Abstract: A method for predicting failure modes in a machine includes learning (31) a multivariate Gaussian distribution for each of a source machine and a target machine from data samples from one or more independent sensors of the source machine and the target machine, learning (32) a multivariate Gaussian conditional distribution for each of the source machine and the target machine from data samples from one or more dependent sensors of the source machine and the target machine using the multivariate Gaussian distribution for the independent sensors, transforming (33) data samples for the independent sensors from the source machine to the target machine using the multivariate Gaussian distributions for the source machine and the target machine, and transforming (34) data samples for the dependent sensors from the source machine to the target machine using the transformed independent sensor data samples and the conditional Gaussian distributions for the source machine and the target machine.

Abstract: A method and system for recognizing (and/or predicting) failures of sensors used in monitoring gas turbines applies a sparse coding process to collected sensor readings and defines the L-1 norm residuals from the sparse coding process as indicative of a potential sensor problem. Further evaluation of the group of residual sensor readings is perform to categorize the group and determine if there are significant outliers (“abnormal data”), which would be considered as more likely associated with a faulty sensor than noisy data. A time component is introduced into the evaluation that compares a current abnormal result with a set of prior results and making the faulty sensor determination if a significant number of prior readings also have an abnormal value. By taking the time component into consideration, the number of false positives is reduced.

Abstract: A method for training a deep learning network includes defining a loss function corresponding to the network. Training samples are received and current parameter values are set to initial parameter values. Then, a computing platform is used to perform an optimization method which iteratively minimizes the loss function. Each iteration comprises the following steps. An eigCG solver is applied to determine a descent direction by minimizing a local approximated quadratic model of the loss function with respect to current parameter values and the training dataset. An approximate leftmost eigenvector and eigenvalue is determined while solving the Newton system. The approximate leftmost eigenvector is used as negative curvature direction to prevent the optimization method from converging to saddle points. Curvilinear and adaptive line-searches are used to guide the optimization method to a local minimum. At the end of the iteration, the current parameter values are updated based on the descent direction.

Abstract: A system for predicting time-to-failure of a machine includes one or more processors and a non-transitory, computer-readable storage medium in operable communication with the processors. The computer-readable storage medium contains one or more programming instructions that, when executed, cause the processors to receive or retrieve multivariate time series data observed a plurality of times, and infer a plurality of state variables from the multivariate time series data, each state variable describing an operating condition of the machine at a particular time. The instructions further cause the processor to compute an average life consumption rate by applying a life consumption rate model to the plurality of state variables and time-to-failure for the machine based on the average life consumption rate. The time-to-failure for the machine may then be reported to one or more users.

Abstract: A method of real-time optimization for a Combined Cooling, Heating and Power system, including determining a first operation sequence of a plurality of chillers and at least one thermal energy storage tank in the system over a time period (410) and determining a second operation sequence of the plurality of chillers and at least one thermal energy storage tank in the system over the time period by using the first operation sequence as input to a greedy algorithm (420).

Abstract: A method (100) for electricity demand shaping through load shedding and shifting in an electrical smart grid.

Abstract: A computer-implemented method for determining optimized amount of radiopharmaceutical to be produced at a production facility, the radiopharmaceutical being for use in nuclear imaging at customer sites, in order to meet aggregate demands of orders placed by the customer sites (e.g. medical imaging centers, hospitals, etc.), wherein the quantity of radiopharmaceutical is sufficient to meet the aggregate demand while minimizing any overproduction of the radiopharmaceutical.

Abstract: A method (100) for demand shaping through load shedding and shifting in an electrical smart grid.

Abstract: A method for solar forecasting includes receiving a plurality of solar energy data as a function of time of day at a first time, forecasting (620) from the solar energy data a mode, where the mode is a sunny day, a cloudy day, or an overcast day, and the forecast predicts the mode for a next solar energy datum, receiving (622) the next solar energy datum, updating a probability distribution function (pdf) of the next solar energy datum given the mode, updating a pdf of the mode for the next solar energy datum from the updated pdf of the new solar energy datum given the mode, forecasting (624, 626) a plurality of future unobserved solar energy data from the updated pdf of the mode, where the plurality of future unobserved solar energy data and the plurality of solar energy data have a Gaussian distribution for a given mode determined from training data.

Abstract: A method to manage operating costs of a combined cooling heating and power (CCHP) plant that includes converting complex models of underlying components of the plant into simplified models (S101), performing an optimization that uses the simplified models as constraints of the optimization to output at least one decision variable (S102), and adjusting controls of the plant based on one or more of the output decision variables (S103).

