An apparatus and method for predicting distress on a physical component. The method can include obtaining distress data. The distress data can be used to determine a distress rank. The distress rank can be compared to a distress output provided by a kernel that use parameters related to the physical component. The comparison can result in a prediction model for the physical component.

Examples relate to a method includes monitoring a set of parameters. The set of parameters are associated with a first set of computing components and a second set of computing components. The first set of computing components is located in a first region and the second set of computing components is located in a second region. The first region is positioned proximate a generation station control system associated with a generation station and the second region is positioned remotely from the generation station control system. Each computing system of the second set of computing systems is configured to adjust power consumption during operation. The method also includes adjusting power consumption at one or more computing components of the second set of computing components based on the set of parameters.

Described herein are systems and methods of dynamically displaying a network of alarms. This can comprise establishing a first hierarchy of a plurality of alarms in configuration an alarm server, the plurality of alarms comprising the network of alarms; receiving a state of the alarms over a network, wherein the state of the alarms are received from one or more Object Linking and Embedding (OLE) for Process Control (OPC) Unified Architecture (UA) clients through a standard interface of an Object Linking and Embedding for Process Control (OPC) Alarms and Events (OPC AE) protocol, communicating with the alarm server; dynamically changing the first hierarchy of the alarms based on the state of the alarms to obtain a second hierarchy of the alarms; and presenting, on a display in communication with the alarm server, a second list of alarms to an operator based on the second hierarchy.

The energy storage system operating apparatus and method includes a pre-optimization processing unit configured to generate an operating schedule, which is for operating the energy storage system during a set period, for each predetermined section by reflecting electric power billing environment data in at least one of a consumer policy and operating characteristics of the energy storage system and configured to set an electric power reserve to prepare for a shortage of an electric power amount; and an operating control unit configured to detect, for each section, an error between a value measured as being actually consumed and a predicted value of the operating schedule generated by the pre-optimization processing unit and configured to selectively reflect the electric power reserve in a discharging amount corresponding to the operating schedule in a subsequent section according to the detected error to control an energy storage system operating unit.

There is provided a steam turbine startup support system capable of easily selecting a proper startup transition pattern from various startup transition patterns. In a steam turbine startup support system of the embodiment, an economic efficiency evaluation device performs economic efficiency evaluation regarding the various startup transition patterns recorded in a startup transition pattern recording device based on parameters recorded in a parameter recording device and information relating to a rotor lifetime recorded in a rotor lifetime recording device. Besides, a screen display device displays a result of the economic efficiency evaluation performed by the economic efficiency evaluation device.

Systems and methods for data collection and processing are described, including a plurality of variable groups of industrial sensor inputs operationally coupled to an industrial environment and a multiplexer communicatively coupled to the industrial sensor inputs; and a controller configured to receive and monitor the data and adaptively schedule the data collector.

A computer-implemented method is provided for managing a plurality of micro grids in an energy system. The method includes collecting and maintaining, in a central database, configuration information and state information for the plurality of micro grids, by a processor-based dynamic operation engine. The method further includes identifying failures in any of the plurality of micro grids and generating updated configuration information and updated state information relating to the failures, based on data analytics and diagnostic polling applied to the configuration information and the state information, by a processor-based micro grid diagnostic engine. The method also includes autonomously recovering from the failures in any of the plurality of microgrids using one or more backup devices determined based on the updated configuration information and the updated state information, by a processor-based system recovery engine operatively coupled to the processor-based dynamic operation engine and to the processor-based micro grid diagnostic engine.

A production process control method includes acquiring first recorded production process information, extracting second production process information from the first recorded production process information, establishing a matching model for correlating a first satisfied parameter with a second satisfied parameter, establishing a production capability prediction model for predicting production capability, inputting a first production parameter into the matching model to obtain a value of a second production parameter for producing a preset workpiece, inputting the value of the first production parameter and the value of the second production parameter into the production capability prediction model to calculate a complex production capability index (CPK) value, determining whether the CPK value reaches a preset capability standard, and setting the value of the first production parameter and the value of the second production parameter when the CPK value reaches the preset capability standard. The second production process information meets a preset quality standard.

A method for automatically adapting a predictive model used to control a heating, ventilation, or air conditioning (HVAC) system in a building to compensate for a detected fault in the HVAC system is shown. The method includes obtaining an indication of the detected fault in the HVAC system or a zone in the building. The method further includes determining a predicted impact of the detected fault on an operational performance of the HVAC system. The method further includes adjusting one or more parameters of the predictive model based on the predicted impact of the detected fault to generate a fault-adapted predictive model. The method further includes operating the HVAC system to control an environmental condition of the building using the fault-adapted predictive model.

A method for automatically determining an optimized process configuration of a process for manufacturing or processing products that can be executed using a technical system and can be configured using a number of different process configuration parameters comprises: determining a process configuration of the process that is optimized with regard to a defined metric and is defined by respective target values of process configuration parameters using an optimization method that is adapted to the process and is at least partially based on machine learning, using input data that include production data and features that are given by historical process configuration data and status data of the system or process or are derived therefrom; and outputting target process configuration data representing the determined optimized process configuration by means of the target values of the process configuration parameters.