@inproceedings {pub4635,
	title = {Estimating the electrical power output of
industrial devices with end-to-end time-series
classification in the presence of label noise},
	author = {Andrea Castellani AND Sebastian Schmitt AND Barbara Hammer},
	year = {2021},
	abstract = {In complex industrial settings, it is common practice to mon-
itor the operation of machines in order to detect undesired states, adjust
maintenance schedules, optimize system performance or collect usage
statistics of individual machines. In this work, we focus on estimating
the power output of a Combined Heat and Power (CHP) machine of
a medium-sized company facility by analyzing the total facility power
consumption. We formulate the problem as a time-series classification
problem where the class label represents the CHP power output. As the
facility is fully instrumented and sensor measurements from the CHP
are available, we generate the training labels in an automated fashion
from the CHP sensor readings. However, sensor failures result in misla-
beled training data samples which are hard to detect and remove from the
dataset. Therefore, we propose a novel multi-task deep learning approach
that jointly trains a classifier and an autoencoder with a shared embed-
ding representation. The proposed approach targets to gradually correct
the mislabelled data samples during training in a self-supervised fashion,
without any prior assumption on the amount of label noise. We bench-
mark our approach on several time-series classification datasets and find
it to be comparable and sometimes better than state-of-the-art methods. On the real-world use-case of predicting the CHP power output, we
thoroughly evaluate the architectural design choices and show that the
final architecture considerably increases the robustness of the learning
process and consistently beats other recent state-of-the-art algorithms
in the presence of unstructured as well as structured label noise.},
	publisher = {Springer, LNCS, LNAI, LNBI},
	booktitle = {Machine Learning and Knowledge Discovery in Databases. Research Track},
	series = {Lecture Notes in Computer Science }
}