ArXiv Preprint
The development and progress in sensor, communication and computing
technologies have led to data rich environments. In such environments, data can
easily be acquired not only from the monitored entities but also from the
surroundings where the entity is operating. The additional data that are
available from the problem domain, which cannot be used independently for
learning models, constitute context. Such context, if taken into account while
learning, can potentially improve the performance of predictive models.
Typically, the data from various sensors are present in the form of time
series. Recurrent Neural Networks (RNNs) are preferred for such data as it can
inherently handle temporal context. However, the conventional RNN models such
as Elman RNN, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) in
their present form do not provide any mechanism to integrate explicit contexts.
In this paper, we propose a Context Integrated RNN (CiRNN) that enables
integrating explicit contexts represented in the form of contextual features.
In CiRNN, the network weights are influenced by contextual features in such a
way that the primary input features which are more relevant to a given context
are given more importance. To show the efficacy of CiRNN, we selected an
application domain, engine health prognostics, which captures data from various
sensors and where contextual information is available. We used the NASA
Turbofan Engine Degradation Simulation dataset for estimating Remaining Useful
Life (RUL) as it provides contextual information. We compared CiRNN with
baseline models as well as the state-of-the-art methods. The experimental
results show an improvement of 39% and 87% respectively, over state-of-the art
models, when performance is measured with RMSE and score from an asymmetric
scoring function. The latter measure is specific to the task of RUL estimation.
Rashmi Dutta Baruah, Mario Muñoz Organero
2023-01-12
