A controller for HVAC equipment stores a cascaded model that includes a disturbance model configured to predict a heat disturbance affecting the building zone as a function of one or more exogenous parameters and a physics model configured to predict a temperature of the building zone as a function of the heat disturbance and an amount of heating or cooling provided to the building zone by HVAC equipment. The processing circuit is configured to execute a combined training procedure to determine parameters of the disturbance model and parameters of the physics model, generate control signals for the HVAC equipment using the disturbance model to predict the heat disturbance and applying the heat disturbance as an input to the physics model, and operate the HVAC equipment to provide the heating or cooling to the building zone in accordance with the control signals.

Systems and methods for device monitoring. The method may include obtaining first measurement data relating to one or more first operating parameters of a target device, obtaining a correlation model corresponding to the target device, and predicting, based on the first measurement data and the correlation model, second measurement data relating to the one or more second operating parameters of the target device. The correlation model may be generated based on first sample measurement data relating to the one or more first operating parameters and one or more second operating parameters of a reference device. The reference device may be of a same type of device as the target device and equipped with one or more additional sensors compared with the target device, the one or more additional sensors being configured for collecting the first sample measurement data relating to the one or more second operating parameters.

In an embodiment, an agricultural intelligence computing system stores a digital model of crop growth, the digital model of crop growth being configured to compute nutrient requirements in soil to produce particular yield values based, at least in part, on data unique to an agricultural field. The system receives agronomic field data for a particular agronomic field, the agronomic field data comprising one or more input parameters for each of a plurality of locations on the agronomic field, nutrient application values for each of the plurality of locations, and measured yield values for each of the plurality of locations. The system computes, for each location of the plurality of locations, a required nutrient value indicating a required amount of nutrient to produce the measured yield values. The system identifies a subset of the plurality of locations where the computed required nutrient value is greater than the nutrient application value. The system computes, for each of the subset of the plurality of locations, a residual value comprising a difference between the required nutrient value and the nutrient application value. The system generates a residual map comprising the residual values at the subset of the plurality of locations. Using the residual map and the one or more input parameters for each of the plurality of locations, the system generates and stores particular model correction data for the particular agronomic field.

A system for data driven diagnostics of a machine including a machine learning model instantiated in a computer, the machine learning model being configured to: receive operational data of the machine; and process the operational data to determine machine diagnostics information. The machine learning model is trained using simulated defect information received from a simulation environment.

The present invention provides an interval error observer-based aircraft engine active fault tolerant control method, and belongs to the technical field of aircraft control. The method comprises: tracking the state and the output of a reference model of an aircraft engine through an error feedback controller; compensating a control system of the aircraft engine having a disturbance signal and actuator and sensor faults through a virtual sensor and a virtual actuator; observing an error between a system with fault of the aircraft engine and the reference model through an interval error observer, and feeding back the error to the error feedback controller; and finally, using a difference between the output of the reference model of the system with fault and the output of the virtual actuator as a control signal to realize active fault tolerant control of the aircraft engine.

This evaluation device comprises: a mathematical model acquisition unit that acquires a mathematical model expressing the state of a power storage element; an operation data acquisition unit that acquires operation data which includes time-series input data input during operation of a system constructed on the basis of the numerical model, and time-series output data output by the system on the basis of the time-series input data; a processing unit that inputs the time-series input data to the numerical model and executes processing causing time-series model output data to be output from the numerical model; and an evaluation unit that evaluates the design and the operation of the system on the basis of the time-series output data and the time-series model output data.

One or more systems, computer-implemented methods and/or computer program products to facilitate a process to monitor and/or facilitate a modification to a manufacturing process. A system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise an initialization component that identifies values of inflow data of one or more inflows of a set of inflows to a manufacturing process as control variables, and a computation optimization component that optimizes one or more intermediate flows, outflows or flow qualities of the manufacturing process using, for mode-specific regression models, decision variables that are based on a set of joint-levels of the control variables. An operation mode determination component can determine operation modes of the manufacturing process that are together defined by a set of joint-levels of the control variables.

The subject matter of this specification can be embodied in, among other things, a torque motor monitoring system that includes a first observer module configured to receive a first collection of operational information about a torque motor when the torque motor is operating normally, and determine a duty offset ratio value based on the first collection of operational information, a second observer module configured to receive a second collection of operational information about a torque motor, determine a MOSFET gate duty ratio value based on the second collection of operational information and the duty offset ratio value, and determine a motor current value, and a fault detection module configured to identify a fault in the torque motor based on the MOSFET gate duty ratio value, the motor current value, and a predetermined fault determination threshold value.

Methods and systems are provided for increasing an accuracy of anomaly detection in assets such as vehicle components. In one example, a method provides for continuous health monitoring of connected physical assets, comprising adapting thresholds for anomaly detection and root cause analysis algorithms for the connected assets based on an aggregation of new connected data using machine learning; updating and ranking advanced statistical and machine learning models based on their performance using connected data until confirming a best performing model; and deploying the best performing model to monitor the connected physical assets.

A method for predicting a remaining lifetime parameter of a component installed in a system is provided, in particular of an engine component and/or a filter, the method comprising: repeatedly sensing at least one parameter of the system to obtain a history of data values;

fitting an aging pattern to the data values; and

determining a remaining lifetime parameter of the component from the aging pattern, wherein at least some data values are erased with time such that the fitting is based on a subset of the data values determined since an initialization of the algorithm, wherein data values from an initial phase are not erased but retained as anchor values for the fitting throughout the lifetime determination of the component.