Personal dependency related to fault tree construction is reduced, and the reliability of an operating machine is improved by improving the accuracy of a fault diagnosis. The present invention provides a fault diagnosis device for a machine in operation, the device comprising: an abnormality degree analysis unit that calculates the abnormality degree of each component configuring the machine by comparing input/output data of the machine with a threshold value; a fault tree automatic generation unit that holds a fault tree of each component in which the fault of each component and the fault of a sensor in each component are associated with each other and generates the fault tree of the entire machine by coupling the fault trees of the components on the basis of a correlation between the input/output data of each component; a fault analysis unit that analyzes the fault of the machine on the basis of the abnormality degree and information of the fault tree of the entire machine; and a display unit that displays information analyzed by the fault analysis unit and issues an alarm.

Disclosures of the present invention describe a machine failure analyzing system. In the machine failure analyzing system, a wearable electronic device is for a user to wear, and a controlling and processing device is provided with a machine history data base 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 can find relative failure causes from the machine history data base, 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 the troubleshooting of the specific machine rapidly and precisely, without needing to spend time finding the failure causes.

A method and apparatus for data analysis according to various aspects of the present invention is configured to test a set of components and generate test data for the components. A diagnostic system automatically analyzes the test data to identify a characteristic of a component fabrication process by recognizing a pattern in the test data and classifying the pattern using a neural network.

A method for an automated aircraft landing analysis including: receiving one or more aircraft landing performance parameters for one or more landing phases; determining a landing performance deviation for each of the one or more landing phases in response to the one or more aircraft landing performance parameters; identifying at least one of a system fault, a failure, and a pilot error that could have led to the landing performance deviations for each of the one or more landing phases; developing a fault tree for the landing performance deviations for each of the one or more landing phases; identifying measurable parameters, calculable parameters, inferable parameters, or observable parameters within the fault tree; converting the fault tree into a high level reasoning model using a standard inference methodology; performing a root cause analysis; identifying a root cause of the landing performance deviation; and displaying the root cause of landing performance deviation.

A method for the automatic process monitoring and/or process diagnosis of a piece-based process, in particular a production process, in particular an injection-molding process, including the steps: a) performing an automated reference finding in order to obtain reference values (r.sub.1 . . . r.sub.n) from values (x.sub.0 . . . x.sub.j) of at least one process variable; b) performing an anomaly detection on the basis of the reference values (r.sub.1 . . . r.sub.n) found in step (a); c) performing an automated cause analysis and/or an automated fault diagnosis on the basis of a qualitative model of process relationships and/or on the basis of dependencies of various process variables on each other.

A system controls and monitors a heating, ventilation, and air conditioning (HVAC) system. The system receives raw data from HVAC equipment, controller performance monitoring (CPM) indicators associated with the HVAC equipment, and a set of rules associated with the HVAC equipment. The system processes the CPM indicators and the raw data using the set of rules to generate fault relevancies, and processes the fault relevancies.

A method of diagnosing a root cause for an exhibited vehicle failure, comprises initiating a vehicle health management (VHM) algorithm, repeatedly monitoring, at a specified time interval, the state of health (SOH) for at least one vehicle component, wherein the SOH for the at least one vehicle component is one of Green (normal operation), Yellow, and Red, calculating a number of consecutive Green SOH results for the at least one vehicle component, and providing an indication of likelihood that the at least one vehicle component is not a root cause of the exhibited vehicle failure based on the number of consecutive Green SOH results for the at least one vehicle component.

A method and system for determining a cause of error in an agricultural working machine with a faulty component is disclosed. An analysis routine may analyze various types of data, such as operating data of the agricultural working machine, including workload data of the agricultural working machine, and/or design data of the agricultural working machine, such as design data of the faulty component of the agricultural working machine, in order to determine a cause of error, as indicated by cause of error data. Specifically, the analysis routine may analyze one or both of operating data and design data in order to determine whether a workload of the agricultural working machine and/or a design of the agricultural working machine, such as the design of the faulty component of the agricultural working machine, can be excluded and/or identified as the cause of error.

Embodiments herein describe a fault tolerant network connected orchestrator which can handle network outages or hardware resets in a work cell. In one embodiment, the orchestrator determines the next task to assign to the work cell depending on whether the previous task was successfully completed. However, a network outage or a hardware failure may prevent the orchestrator from receiving the results of the previous action from the work cell. In one embodiment, the orchestrator recovers from a communication error by requesting the current state of sensors. Using this information, the orchestrator can deduce or determine the current state of the work cell and determine the next task for the work cell. In this manner, the orchestrator is fault tolerant such that it can recover from communication errors.

A risk assessment device for displaying a risk matrix in which a probability of malfunction and a degree of influence of malfunction are set as two axes includes a malfunction probability acquisition unit configured to acquire, with respect to a target device group, a data group that indicates a temporal change of the probability of malfunction from a current point in time, an influence degree acquisition unit configured to acquire a degree of influence that corresponds to the target device group, and an image data creation unit configured to create image data for displaying a plot diagram that is obtained by plotting, with respect to each probability of malfunction that constitutes the acquired data group, a pair of the probability of malfunction and the acquired degree of influence on the risk matrix.