Dimaag-ai, Inc.

Fuk Ho Pius Ng

Satish Padmanabhan

Rajaram Kudli

Ian Wright

Gomathi Sankar

Nagarjun Pogakula Surya Prakash

Nagarjun Pogakula Surya

Dushyanth Gokhale

Ananda Shekappa Sonnada

The failure modes of mechanical components may be determined based on text analysis. For example, a word embedding may be determined based on a plurality of text documents that include a plurality of maintenance records characterizing failure of mechanical components. A vector representation for a particular maintenance record may then be determined based on the word embedding. Based on the vector representation, the particular maintenance record may then be identified as belonging to a particular failure mode out of a set of possible failure modes.

Failure mode discovery for machine components

A feature data segment may be determined by applying a feature segmentation model to a test data observation. The feature segmentation model may be pre-trained via a plurality of training data observations and may divide the plurality of training data observations into a plurality of feature data segments. A predicted target value may be determined by applying to a test data observation a prediction model pre-trained via a plurality of training data observations. One or more distance metrics representing a respective distance between the test data observation and the feature data segment along one or more dimensions may be determined. The one or more distance metrics may be represented in a user interface. An updated prediction model and an updated feature segmentation model that both incorporate the test data observation and the training data observations may be determined based on user input.

Detection and visualization of novel data instances for self-healing AI/ML model-based solution deployment

In many industrial settings, a process is repeated many times, for instance to transform physical inputs into physical outputs. To detect a situation involving such a process in which errors are likely to occur, information about the process may be collected to determine time-varying feature vectors. Then, a drift value may be determined by comparing feature vectors corresponding with different time periods. When the drift value crosses a designated drift threshold, a predicted outcome value may be determined by applying a prediction model. Sensitivity values may be determined for different features, and elements of the process may then be updated based at least in part on the sensitivity values.

Drift detection in static processes

Systems and methods are disclosed for training a previously trained neural network with incremental dataset. Original train data is provided to a neural network and the neural network is trained based on the plurality of classes in the sets of training data and/or testing data. The connected representation and the weights of the neural network is the model of the neural network. The trained model is to be updated for an incremental train data. The embodiments provide a process by which the trained model is updated for the incremental train data. This process creates a ground truth for the original training data and trains on the combined set of original train data and the incremental train data. The incremental training is tested on a test data to conclude the training and to generate the incremental trained model, minimizing the knowledge learned with the original data. Thus, the results remain consistent with the original model trained by the original dataset except the incremental train data.

Apparatus for knowledge based evolutionary learning in AI systems

Described herein are methods and systems for remote charging of work vehicles using recharge vehicles. A recharge vehicle is equipped with power storage that has a sufficient capacity for propelling the recharge vehicle between a charging station (used for charging the recharge vehicle) and a work location (of the work vehicle) and also for charging the work vehicle at the work location. In some examples, the charging of the work vehicle and even the connection between the vehicles are formed while the work vehicle continues to operate. This approach helps to maximize the operating time of the work vehicle. Furthermore, this approach relaxes the charge rate requirement for charging the recharge vehicle and also for charging the work vehicle as well as the location of the charging station (for charging the recharge vehicle). In some examples, work vehicles and/or recharge vehicles are autonomous vehicles and use vehicle-to-vehicle coordination features.

Methods and systems for remote charging of work vehicles using recharge vehicles

Remaining useful life may be estimated for a machine component by training a prediction model, even when limited data from actual failures is available. Feature data such as sensor readings associated with a mechanical process may be collected over time. Such readings may be paired with estimates of remaining useful life, for instance as extracted from unstructured text of maintenance records. Such data may be used to train and test the prediction model.

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Portland, OR, United States of America

Fuk Ho Pius Ng