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Extracting Dynamic Latent Feature with Bayesian Approaches for Process Data Analysis
- Author / Creator
- Ma, Yanjun
Data-driven approaches have been profoundly studied and successfully applied for process
industries, such as in the development of inferential sensors. Among a variety of modelling
techniques, the latent variable modelling approaches are widely preferred, which can learn
informative features from massive industrial data. In order to make latent variable models
more practical for process data analytics, the temporal correlations should be considered
in feature extraction. Following the probabilistic modelling procedure, dynamic models are
developed in this thesis to describe the latent feature. Besides several probability models,
novel inferencing algorithms are elaborated for different application scenarios.
In most chemical processes, features with large inertia and small varying velocity are
believed to be more informative. By imposing this modelling preferences as prior distributions
of model parameters, the first contribution of this thesis builds the dynamic latent
features under a fully Bayesian framework. The preference for large inertia is implemented
through a constraint and a prior distribution for the dynamic model of latent features,
namely the transition function. The consideration of regularization is implemented through
the generative model of raw process data, namely the observation function. Based on the
variational Bayesian inference, a novel learning method is developed to extract the slowly
varying features and learn model parameters.
The second contribution of this thesis forms a transition function for the constrained
latent features. As a hierarchical extension of the hidden Markov model, it describes a
dynamic model for the probabilities of discrete variables. By using the Beta distribution
to replace the Gaussian distribution, the novel transition function retained similar dynamic
characteristics in the constrained domain. The preferred region of transition parameters
can be determined for Bayesian inference. In this feature extraction model, a non-linear observation
function is used to learn the constrained feature from unconstrained observations,
where novel smoothing and marginalizing algorithms are created.
In the third contribution of this thesis, a more practical observation function is proposed
to extract dynamic features from multiple operating regions and outlier contaminated data. Specifically, multiple linear models are utilized to accommodate switching operation regions,
and a heavy-tailed noise distribution is used to improve robustness. In order to integrate
multiple observation models into the unied dynamic latent feature, a novel Bayesian state
estimation algorithm is developed. In its online application, the proposed method is also
extended to general multiple model state estimation.
In the fourth contribution of this thesis, another observation function is proposed, which
generalizes the ARMAX identification problem under the probabilistic framework. In this
work, the dynamic latent feature is used to represent the random (time-variant) time delay,
and the proposed Bayesian algorithm can solve the problem of parameter estimation and
time delay estimation jointly. In particular, the random time delay is studied for three
scenarios, where a static model, a hierarchical model, and a Markov model are developed.
With the consideration of temporal correlations, the Markov model provides better performance
for system identification. Besides, the hierarchical model also demonstrates its
effectiveness of modelling sequentially independent time delay.
The practicality of these proposed feature extraction models and inferencing algorithms
are verified using numerical examples, benchmark simulations, and case studies on industrial
data. Specifically, the application includes modelling the emulsion quality from the subsurface recovery process, modelling the steam quality from the steam generation process,
and a target tracking problem with multiple models.
- Graduation date
- Fall 2019
- Type of Item
- Doctor of Philosophy
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