Abstract: A maintenance solution pipeline is automatically selected from a plurality of maintenance solution pipelines, based on obtained information. The maintenance solution pipeline is to be used in providing a physical asset maintenance solution for a plurality of physical assets. Code and model rendering for the maintenance solution pipeline automatically selected is initiated. Output from an artificial intelligence process is obtained. The output includes an automatically generated risk estimation relating to one or more conditions of at least one physical asset of the plurality of physical assets. Code and model rendering for the maintenance solution pipeline is re-initiated, based on the output from the artificial intelligence process. The maintenance solution pipeline automatically selected is reused.

Abstract: An embodiment includes receiving, by a transformer monitoring system associated with a transformer, sensor data from one or more sensors during operation of the transformer. The embodiment also includes generating, by the transformer monitoring system, energy loss data representative of a predicted energy loss of the transformer based at least in part on the sensor data. The embodiment also includes training, by the transformer monitoring system, a failure rate prediction model using failure data, resulting in a trained failure rate prediction model that calculates failure probability distribution data indicative of a time at which a failure of the transformer is most likely to occur. The embodiment also includes generating, by the transformer monitoring system, replacement data representative of an optimal time for replacing the transformer based at least in part on the energy loss data, the failure probability distribution data, and specification data for the transformer.

Abstract: A method of improving at least one of quality and yield of a physical process comprises: obtaining values, from respective performances of the physical process, for a plurality of variables associated with the physical process; determining at least one Gaussian mixture model (GMM) representing the values for the variables for the performances of the physical process; based at least in part on the at least one GMM, computing at least one anomaly score for at least one of the variables for at least one of the performances of the physical process; based on the at least one anomaly score, identifying the at least one of the performances of the physical process as an outlier; and, based at least in part on the outlier identification, modifying the at least one of the variables for one or more subsequent performances of the physical process.

Abstract: In example aspects of this disclosure, a method includes generating, by one or more computing devices, a parametric model that expresses condition states for each of a plurality of assets, and the probability of the assets transitioning between the condition states; generating, by the one or more computing devices, stochastic degradation predictions of a group of the assets, based on the condition states and the probability of transitioning between the condition states for at least some of the assets; and generating, by the one or more computing devices, a maintenance schedule based on: the stochastic degradation predictions of the group of the assets, costs of corrective maintenance for assets in a failed state, and costs of scheduled maintenance for the assets.

Abstract: Methods, systems, and computer program products for a decision-improvement framework are provided herein. A computer-implemented method includes obtaining regression functions that predict an output of processes of a physical system based on inputs received at each process; automatically generating one or more constraints and one or more objective functions for a model for the physical system based on the regression functions and a representation of the physical system, where the representation specifies relationships between at least a portion of the processes; identifying a set of parameter values for controlling the physical system based on the model; generating a score, for the set of parameter values, based on a predicted improvement of the physical system relative to historical performance of the physical system; and in response to the generated score satisfying a threshold, causing the physical system to be configured in accordance with the set of parameter values.

Abstract: A DC electric motor having a stator mounted to a substrate, the stator having a coil assembly having a magnetic core, a rotor mounted to the stator with a first set of permanent magnets distributed radially about the rotor to facilitate rotation of the rotor and a second set of permanent magnets on the rotor to facilitate determination of an absolute position of the rotor. The motor further includes first and second set of sensors for detection of the magnets of the inner and outer rings. During operation of the motor passage of the permanent magnets over the sensors produces a substantially sinusoidal signal of varying voltage substantially without noise and/or saturation, allowing an absolute position of the rotor relative the substrate to be determined from the sinusoidal signals without requiring use of an encoder or position sensors and without requiring noise-reduction or filtering of the signal.

