  Automating the monitoring of industrial processes has the potential to
enhance efficiency and optimize quality by promptly detecting abnormal events
and thus facilitating timely interventions. Deep learning, with its capacity to
discern non-trivial patterns within large datasets, plays a pivotal role in
this process. Standard deep learning methods are suitable to solve a specific
task given a specific type of data. During training, deep learning demands
large volumes of labeled data. However, due to the dynamic nature of the
industrial processes and environment, it is impractical to acquire large-scale
labeled data for standard deep learning training for every slightly different
case anew. Deep transfer learning offers a solution to this problem. By
leveraging knowledge from related tasks and accounting for variations in data
distributions, the transfer learning framework solves new tasks with little or
even no additional labeled data. The approach bypasses the need to retrain a
model from scratch for every new setup and dramatically reduces the labeled
data requirement. This survey first provides an in-depth review of deep
transfer learning, examining the problem settings of transfer learning and
classifying the prevailing deep transfer learning methods. Moreover, we delve
into applications of deep transfer learning in the context of a broad spectrum
of time series anomaly detection tasks prevalent in primary industrial domains,
e.g., manufacturing process monitoring, predictive maintenance, energy
management, and infrastructure facility monitoring. We discuss the challenges
and limitations of deep transfer learning in industrial contexts and conclude
the survey with practical directions and actionable suggestions to address the
need to leverage diverse time series data for anomaly detection in an
increasingly dynamic production environment.
