Causal Mechanism Transfer Network for Time Series Domain Adaptation in Mechanical Systems

10/13/2019
by   Zijian Li, et al.
0

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.

READ FULL TEXT
research
05/07/2022

Time-Series Domain Adaptation via Sparse Associative Structure Alignment: Learning Invariance and Variance

Domain adaptation on time-series data is often encountered in the indust...
research
12/22/2020

Time Series Domain Adaptation via Sparse Associative Structure Alignment

Domain adaptation on time series data is an important but challenging ta...
research
05/22/2020

Multi-Source Deep Domain Adaptation with Weak Supervision for Time-Series Sensor Data

Domain adaptation (DA) offers a valuable means to reuse data and models ...
research
09/19/2017

A textual transform of multivariate time-series for prognostics

Prognostics or early detection of incipient faults is an important indus...
research
07/17/2019

Remaining Useful Lifetime Prediction via Deep Domain Adaptation

In Prognostics and Health Management (PHM) sufficient prior observed deg...
research
02/07/2022

Structured Time Series Prediction without Structural Prior

Time series prediction is a widespread and well studied problem with app...
research
08/06/2023

Causal Disentanglement Hidden Markov Model for Fault Diagnosis

In modern industries, fault diagnosis has been widely applied with the g...

Please sign up or login with your details

Forgot password? Click here to reset