论文标题
一个基于因果关系的多模式多变量时间序列验证，通过无监督的深度学习增强了行业4.0的推动者。
A Causal-based Framework for Multimodal Multivariate Time Series Validation Enhanced by Unsupervised Deep Learning as an Enabler for Industry 4.0
论文作者
论文摘要
多模式多变量时间序列的高级概念验证框架定义了从单变量上下文定义到与工业过程相关的异构数据（图像，时间序列，声音等），从单变量上下文定义到由自动编码器学到的多模式抽象上下文表示的多层次异常检测。框架的每个级别都适用于历史数据和/或实时数据。最终级别基于因果发现，以识别观察数据中的因果关系，以将偏置数据排除到训练机器学习模型，并向域专家提供手段，以发现数据样本表示的基本过程中未知的因果关系。在多变量时间序列上成功评估了长期的短期内存自动编码器，以验证与爆炸炉多个资产相关的抽象上下文的学习表示。将研究路线图确定为将因果发现和表示学习结合在一起，作为应用于过程行业的无监督根本原因分析的推动者。
An advanced conceptual validation framework for multimodal multivariate time series defines a multi-level contextual anomaly detection ranging from an univariate context definition, to a multimodal abstract context representation learnt by an Autoencoder from heterogeneous data (images, time series, sounds, etc.) associated to an industrial process. Each level of the framework is either applicable to historical data and/or live data. The ultimate level is based on causal discovery to identify causal relations in observational data in order to exclude biased data to train machine learning models and provide means to the domain expert to discover unknown causal relations in the underlying process represented by the data sample. A Long Short-Term Memory Autoencoder is successfully evaluated on multivariate time series to validate the learnt representation of abstract contexts associated to multiple assets of a blast furnace. A research roadmap is identified to combine causal discovery and representation learning as an enabler for unsupervised Root Cause Analysis applied to the process industry.
