A controller for a building system receives training data including input data and output data. The output data indicate a state of the building system affected by the input data. The controller pre-processes the training data using a first set of pre-processing options to generate a first set of training data and pre-processes the training data using a second set of pre-processing options to generate a second set of training data. The controller performs a multi-stage optimization process to identify multiple different sets of model parameters of a dynamic model for the building system. The multi-stage optimization process includes a first stage in which the controller uses the first set of training data to identify a first set of model parameters and a second stage in which the controller uses the second set of training data to identify a second set of model parameters. The controller uses the dynamic model to operate the building system.

System and method for improving operation of an industrial automation system, which includes a control system that controls operation of an industrial automation process. The control system includes a feature extraction block that determines extracted features by transforming process data determined during operation of an industrial automation process based at least in part on feature extraction parameters; a feature selection block that determines selected features by selecting a subset of the extracted features based at least in part on feature selection parameters, in which the selected features are expected to be representative of the operation of the industrial automation process; and a clustering block that determines a first expected operational state of the industrial automation system by mapping the selected features into a feature space based at least in part on feature selection parameters.

A system usable in a wellbore can include a processing device and a memory device in which instructions executable by the processing device are stored for causing the processing device to: generate multiple predicted values of a first parameter associated with a well environment or a wellbore operation; determine a first trend indicated by the multiple predicted values; receive, from a sensor, multiple measured values of a second parameter associated with the well environment or the wellbore operation; determine a second trend indicated by the multiple measured values; determine a difference between the first trend and the second trend or a rate of change of the difference; and in response to the difference exceeding a threshold or the rate of change exceeding another threshold, determine a source of the difference including at least one of an erroneous user input, an equipment failure, a wellbore event, or a model error.

A mobility device that can accommodate speed sensitive steering, adaptive speed control, a wide weight range of users, an abrupt change in weight, traction control, active stabilization that can affect the acceleration range of the mobility device and minimize back falls, and enhanced redundancy that can affect the reliability and safety of the mobility device.

Predictor variables that affect production or consumption of a resource are sampled at a plurality of times within a time period and aggregated to generate an aggregated value for each predictor variable over the time period. A model is generated which estimates the production or consumption in terms of the predictor variables. A regression process is performed to generate values for a plurality of regression coefficients in the model based on a cumulative production or consumption of the resource for the time period and the aggregated values. The sampled values of the predictor variables are then applied as inputs to the model to estimate productions or consumptions of the resource at each of the plurality of times. The estimated productions or consumptions may be used as inputs to a controller that operates equipment.

The present application provides a method and system for outlier detection, anomalous behavior detection, missing data imputation and prediction of consumption in energy data for one or more energy sensors by using a unified model. The application discloses a data collection module for collect a time series data to be used as training data, a model training module for training the unified model using the collected time series data to enable computation of a plurality of parameters, and a model implementation module for implementing, by the trained unified model, the plurality of parameters on a new data of energy consumption wherein the plurality of parameters are used perform at least one from a group of outlier detection, anomaly detection, missing data imputation and prediction of consumption in energy data.

A building HVAC system includes an airside system having a plurality of airside subsystems, a high-level model predictive controller (MPC), and a plurality of low-level airside MPCs. Each airside subsystem includes airside HVAC equipment configured to provide heating or cooling to the airside subsystem. The high-level MPC is configured to perform a high-level optimization to generate an optimal airside subsystem load profile for each airside subsystem. The optimal airside subsystem load profiles optimize energy cost. Each of the low-level airside MPCs corresponds to one of the airside subsystems and is configured to perform a low-level optimization to generate optimal airside temperature setpoints for the corresponding airside subsystem using the optimal airside subsystem load profile for the corresponding airside subsystem. Each of the low-level airside MPCs is configured to use the optimal airside temperature setpoints for the corresponding airside subsystem to operate the airside HVAC equipment of the corresponding airside subsystem.

The invention relates to the remote monitoring of equipment. In a method for identifying incipient faults in technical equipment, data is obtained about the equipment being monitored; a reference sample of performance indices of the equipment is generated; state matrices and empirical state forecasting models are constructed. Disruptions and integral criteria characterizing deviations in the parameter indices of the equipment being monitored are also determined; information from the equipment being monitored is analyzed; the reference sample is modified; the empirical models are updated. The degree to which the parameter indices of the equipment being monitored deviate from the indices of the empirical models is also determined, and disruptions pertaining to such indices are identified. The calculated disruptions are then ranked; an anomaly for a performance index of the equipment is determined; the type of fault is determined for each anomaly; an equipment fault classifier is generated and an incipient fault is determined and the development thereof is forecast. Automated fault determination is hereby provided.

Disclosed is a soft measurement method of DXN emission concentration based on multi-source latent feature selective ensemble (SEN) modeling. First, MSWI process data is divided into subsystems of different sources according to industrial processes, and principal component analysis (PCA) is used to separately extract the subsystems' latent features and conduct multi-source latent feature primary selection according to the threshold value of the principal component contribution rate preset by experience. Using mutual information (MI) to evaluate the correlation between the latent features of the primary selection and DXN, and adaptively determine the upper and lower limits and thresholds of the latent feature reselection; finally, based on the reselected latent features, a least squares-support vector machine (LS-SVM) algorithm with a hyperparameter adaptive selection mechanism is used to establish DXN emission concentration sub-models for different subsystems, and based on branch and bound (BB) and prediction error information entropy weighting algorithm to optimize the selection of sub-models and calculation weights coefficient, a SEN soft measurement model of DXN emission concentration is constructed.

A control system and method utilizing one or more processors that are configured to determine contaminant loading of blades of a turbomachinery compressor based on one or more environmental conditions to which the turbomachinery compressor is exposed and one or more atmospheric air inlet conditions of the turbomachinery compressor. The one or more processors then determine a corrosion contaminant concentration on the blades of the turbomachinery compressor based on the contaminant loading that is determined and determine an upper limit on or a distribution of potential corrosion of the blades of the turbomachinery based on the corrosion contaminant concentration, at least one of the environmental conditions to which the turbomachinery compressor is exposed, and the corrosion contaminant concentration that is determined.