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.
翻译:传感器、通信和计算技术的发展和进步导致数据丰富的环境。在这样的环境中,不仅可以从被监测的实体中,而且可以从实体运作的周围环境中轻易地获取数据。从问题域中可获得的额外数据,不能独立用于学习模型,构成背景。如果在学习过程中考虑到这些背景,则有可能改进预测模型的性能。一般而言,各种传感器的数据以时间序列的形式出现。经常神经网络(RNN)更适合用于这类数据,因为它本身可以处理时间背景。然而,传统的RNN模型,如Elman RNN、长期短期内存(LSTM)和Ged 经常股(GRU)等常规 RNNN模型,目前的形式无法提供任何机制,用于整合明确背景模型。在本文中,我们提出一个背景模型综合 RNN(CIRNNN) 数据,网络的权重因环境特征而受到影响,因此,与特定背景环境背景中的主要输入特征更加相关。但是,对一个背景模型进行测量的重要性更大。为了显示一个用于SIMR IM RUR 的特定数据,我们所选的SIMR 的运行数据,我们所选的SIM 的SIM 的SIM 的运行数据工具的运行中的数据工具的性数据是用来显示一个特定的系统。