Robert Boschgmbh

Hewlett-packard Development Company, L.p.

Hewlett Packard Enterprise Development LP

Rumi Ghosh

Soundararajan Srinivasan

Ruobing Chen

Shan Kang

Marc Naumann

Sitaram Asur

Mahesh Goud Tandarpally

Soundar Srinivasan

Zubin Abraham

Prasanth Lade

Charmgil Hong

Subhro Das

Methods and systems of identifying a time reduction in a manufacturing time associated with a plurality of products. One system includes an electronic processor configured to receive training data. The electronic processor is also configured to determine a first set of testing parameters from the plurality of testing parameters to remove for the assembly line based on the training data and determine a second set of testing parameters to keep by removing the first set of testing parameters from the plurality of testing parameters. The electronic processor is also configured to determine a predictive model to replace the first set of testing parameters based on the training data associated with the second set of testing parameters, and automatically update a testing process for the assembly line to turn off the first set of testing parameters and use the predictive model in place of the first set of testing parameters.

Test time reduction for manufacturing processes by substituting a test parameter

Methods, systems, and apparatuses for adapting a predictive model for a manufacturing process. One method includes receiving, with an electronic processor, a plurality of data points for a plurality of manufactured parts and the predictive model. The predictive model outputs a label for a manufactured part provided by the manufacturing process indicating whether the manufactured part is accepted or rejected. The method also includes estimating, with the electronic processor, a drift for each of the plurality of data points and generating, with the electronic processor, an adapted version of the predictive model based on the predictive model and the drift for each of the plurality of data points. In addition, the method includes outputting, with the electronic processor, a label for each of the plurality of manufactured parts using the adapted version of the predictive model.

Model adaptation and online learning for unstable environments

Methods and systems of identifying a time reduction in a manufacturing time associated with a plurality of products. One system includes an electronic processor configured to receive a dataset associated with an assembly line. The dataset includes a classification for each of the plurality of products produced by the assembly line, where the assembly line is associated with a plurality of tests. The electronic processor is also configured to determine a set of test combinations for the assembly line based on the plurality of tests and, for each test combination, determine a number of missing products based on the classification for each of the plurality of products. The electronic processor is also configured to determine at least one test to remove based on the number of missing products for each test combination and output a result including an indication of the at least one test to remove.

Test time reduction for manufacturing processes by removing a redundant test

A manufacturing process system comprises any number of assembly stations and test stations, a model unit, and any number of final products is provided. Any of a sample test method and the statistical distribution monitoring method performed by the model unit is configured to monitor the model quality after it is deployed and reduce potential unnecessary costs, such as warranty claim costs as a result of sending bad units to the customers, and rework costs as a result of predicting a good part as bad and wasting additional testing efforts on the bad parts. Further, both methods are configured to maximize the probability of detecting hazardous issues, while having control of the false alarm rate.

System and method for monitoring a manufacturing plant

Methods, systems, and apparatuses for performing target parameter analysis for an assembly line including a plurality of stations. One method includes receiving, with an electronic processor, training data associated with the assembly line. The training data including a plurality of attributes. The method also includes receiving, with the electronic processor, value addition data for each of the plurality of stations. The value addition data for each of the plurality of stations specifying a non-negative value added by each of the plurality of stations. The method also includes learning, with the electronic processor, a decision tree based on the training data and the value addition data. The method also includes performing the target parameter analysis based on the decision tree.

Value addition dependent data mining techniques for assembly lines

In the examples provided herein, a controversy detection system includes a classifier engine to classify each of a plurality of comments about a publication as controversial or non-controversial. The system also includes a controversy detector engine to determine, based on the classification of the plurality of comments, whether the publication is controversial or non-controversial.

Controversy detector

Examples relate to integrating and extracting topics from content of heterogeneous sources. Observed words are identified in documents, which are received from the heterogeneous sources. Next, document metadata and source metadata are obtained from the heterogeneous sources. The document metadata is used to calculate word topic probabilities for the observed words, and the source metadata is used to calculate source topic probabilities for the observed words. A latent topic is then determined for one of the documents based on the observed words, the word topic probabilities, and the source topic probabilities.

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Rumi Ghosh