Abstract: A system and method of predicting future power plant operations is based upon an artificial neural network model including one or more hidden layers. The artificial neural network is developed (and trained) to build a model that is able to predict future time series values of a specific power plant operation parameter based on prior values. By accurately predicting the future values of the time series, power plant personnel are able to schedule future events in a cost-efficient, timely manner. The scheduled events may include providing an inventory of replacement parts, determining a proper number of turbines required to meet a predicted demand, determining the best time to perform maintenance on a turbine, etc. The inclusion of one or more hidden layers in the neural network model creates a prediction that is able to follow trends in the time series data, without overfitting.

Abstract: A method and system for generating a high-level language (i.e., PDF) report with embedded 3D objects. The report is prepared by using an XML template where selected 3D objects are imported into the template and enabled to be activated and manipulated by persons viewing the report, without the need to utilize vendor-specific 3D software. The XML template supports various types of 3D models from various data sources, such as engineering CAD models, medical volumetric data, etc. A specific XML fragment in the template is configured to allow for a 3D object (created using any type of software system) to be imported in “active” form to the document being created. Once the actual PDF report is generated, it may be distributed to various recipients who are then able to manipulate the 3D object(s).

Abstract: A system and method for augmenting an existing u3d file to include additional 3D functions/illustrations accessible by anyone who later accesses the file is based upon adding a “composer” to the system used to generate 3D PDF reports. The composer utilizes a high-level specification based on a new “Additional Data Inclusion” (ADI) file format that defines additional types of 3D information that can be added to the existing u3d file. The various types of additional information may include, for example, additional viewing planes, clipping planes, textures, and the like. With the ability to add this type of information, an individual preparing a report including a 3D object is able to augment the supplied 3D information (i.e., the “existing” u3d file) with particular information that may be relevant to those individuals later reviewing the report.

Abstract: A generalized autoregressive integrated moving average (ARIMA) model for use in predictive analytics of time series is based upon creating all possible ARIMA models (by knowing a priori the largest possible values of the p, d and q parameters forming the model), and utilizing the results of at least two different performance measures to ultimately choose the ARIMA(p,d,q) model that is most appropriate for the time series under study. The method of the present invention allows each parameter to range over all possible values, and then evaluates the complete universe of all possible ARIMA models based on these combinations of p, d and q to find the specific p, d and q parameters that yield the “best” (i.e., lowest value) performance measure results. This generalized ARIMA model is particularly useful in predicting future operating hours of power plants and scheduling maintenance events on the gas turbines at these plants.

Abstract: A method of predicting an amount of power that will be generated by a solar power plant at a future time includes: forecasting a value of a data variable at the future time that is likely to affect the ability of the solar power plant to produce electricity (S301); computing a plurality of features from prior observed amounts of power generated by the power plant during different previous durations (S302); determining a trending model from the computed features and the forecasted value (S303); and predicting the amount of power that will be generated by the power plant at the future time from the determined model (S304).

Abstract: According to an aspect of the invention, there is provided a method for optimizing a cost of electric power generation in a smart site energy management model, including providing a cost function that models a smart building-grid energy system of a plurality of buildings on a site interconnected with electric power grid energy resources and constraints due to a building model, an electric grid model, and a building-grid interface model, where decision variables for each of the building model, the electric grid model, and the building-grid interface model are box-constrained, and minimizing the cost function subject to the building model constraints, the electric grid model constraints, and building-grid interface model constraints.

Abstract: A statistical technique is used to estimate the status of switching devices (such as circuit breakers, isolator switches and fuses) in distribution networks, using scares (i.e., limited or non-redundant) measurements. Using expected values of power consumption, and their variance, the confidence level of identifying the correct topology, or the current status of switching devices, is calculated using any given configuration of real time measurements. Different topologies are then compared in order to select the most likely topology at the prevailing time. The measurements are assumed as normally distributed random variables, and the maximum likelihood principle or a support vector machine is applied.

Abstract: Reference data from sensors measuring characteristics of a gas turbine are analyzed to identify underperformance of the gas turbine, which may be a predictor of an unscheduled shutdown. Time series data from the sensors are compared to annotated query data using an open-begin-end dynamic time warping algorithm. Identified subsequences are examined as possible underperformance indicators. In a related technique, multiple time series from the sensors are pairwise compared using a dynamic time warping algorithm, and computed distances between the time series are used to group the time series using a hierarchical clustering algorithm. The clusters are examined to identify underperformance indicators.

Abstract: A method (100) for demand shaping through load shedding and shifting in an electrical smart grid.

Abstract: Properties of coal are determined from samples processed by a near-infrared spectroscopy (NIR) device that generates wavelengths dependent spectra. Target values of the properties are associated with the NIR spectra by a kernel based regression model generated from training data based on an anisotropic kernel function that is extended by defining the kernel parameters as a smooth function over the wavelengths associated with a spectrum. Like the anisotropic case each wavelength related dimension has its own kernel parameter. Adjacent dimensions are restricted to have similar kernel parameters. Measured spectra with a limited number of features are reconstructed by applying a regression model based on training data of spectra having an extended number of features. Training data are pruned based on a regression model by removing outliers.