Abstract: A DC electric motor having a stator mounted to a substrate, the stator having a coil assembly having a magnetic core, a rotor mounted to the stator with permanent magnets distributed radially about the rotor, the permanent magnets extending beyond the magnetic core, and sensors mounted to the substrate adjacent the permanent magnets. During operation of the motor passage of the permanent magnets over the sensors produces a substantially sinusoidal signal of varying voltage substantially without noise and/or saturation, allowing an angular position of the rotor relative the substrate to be determined from linear portions of the sinusoidal signal without requiring use of an encoder or position sensors and without requiring noise-reduction or filtering of the signal.

Abstract: A method and system of optimizing a machine learning process includes receiving an input set of historical data including input values and output values. The historical data is incorporated into a sampling design to form the initial dataset. A surrogate model of the machine learning model is generated by fitting the historical data using a rectified linear activation function (ReLU) deep neural network. Mixed-integer linear programming techniques are applied to the surrogate model to arrive at a set of predicted optimal inputs. The machine learning model is tested using the predicted optimal inputs. Output from the testing of the machine learning model is generated using the predicted optimal inputs. A determination from the output is made as to whether an optimal output has been generated by the testing of the machine learning model using the predicted optimal inputs.

Abstract: In an approach to hyperparameter optimization, one or more computer processors express a hyperparameter tuning process of a model based on a type of model, one or more dimensions of a training dataset, associated loss function of the model, and associated computational constraints of the model, comprising: identifying a set of optimal hyper-rectangles based a calculated local variability and a calculated best function value; calculating a point as a representative for each identified potentially optimal hyper-rectangle by locally searching over the identified set of potentially optimal hyper-rectangles; dividing one or more hyper-rectangles in the identified set of optimal hyper-rectangles into a plurality of smaller hyper-rectangles based on each calculated point; and calculating one or more optimal hyperparameters utilizing a globally converged hyper-rectangle from the plurality of smaller hyper-rectangles.

Abstract: A method includes training, by one or more processing devices, a plurality of machine learning predictive models, thereby generating a plurality of trained machine learning predictive models. The method further includes generating, by the one or more processing devices, a solved machine learning optimization model, based at least in part on the plurality of trained machine learning predictive models. The method further includes outputting, by the one or more processing devices, one or more control input and predicted outputs based at least in part on the solved machine learning optimization model.

Abstract: A relationship between an input, a set-point of a plurality of processes and an output of a corresponding process is learned using machine learning. A regression function is derived for each process based upon historical data. An autoencoder is trained for each process based upon the historical data to form a regularizer and the regression functions and regularizers are merged together into a unified optimization problem. System level optimization is performed using the regression functions and regularizers and a set of optimal set-points of a global optimal solution for operating the processes is determined. An industrial system is operated based on the set of optimal set-points.

Abstract: A system and method for optimizing materials and devices design. The method includes building machine learning models to predict a quality of target measurements based on an experimental design input by formulating a regularized multi-objective optimization to recommend the final experimental design using a logistic curve for the loss function and a model uncertainty quantification term for the final solution. Alternately, the system and method uses a black-box optimization for optimal process design that includes iteratively building a sequence of surrogate functions, where intermediate designs are generated to improve the quality of the surrogate function. Further a derivative-free optimization is performed that utilizes global optimization techniques (global search) with Gaussian process (local method) with a Bayesian optimization to produce a sequence of designs that leads to an optimal design.

Abstract: A system and method for learning a predictive function that can automatically learn different operating modes for a multi-modal system and predict the number of operating states for a multi-modal system and additionally the detailed structure for each state. Once learned, the predictive function (model) can be used to determine a mode of a new sample (an asset). Based on the determined components that maximize a log likelihood function, a mode of the new sample is detected into the model via dependency graphs. One aspect includes enforcing a lower bound for the number of sample points to form an operational mode for an asset. While a mode relates to sample points which maximizes like log-likelihood, an ability is provided to remove artifact modes due to noisy data by considering a sufficient sample data condition and maximizing log-likelihood. Domain knowledge can be incorporated into the model via dependency graphs.

