A system and method for conveyor configuration and testing. The system is configured to execute the method, which includes: receive input data relating to configuration of a conveyor system; prepare a simulation of the configured conveyor system; operate the simulation of the conveyor system; determine at least one operational parameter related to the conveyor system to be monitored; monitor the at least one operational parameter during operation of the simulation of the conveyor system; determine if the configuration of the conveyor system needs to be adjusted based on the monitored operational parameter; if the configuration needs to be adjusted, automatically make an adjustment and return to operate the simulation of the conveyor system; and continue the simulation until otherwise terminated. In some cases, the monitoring operational parameters uses a machine learning model based on actual data from operating conveyors.

A diagnostic device includes a storage unit that stores first information including a first abnormal event which occurred in the past in a plant, a first attribution event that is a cause of the first abnormal event, and a first occurrence probability of the first attribution event, in which a causal relationship between the first abnormal and attribution events is indicated by a tree structure, and second information including a second abnormal event which is supposed to occur in the plant but has not yet occurred, a second attribution event that is a cause of the second abnormal event, and a second occurrence probability of the second attribution event, in which a causal relationship between the second abnormal and attribution events is indicated by a tree structure; and an estimation unit that estimates the cause of the sign of the abnormality, based on the first and second information.

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.

Aspects provide for dynamic pathloss mitigation via a cross layer tool chain by simulating a three-dimensional model of a physical environment including an access point, an endpoint running an application, and a passive object; emulating network traffic for the application transmitted between the access point and the endpoint; simulating, in the model, pathways for signals to carry the traffic in a plurality of regions for the physical environment; emulating signal degradation along the pathways in the plurality of regions based on respective locations for the access point, the endpoint, and the passive object in the physical environment; and in response to the signal degradation satisfying a pathloss threshold, outputting a command to the application to affect operations of the endpoint. Additionally, the cross layer tool chain outputs a Graphical User Interface showing a signal degradation map based on the simulated network traffic overlaid on the model.

Provided is a computer-implemented method for generating a Component Fault and Deficiency Tree of a multi-component system the method including: a. modeling the multi-component system using a Component Fault and Deficiency Tree, b. the Component Fault and Deficiency Tree includes a plurality of component fault and deficiency tree elements associated with the respective components; c. each component fault and deficiency tree element includes at least one inport and at least one outport; d. each component fault and deficiency tree element includes at least two events as internal fault tree logic; e. at least one gate, f. each component fault and deficiency tree element includes at least one mitigation logic; g. at least one Boolean AND-Gate, configured to connect the internal fault tree logic and the at least one mitigation logic; and h. providing the generated Component Fault and Deficiency Tree of the multi-component system as output.

Aspects provide for dynamic pathloss mitigation via a cross layer tool chain by simulating a three-dimensional model of a physical environment including an Access Point, an End Point running an application, and a passive object; emulating network traffic for the application transmitted between the Access Point and the End Point; simulating, in the model, pathways for signals to carry the traffic in a plurality of regions for the physical environment; emulating signal degradation along the pathways in the plurality of regions based on respective locations for the Access Point, the End Point, and the passive object in the physical environment; and in response to the signal degradation satisfying a pathloss threshold, outputting a command to the application to affect operations of the End Point. Additionally, the cross layer tool chain outputs a Graphical User Interface showing a signal degradation map based on the simulated network traffic overlaid on the model.

A diagnostic device includes a storage unit that stores first information including a first abnormal event which occurred in the past in a plant, a first attribution event that is a cause of the first abnormal event, and a first occurrence probability of the first attribution event, in which a causal relationship between the first abnormal and attribution events is indicated by a tree structure, and second information including a second abnormal event which is supposed to occur in the plant but does not occur yet, a second attribution event that is a cause of the second abnormal event, and a second occurrence probability of the second attribution event, in which a causal relationship between the second abnormal and attribution events is indicated by a tree structure; and an estimation unit that estimates the cause of the sign of the abnormality, based on the first and second information.

Provided is a computer-implemented method for generating a Component Fault and Deficiency Tree of a multi-component system the method including: a. modeling the multi-component system using a Component Fault and Deficiency Tree, b. the Component Fault and Deficiency Tree includes a plurality of component fault and deficiency tree elements associated with the respective components; c. each component fault and deficiency tree element includes at least one inport and at least one outport; d. each component fault and deficiency tree element includes at least two events as internal fault tree logic; e. at least one gate, f. each component fault and deficiency tree element includes at least one mitigation logic; g. at least one Boolean AND-Gate, configured to connect the internal fault tree logic and the at least one mitigation logic; and h. providing the generated Component Fault and Deficiency Tree of the multi-component system as output.

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.

A system and method for conveyor configuration and testing. The system is configured to execute the method, which includes: receive input data relating to configuration of a conveyor system; prepare a simulation of the configured conveyor system; operate the simulation of the conveyor system; determine at least one operational parameter related to the conveyor system to be monitored; monitor the at least one operational parameter during operation of the simulation of the conveyor system; determine if the configuration of the conveyor system needs to be adjusted based on the monitored operational parameter; if the configuration needs to be adjusted, automatically make an adjustment and return to operate the simulation of the conveyor system; and continue the simulation until otherwise terminated. In some cases, the monitoring operational parameters uses a machine learning model based on actual data from operating conveyors.