Abstract: Example implementations described herein involve systems and methods for efficient learning for mixture of domains which can include applying a clustering technique to a set of data comprised of multiple domains to obtain an initial domain separation of the set of data into one or more clusters; training one or more experts associated with each of the one or more clusters based on the initial domain separation where each expert corresponds with one domain of the multiple domains; inputting all data points to the one or more experts for refining each of the one or more clusters using expert output probabilities; retraining the one or more experts based on the refined one or more clusters; and training a gating mechanism to route an input to an appropriate expert of the one or more experts based on the refined one or more clusters.

Abstract: A method for detecting an anomaly in time series sensor data. The method may include identifying a noisiest cycle from the time series sensor data; for an evaluation of the noisiest cycle indicative of the anomaly being detected at a confidence level above a threshold, providing an output associated with the noisiest cycle as being the anomaly; and for the evaluation of the noisiest cycle indicative of the anomaly being detected at the confidence level not above the threshold: identifying a cycle from the time series sensor data having a most differing shape; and providing the output associated with the cycle having the most differing shape as being the anomaly.

Abstract: An apparatus for predicting a characteristic of a system is provided. The apparatus may include a memory and at least one processor coupled to the memory. The at least one processor may be configured to perform a method including measuring, at a high sample rate, data relating to an operation of the system over a first time period. The method may further include producing a two-dimensional (2D) time-and-frequency input data set by applying a wavelet transform to the measured data. The method may additionally include generating a set of one or more values associated with one or more system characteristics by processing the 2D time-and-frequency input data set using a functional neural network (FNN).

Abstract: Example implementations described herein can involve systems and methods involving, for receipt of input data from one or more assets, identifying and separating different event contexts from the input data; training a plurality of machine learning models for each of the different event contexts; selecting a best performing model from the plurality of machine learning models to form a compound model; selecting a best performing subset of the input data for the compound model based on maximizing a metric; and deploying the compound model for the selected subset.

Abstract: Systems and methods described herein involve facilitating a recommendation of materials to users, which can involve determining, from a job profile, a job experience level of a user for a job type and equipment type combination; determining, for each material in a database of materials, the job experience level associated with the each material based on an access log to the each material by one or more users and content of the each material to generate a material profile for each of the job experience level; and generating a recommendation of materials from the database for the user based on the determined job experience level of the user and the job experience level associated with the each material.

Abstract: Example implementations described herein involve systems and methods for generating an ensemble of deep learning or neural network models, which can involve, for a training set of data, generating a plurality of model samples for the training set of data, the plurality of model samples generated from deep learning or neural network methods; and aggregating output of the model samples to generate an output of the ensemble models.

Abstract: Example implementations involve systems and methods for predicting failures and remaining useful life (RUL) for equipment, which can involve, for data received from the equipment comprising fault events, conducting feature extraction on the data to generate sequences of event features based on the fault events; applying deep learning modeling to the sequences of event features to generate a model configured to predict the failures and the RUL for the equipment based on event features extracted from data of the equipment; and executing optimization on the model.

Abstract: Example implementations described herein are directed to systems and methods for estimating the remaining useful life of a component or equipment through the application of models for deriving functions that can express the remaining useful life over time. In an aspect, the failure acceleration time point is determined for a given type of component, and a function is derived based on the application of models on the failure acceleration time point.

Abstract: Example implementations described herein involve systems and methods for generating an ensemble of deep learning or neural network models, which can involve, for a training set of data, generating a plurality of model samples for the training set of data, the plurality of model samples generated from deep learning or neural network methods; and aggregating output of the model samples to generate an output of the ensemble models.

Abstract: Example implementations described herein involve a system for maintenance predictions generated using a single deep learning architecture. The example implementations can involve managing a single deep learning architecture for three modes including a failure prediction mode, a remaining useful life (RUL) mode, and a unified mode. Each mode is associated with an objective function and a transformation function. The single deep learning architecture is applied to learn parameters for an objective function through execution of a transformation function associated with a selected mode using historical data. The learned parameters of the single deep learning architecture can be applied with streaming data from with the equipment to generate a maintenance prediction for the equipment.

