including an inspection period and a replacement scheduled component in the operation period of the device; a component information acquisition unit configured to acquire component information including lifespan information and stock quantity information about each of the components, based on use history information about each of the plurality of components; a dummy information creation unit configured to create, when the acquired component information includes missing information, dummy information to be used instead of the missing information; and a plan creation unit configured to create the operation plan for the components related to the device based on the operation information and the component information or the dummy information.

A machine failure analyzing system includes a wearable electronic device is for a user to wear, a controlling and processing device provided with a machine history database and a failure causes analyzing unit. When a specific machine is malfunctioning or in a failure status, the controlling and processing device utilizes a machine status data collecting unit to collect machine status data from the specific machine. Subsequently, based on the machine status data, the failure causes analyzing unit is configured to find relative failure causes from the machine history database, thereby generating at least one troubleshooting solution. As such, under instructions of the troubleshooting solution, a field engineer who wears the wearable electronic device can achieve troubleshooting of the specific machine rapidly and precisely, without needing to spend time finding the failure causes.

A method of performing fault isolation for an aircraft comprises identifying a fault that occurs during a flight of the aircraft; identifying a first set of parameters associated with the aircraft based on the identification of the fault; automatically determining values of the first set of parameters to obtain a first set of measured values; determining whether the first set of measured values are within acceptable ranges; and identifying a source of the fault based on the determination of whether the first set of measured values are within the acceptable ranges.

A control system (1) according to the present invention comprises: a control device (100) that monitors the operation of a plurality of moving parts for machining a workpiece (155), and controls the operation of the plurality of moving parts in each control cycle by issuing command values to the plurality of moving parts; and an inspection device (200) for inspecting the workpiece (155). The control device (100) comprises: an identification unit (160) for identifying, based on inspection results of the inspection device (200) and the command values issued to the plurality of moving parts, which moving part from among the plurality of moving parts has caused an abnormality in the inspection results; and a storage unit (170) for collecting and storing data on the moving part that has been identified by the identification unit (160) and caused the abnormality in the inspection results.

A computer-based method for automated hazard detection for a technical system, the method includes the step of selecting an output failure mode of a component of a component fault tree of the technical system as a top level hazard. The computer-based method includes the step of generating a hazard information tree by means of a subtree of the component fault tree, wherein the subtree is selected by means of the top level hazard, wherein the subtree is modified by removing predefined nodes from the subtree and by enhancing output failure modes of the subtree with information from which component the output failure modes originate, wherein the modified subtree is saved in a memory unit as hazard information tree. The computer-based method includes the step of evaluating the hazard information tree, and providing a control signal comprising a result of the evaluation.

The present invention provides a method, a program, and a device that can accurately learn a causal relationship in a deterministic system and a system that performs anomaly analysis by using the causal relationship. A causal relationship learning device according to one example embodiment of the present invention includes: a correlation determination unit that determines a correlation between measurement values measured by two sensors; and a low correlation causal relationship estimation unit that, when the correlation is lower than a predetermined reference, determines a causal relationship between the two sensors by estimating one of the measurement values which is a cause from the other of the measurement values which is a result.

There is provided a design support system including a designer terminal, a maintenance personnel terminal, and a design support device, in which the design support device includes a failure causal model database, a failure causal model updating unit that updates the failure causal model based on an assumed cause of a failure of the input target equipment, a maintenance support unit that estimates a type of a failure having a high occurrence probability and the cause thereof based on a symptom of the input target equipment and the failure causal model, and outputs the estimated result to the maintenance personnel terminal, a maintenance record database that stores authenticity information input from the maintenance personnel terminal for the estimated type of the failure and the cause, and a failure cause analysis unit that updates the failure causal model based on the authenticity information stored in the maintenance record database, estimates a failure cause of the target equipment based on the updated failure causal model, and outputs the estimated result to the designer terminal.