Systems, methods, and processing nodes predicting and perform preventive maintenance in a transportation system. Predicting and performing preventive maintenance in a transportation system includes determining historical data for electronic devices in the transportation system. Predicting and performing preventive maintenance also includes determining dependencies of the electronic devices based on the historical data. Predicting and performing preventive maintenance includes determining a likelihood of a fault in the target electronic device during a time period based on the dependencies of the electronic devices and a mutual probability of failure of the target electronic device and parent electronic devices associated with the target electronic device. Predicting and performing preventive maintenance also includes initiating preemptive maintenance on the target electronic device based on the likelihood of the fault.

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.

A computer-based method for generating one or more FTA fault trees from an FMEA table of a technical system or vice versa. The method includes defining a common data set for both the FMEA table and the one or more FTA fault tree(s) of the technical system, obtaining data of the common data set for the technical system, selecting a representation of the technical system as a FMEA table or as one or more FTA fault tree(s), and using the data of the common data for generating and displaying on a graphical user interface the FMEA table of the technical system or one or more FTA fault tree(s) of the technical system, depending on the selected representation.

There is described a zone controller and method for identifying a root cause failure at a zone. The zone controller determines whether a temperature measurement deviates from a temperature setpoint of the temperature sensor, and generates a first repair code, a second repair code, and/or a third repair code. The first repair code replaces a temperature sensor in response to detecting that a reading of the temperature sensor has failed. The second repair code releases an operator override on the reading of the temperature sensor in response to detecting that the reading of the temperature sensor has been overridden. The third repair code releases an operator override on a setpoint of the temperature sensor in response to detecting that the setpoint of the temperature sensor is outside the predetermined setpoint range. One or more of these repair codes are provided to a remote device.

Currently solutions for early detection of failures in manufacturing utilize predefined threshold levels of the process variables associated with equipment in manufacturing unit/industry plants. The pre-defined threshold and levels thereof are compared with the real values obtained from the manufacturing unit to check behavior of process variables (also referred as ‘process parameters’) and thus are prone to error. The present disclosure provides systems and method for predicting early warning of operating mode of equipment operating in industry plants which is based on transforming conditions on process parameters into conditions on corresponding fuzzy indices based on their thresholds. The fuzzy indices (concordance index, discordance index) of individual conditions are combined into a composite fuzzy index (composite index or degree of credibility) that describes the failure scenario in the process parameter space. A fuzzy logic-based detection is useful for detecting a failure mode early and providing alerts to operators for necessary action.

When a fault occurs in the prior art, it is necessary to exhaustively cover combinations of the field, product, and usage environment in which a component is used and create a model for determining priority degree. The person-hours required to create such a model represent a problem. In order to solve this problem, a fault tree generation unit 111 automatically generates a fault tree on the basis of the causal relationship of defects to events to be analyzed, and a score calculation unit 113 additionally calculates a priority degree (score) for the individual events in the generated fault tree by means of the number of co-occurrences of “events” and “event-related information” in past defect information on the basis of “event-related information” in which a component is used, then presents a scored fault tree to which the scores have been applied.

An abnormality diagnosis method for diagnosing an abnormality in equipment includes acquiring multivariate time-series data for a plurality of measurement items from the equipment, diagnosing an abnormality in operational state of the equipment based on the multivariate time-series data, and diagnosing a cause of the abnormality. The diagnosing a cause of the abnormality includes extracting a feature of a first section before the occurrence of the abnormality from the multivariate time-series data of the first section, extracting a feature of a second section after the occurrence of the abnormality from the multivariate time-series data of the second section, obtaining an amount of change in feature from a difference between the feature of the first section and the feature of the second section, and diagnosing a measurement item that is the cause of the abnormality based on the amounts of change in features of the plurality of measurement items.

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 providing an analytical artifact used for development and/or analysis of an investigated technical system of interest comprised of components having associated machine readable functional descriptions including port definitions and component failure modes processed to generate automatically the analytical artifact in response to at least one applied system evaluation criterion.

An abnormality diagnosis device includes an abnormality determination unit configured to determine whether or not there is an abnormality with respect to a state quantity acquired from equipment and a cause estimation unit configured to estimate a cause of the abnormality in the equipment from a state quantity determined to be abnormal by the abnormality determination unit using a cause correspondence table in which a cause of an abnormal mode of the equipment identified in fault tree analysis is associated with the state quantity that is abnormal when the cause has occurred.