  With the proliferation of IoT devices, the distributed control systems are
now capturing and processing more sensors at higher frequency than ever before.
These new data, due to their volume and novelty, cannot be effectively consumed
without the help of data-driven techniques. Deep learning is emerging as a
promising technique to analyze these data, particularly in soft sensor
modeling. The strong representational capabilities of complex data and the
flexibility it offers from an architectural perspective make it a topic of
active applied research in industrial settings. However, the successful
applications of deep learning in soft sensing are still not widely integrated
in factory control systems, because most of the research on soft sensing do not
have access to large scale industrial data which are varied, noisy and
incomplete. The results published in most research papers are therefore not
easily reproduced when applied to the variety of data in industrial settings.
Here we provide manufacturing data sets that are much larger and more complex
than public open soft sensor data. Moreover, the data sets are from Seagate
factories on active service with only necessary anonymization, so that they
reflect the complex and noisy nature of real-world data. We introduce a
variance weighted multi-headed auto-encoder classification model that fits well
into the high-dimensional and highly imbalanced data. Besides the use of
weighting or sampling methods to handle the highly imbalanced data, the model
also simultaneously predicts multiple outputs by exploiting output-supervised
representation learning and multi-task weighting.
