Provided is a method for the computer-assisted creation of digital rules for monitoring a technical system. In the method, an ontology is used, which contains a plurality of classes including classes of components of the technical system and classes of operating state characteristics of the technical system and contains semantic relations between the classes. By means of a user interface, a user can formulate abstract rules by means of the classes and the semantic relations from the ontology. The abstract rules are converted into concrete rules valid for the specific technical system in an automated manner. The method has the advantage that corresponding rules no longer have to be formulated individually for individual technical systems by the user. Instead, abstract rules only have to be created one time for identical or similar technical systems.

Described herein are systems, methods, and non-transitory computer-readable media for self-detection of a fault condition by a vehicle component, generation of a device health code that includes multiple tiers of information relating to the fault condition experienced by the vehicle component, and broadcasting of the device health code to one or more other vehicle components via one or more vehicle communication networks. A recommended vehicle response measure indicated by a reaction code in the device health code can then be taken or alternate vehicle response may be selected and initiated based on an evaluation of current vehicle operational data.

An asset management method for a substation in accordance with the present invention generates a unique reliability model for each element of the substation by comparing reliability of a reference reliability model for each substation type with a health index of the each element thereof generated based on state data and real-time monitoring data of the each element of the substation and compensating the reference reliability model for the each element of the substation; assessing system reliability index and economic feasibility for each maintenance scenario based on a reference system reliability model for each candidate element subject to maintenance among the elements of the substation; selecting a maintenance scenario as a result of the health index and the unique reliability model for the each element of the substation, the system reliability index, and the economic feasibility and updating the unique reliability model for the each element of the substation by executing maintenance.

Methods and systems for fault identification and mitigation in an engine system. A state observer obtains current state information from the engine system, and a feature calculator uses data obtained from the state observer to calculate one or more feature indicators, which are monitored by a health estimator for the occurrence of a change using one or more change probability models. When the health estimator identifies a change, a fault isolator determines a component of the engine system that is subject to fault or health deterioration.

Systems for reducing reduce downtime include industrial machines, sensor(s) associated therewith, edge agent(s), central controller(s), and display(s). The sensor(s) detect operational parameters (e.g., error condition) of the industrial machines associated therewith. The edge agent(s) receive information (e.g., logging messages) from the industrial machines and/or information from the sensors. The central controller(s). The central controllers receive and transform the information into standardized logging messages that are formatted identically, analyze the standardized logging messages to determine occurrence(s) of a current or anticipated error condition for any of the industrial machines, and transmit an alert and/or notification to the display(s) so users can take corrective action to ensure proper operation of the industrial machines. Such a system can be used in performing a corresponding method for reducing downtime associated with current or anticipated error conditions of such industrial machines.

In an aspect, a test platform for testing an embedded control system having a plurality of interconnected components is operable to: receive, at run time, configuration data for configuring a system under test (“SUT”) representing the embedded control system, the configuration data specifying: which of the components shall be simulated versus hardware-in-the-loop (HIL) components in the SUT; and an inter-component signal mapping that maps input signals to output signals of the specified simulated or HIL components of the SUT; for each of the simulated or HIL components, automatically activate, at run time, a corresponding object code portion for simulating the embedded system component in the test platform or a corresponding object code portion for facilitating communication with the HIL component connected to the test platform, respectively; and automatically map input signals of the activated object code portions to output signals of the activated code portions according to the signal mapping.

A method of remotely configuring one or more building system components at a building site uses a cloud-based server remote from the building site. The cloud-based server receives information from an intelligent gateway at the building site that identifies each of one or more building system components at the building site. The cloud-based server is used to create a site configuration that is based at least in part on the identified information for each of the one or more building system components. The site configuration is then downloaded from the cloud-based server to the intelligent gateway such that the intelligent gateway is able to pass configuration information to one or more local controllers that control operation of the one or more building system components.

A plant control and monitoring device includes a communication card having a first wireless communication chip on which a first unique ID is recorded, a CPU card having a second wireless communication chip on which a second unique ID is recorded, an input card having a third wireless communication chip on which a third unique ID is recorded, and a power supply unit having a fourth wireless communication chip on which a fourth unique ID is recorded. The first wireless communication chip reads out a unique ID from the second wireless communication chip or the fourth wireless communication chip, and transmits a read out unique ID to a maintenance tool through a control network. In a plant control and monitoring system, when the maintenance tool receives a unique ID transmitted through the control network, the maintenance tool collates the received unique ID with an ID database.

A method of remotely configuring one or more building system components at a building site uses a cloud-based server remote from the building site. The cloud-based server receives information from an intelligent gateway at the building site that identifies each of one or more building system components at the building site. The cloud-based server is used to create a site configuration that is based at least in part on the identified information for each of the one or more building system components. The site configuration is then downloaded from the cloud-based server to the intelligent gateway such that the intelligent gateway is able to pass configuration information to one or more local controllers that control operation of the one or more building system components.

The objective of the present invention is to realize an operating state classification system having a classification accuracy that continuously improves through additional learning, and which is appropriately protected from unauthorized duplication of a classification function. In this operating state classification system, in which an edge device and a server are connected by means of a communication network, and which inputs sensor data into a neural network and outputs a state label: the edge device includes a first storage unit which stores an upstream side of the neural network, and a dimensionality reduction unit which inputs the sensor data into the upstream side of the neural network and outputs intermediate data; and the server includes a second storage unit which stores a downstream side of the neural network, an inference executing unit which inputs the intermediate data into the downstream side of the neural network and outputs the state label, and a learning unit which updates the downstream side of the neural network by means of additional learning.