Abstract: In unsupervised interpretable machine learning, one or more datasets having multiple features can be received. A machine can be trained to jointly cluster and interpret resulting clusters of the dataset by at least jointly clustering the dataset into clusters and generating hyperplanes in a multi-dimensional feature space of the dataset, where the hyperplanes separate pairs of the clusters, where a hyperplane separates a pair of clusters. Jointly clustering the dataset into clusters and generating hyperplanes can repeat until convergence, where the clustering in a subsequent iteration uses the generated hyperplanes from a previous iteration to optimize performance of the clustering. The hyperplanes can be adjusted to further improve the performance of the clustering. The clusters and interpretation of the clusters can be provided, where a cluster's interpretation is provided based on hyperplanes that construct a polytope containing the cluster.

Abstract: Software that performs the following operations: (i) receiving a set of graph predictions corresponding to an input text, where graph predictions of the set of graph predictions are generated by different respective machine learning models; (ii) blending the graph predictions of the set of graph predictions to generate a plurality of candidate blended graphs, where nodes and edges of the candidate blended graphs have respective selection metric values, generated using a selection metric function, that meet a minimum threshold; and (iii) selecting as an output blended graph a candidate blended graph of the plurality of candidate blended graphs having a highest total combination of selection metric values among the plurality of candidate blended graphs.

Abstract: A method includes: receiving, by a computing device, data from sensors in a manufacturing environment; mapping, by the computing device, the data into a deep learning network; learning, by the computing device, correlations between inputs and outputs of the manufacturing environment using the data; pruning, by the computing device, the deep learning network; predicting, by the computing device and using the pruned network, an output of the pruned network from the inputs of the manufacturing environment; linearizing, by the computing device, the pruned network; optimizing, by the computing device, the output of the linearized pruned network to calculate predicted inputs for the manufacturing environment; and changing, by the computing device, operation inputs in the manufacturing environment to match the predicted inputs.

Abstract: A DC electric motor having a stator mounted to a substrate, the stator having a coil assembly having a magnetic core, a rotor mounted to the stator with permanent magnets distributed radially about the rotor, the permanent magnets extending beyond the magnetic core, and sensors mounted to the substrate adjacent the permanent magnets. During operation of the motor passage of the permanent magnets over the sensors produces a substantially sinusoidal signal of varying voltage substantially without noise and/or saturation, allowing an angular position of the rotor relative the substrate to be determined from linear portions of the sinusoidal signal without requiring use of an encoder or position sensors and without requiring noise-reduction or filtering of the signal.

Abstract: An approach to federated learning of a machine learning model may be provided. The approach may include broadcasting hyperparameters of a machine learning model to one or more client computing devices from a primary device associated with an outer loop or an inner loop. A gradient for the loss function may be calculated at the client device if previous gradients have been sufficiently large. If gradients exceeds a threshold, the client can send the mini-batch of gradients or the difference of the mini-batch of gradients back to the primary device. A search direction may be calculated based on the full gradient of the loss function for an outer loop or the mini-batch of gradient differences for an inner loop. A learning rate step may be calculated from the search direction. The hyperparameter may be updated for the inner loop based on the learning rate.

Abstract: A system and method can be provided for constructing and training a decision tree for machine learning. A training set can be received. The decision tree can be initialized by constructing a root node and a root solver can be trained with the training set. A processor can grow the decision tree by iteratively splitting nodes of the decision tree, where at a node of the decision tree, dimension reduction is performed on features of data of the training set received at the node, and the data having reduced dimension is split based on a routing function, for routing to another node of the decision tree. The dimension reduction and the split can be performed together at the node based on solving a nonlinear optimization problem.

Abstract: Aspects of the invention include an optimal interpretable decision tree using integer linear programming techniques. A non-limiting example computer-implemented method includes receiving, using a processor, a plurality of data inputs from a process and selecting, using the processor, a data subset from the plurality of data inputs by solving linear programming to obtain a solution. The method builds and optimizes, using the processor, an optimal decision tree based on the data subset and alerts, using the processor, a user when a prediction of the optimal decision tree is greater than a threshold value.