A method is provided for controlling electrical components in a vehicle including multiple traction voltage systems, wherein each traction voltage system includes at least one electrical component, and which electrical component has the same function in each traction voltage system, the method involving the steps of monitoring and registering the state of health of each electrical component over time; predicting a predetermined parameter for each electrical component, which parameter is related to a future operating state inhibiting the use of the components; determining a control strategy for each electrical component based on the state of health of the electrical components to balance the parameters towards a common value; and controlling the electrical components based on the determined control strategy.

Disclosed are embodiments for automatically resolving faults in a complex network system. Some embodiments monitor one or more of system operational parameter values and message exchanges between network components. A machine learning model detects a fault in the complex network system, and an action is selected based on a cause of the fault. After the action is applied to the complex network system, additional monitoring is performed to either determine the fault has been resolved or additional actions are to be applied to further resolve the fault.

A system includes equipment operable to affect, monitor, or control one or more variable states or conditions in a building. The system further includes circuitry configured to perform a plurality of performance checks for the equipment, determine a plurality of individual performance check indicators based on the plurality of performance checks using a plurality of first weights determined based on different timings, generate an overall performance index for the equipment using the plurality of individual performance check indicators and a plurality of second weights, and initiate or execute a preventative maintenance action for the equipment based on the overall performance index.

Detecting equipment failure risk in industrial process may include distributing equipment operations data to a cluster of servers based on a range of time and operation specified in maintenance data associated with the equipment. From a record entry in the maintenance data, an operation and installation and maintenance time may be determined. A plurality of servers storing equipment operations data associated with the operation during a time range between the installation and the maintenance time are selected. A distributed processing operation in each of the plurality of servers is executed to run in parallel and computes operation features. The operation features are aggregated and added as an entry in a target table. Equipment failure risk is detected by risk failure analysis performed based on the target table. A signal may be sent to automatically adjust or correct one or more operation features.

A diagnostics system, for mass flow controller calibration, comprising a controller communicable coupled to a sensor(s) and a valve. The controller controls the valve based on a predetermined set point value and communication from the at least one sensor. The controller determines a number of set point value adjustments and compares results of a calibration operation and a set point value plus a tolerance value. The controller generates a notification message indicating at least one of the predetermined number of set point value adjustments and results of the comparing. The controller calibrates the mass flow controller based on, at least in part, one of a predetermined number of set point value adjustments, results of the comparison, and user input. The notification message can comprise temperature values, valve drive values, sensor flow rate values, gas flow hours, and a remaining number of set point adjustments based on a total fluid hours.

A system and computer-implemented method for identifying and repairing suboptimal operation of a machine, the computer-implemented method including: monitoring sensory input data related to an industrial machine; analyzing, using an unsupervised machine learning model, the monitored sensory inputs, wherein the output of the unsupervised machine learning model includes at least one indicator; identifying, based on the at least one indicator, at least one behavioral pattern related to the industrial machine, wherein each of the at least one behavioral pattern is indicative of at least one suboptimal operation of the industrial machine; selecting at least one corrective action based on the at least one behavioral pattern; and performing the at least one corrective action on the industrial machine.

A computer-implemented method and system is provided. The system adaptively switches prediction strategies to optimize time-variant energy demand and consumption of built environments associated with renewable energy sources. The system analyzes a first, second, third, fourth and a fifth set of statistical data. The system derives of a set of prediction strategies for controlled and directional execution of analysis and evaluation of a set of predictions for optimum usage and operation of the plurality of energy consuming devices. The system monitors a set of factors corresponding to the set of prediction strategies and switches a prediction strategy from the set of derived prediction strategies. The system predicts a set of predictions for identification of a potential future time-variant energy demand and consumption and predicts a set of predictions. The system manipulates an operational state of the plurality of energy consuming devices and the plurality of energy storage and supply means.

The present disclosure provides a computer-implemented method for ranking one or more control schemes for controlling peak loading conditions and abrupt changes in energy pricing of one or more built environments associated with renewable energy sources. The computer-implemented method includes analysis of a first set of statistical data, a second set of statistical data, a third set of statistical data, a fourth set of statistical data and a fifth set of statistical data. Further, the computer-implemented method includes identification and execution of the one or more control schemes. In addition, the computer-implemented method includes scoring the one or more control schemes by evaluating a probabilistic score. Further, the computer-implemented method includes ranking the one or more control schemes to determine relevant control schemes for controlling real time peak loading conditions and abrupt changes in energy pricing associated with the one or more built environments.

Methods and systems for a data marketplace in a conveyor environment includes a self-organizing data marketplace. The self-organizing data marketplace includes at least one data collector and at least one corresponding conveyor in an industrial environment, wherein the at least one data collector is structured to collect detection values from at least one sensor of a power roller of the at least one corresponding conveyor; a data storage structured to store a data pool comprising at least a portion of the detection values; a data marketplace structured to self-organize the data pool; and a transaction system structured to interpret a user data request, and to selectively provide a portion of the self-organized data pool to a user in response to the user data request.

To determine system settings for an industrial system, digital twin data of a digital twin of the industrial system is retrieved. System simulations of the industrial system are performed based on the digital twin data to explore candidate system settings for the industrial system prior to application of one of the candidate system settings to the industrial system. At least one optimization objective or at least one constraint used in the system simulations is changed while the system simulations are being performed on an ongoing basis. The results of the system simulations are used to identify one of the candidate system settings for application to the industrial system.