Multi-source transfer learning of time series in cyclical manufacturing

Thispaperdescribesanewtransferlearningmethodformodelingsensortimeseriesfollowingmultipledifferentdistributions, e.g.originatingfrommultipledifferenttoolsettings.Themethodaimsatremovingdistributionspecicinformationbeforethe modelingoftheindividualtimeseriestakesplace.Thisisdonebymappingthedatatoanewspacesuchthattherepresentations ofdifferentdistributionsarealigned.Domainknowledgeisincorporatedbymeansofcorrespondingparameters,e.g.physical dimensions of tool settings. Results on a real-world problem of industrial manufacturing show that our method is able to signicantly improve the performance of regression models on time series following previously unseen distributions.(VLID)4858151Version of recor

Multi-source transfer learning of time series in cyclical manufacturing

This paper describes a new transfer learning method for modeling sensor time series following multiple different distributions, e.g. originating from multiple different tool settings. The method aims at removing distribution specific information before the modeling of the individual time series takes place. This is done by mapping the data to a new space such that the representations of different distributions are aligned. Domain knowledge is incorporated by means of corresponding parameters, e.g. physical dimensions of tool settings. Results on a real-world problem of industrial manufacturing show that our method is able to significantly improve the performance of regression models on time series following previously unseen distributions.Version of recor