Abstract: Example implementations described herein are directed to constructing prediction models and conducting predictive maintenance for systems that provide sparse sensor data. Even if only sparse measurements of sensor data are available, example implementations utilize the inference of statistics with functional deep networks to model prediction for the systems, which provides better accuracy and failure prediction even if only sparse measurements are available.

Abstract: Example implementations described herein involve a system for maintenance predictions generated using a single deep learning architecture. The example implementations can involve managing a single deep learning architecture for three modes including a failure prediction mode, a remaining useful life (RUL) mode, and a unified mode. Each mode is associated with an objective function and a transformation function. The single deep learning architecture is applied to learn parameters for an objective function through execution of a transformation function associated with a selected mode using historical data. The learned parameters of the single deep learning architecture can be applied with streaming data from with the equipment to generate a maintenance prediction for the equipment.

Abstract: Example implementations described herein are directed to systems and methods for estimating the remaining useful life of a component or equipment through the application of models for deriving functions that can express the remaining useful life over time. In an aspect, the failure acceleration time point is determined for a given type of component, and a function is derived based on the application of models on the failure acceleration time point.

Abstract: Equipment uptime is getting increasingly important across different industries which seek for new ways of increasing equipment availability. Detecting faults in the system by condition based maintenance (CBM) is not enough, because at the time of fault occurrence, the spare parts might not available or the needed resources (maintainers) are busy. Therefore, prediction failures and estimation of remaining useful life can be necessary. Moreover, not only predictions but also uncertainty in the predictions is critical for decision making. Example implementations described herein are directed to tuning parameters of deep learning network architecture by developing a mechanism to optimize for accuracy and uncertainty simultaneously, thereby achieving better asset availability, maintenance planning and decision making.

Abstract: Provided herein are microfluidic devices that can be used as a 3D bioassay, e.g., for drug screening, personalized medicine, tissue engineering, wound healing, and other applications. The device has a series of channels {e.g., small fluid channels) in a small polymer block wherein one or more of the channels can be filled with a biologically relevant gel, such as collagen, which is held in place by posts. As shown herein, when the device is plated with cells such as endothelial cells, new blood vessels grow in the gel, which is thick enough for the cells to grow in three dimensions. Other channels, e.g., fluid channels, allow drugs or biological material to be exposed to the 3D cell growth. Cells, such as endothelial cells, can be cultured and observed as they grow on the surface of a 3D gel scaffold, where e.g., rates of angiogenesis can be measured, as well as intervascularization and extravascularization of cancerous cells.

Abstract: Provided herein are microfluidic devices that can be used as a 3D bioassay, e.g., for drug screening, personalized medicine, tissue engineering, wound healing, and other applications. The device has a series of channels {e.g., small fluid channels) in a small polymer block wherein one or more of the channels can be filled with a biologically relevant gel, such as collagen, which is held in place by posts. As shown herein, when the device is plated with cells such as endothelial cells, new blood vessels grow in the gel, which is thick enough for the cells to grow in three dimensions. Other channels, e.g., fluid channels, allow drugs or biological material to be exposed to the 3D cell growth. Cells, such as endothelial cells, can be cultured and observed as they grow on the surface of a 3D gel scaffold, where e.g., rates of angiogenesis can be measured, as well as intervascularization and extravascularization of cancerous cells.

Abstract: A disc drive includes a motor that positions a head assembly on a disc surface. The motor comprises a central stator assembly that includes coils arranged on a yoke. The coils provide multiple stator current loops on the central stator assembly. The motor also comprises a rotor assembly that includes a permanent magnet ring. The permanent magnet ring is rotatably arranged around the central stator assembly and provides multiple rotor dipole magnetic fields that cross the current loops. The motor includes a beam coupled between the permanent magnet ring and the head assembly.