For modeling and monitoring large-scale plant-wide processes with big data from multiple operating conditions, a novel distributed parallel Gaussian mixture model is proposed based on the Hadoop MapReduce framework. To deal with high-dimensional process variables, a multiblock method is adopted. For big data chunks in each divided block, an analytical procedure is carried out with three key procedures. First, the fundamental data statistics are obtained with the designed distributed and parallel manners for data standardization. Second, conventional Gaussian mixture model learning steps are accommodated in the parallel paradigm of the MapReduce platform. Finally, multilevel fault detection and diagnosis schemes are developed to conduct hierarchical monitoring from plant-wide, unit block, and variable levels. The feasibility and effectiveness of the proposed method are demonstrated on two study cases.

为了对来自多个操作条件的大数据进行大规模工厂范围内的过程建模和监控，提出了一种基于Hadoop MapReduce框架的新型分布式并行高斯混合模型。为了处理高维过程变量，采用了多块方法。对于每个分割块中的大数据块，使用三个关键过程执行分析过程。首先，通过设计的分布式和并行方式获得基本数据统计数据，以实现数据标准化。其次，将传统的高斯混合模型学习步骤包含在MapReduce平台的并行范式中。最后，开发了多级故障检测和诊断方案，以便从工厂范围，单元块和可变级别进行分层监视。