  Data-driven models are becoming essential parts in modern mechanical systems,
commonly used to capture the behavior of various equipment and varying
environmental characteristics. Despite the advantages of these data-driven
models on excellent adaptivity to high dynamics and aging equipment, they are
usually hungry to massive labels over historical data, mostly contributed by
human engineers at an extremely high cost. The label demand is now the major
limiting factor to modeling accuracy, hindering the fulfillment of visions for
applications. Fortunately, domain adaptation enhances the model generalization
by utilizing the labelled source data as well as the unlabelled target data and
then we can reuse the model on different domains. However, the mainstream
domain adaptation methods cannot achieve ideal performance on time series data,
because most of them focus on static samples and even the existing time series
domain adaptation methods ignore the properties of time series data, such as
temporal causal mechanism. In this paper, we assume that causal mechanism is
invariant and present our Causal Mechanism Transfer Network(CMTN) for time
series domain adaptation. By capturing and transferring the dynamic and
temporal causal mechanism of multivariate time series data and alleviating the
time lags and different value ranges among different machines, CMTN allows the
data-driven models to exploit existing data and labels from similar systems,
such that the resulting model on a new system is highly reliable even with very
limited data. We report our empirical results and lessons learned from two
real-world case studies, on chiller plant energy optimization and boiler fault
detection, which outperforms the existing state-of-the-art method.
