Practical industrial processes usually have nonstationary properties, which make the monitoring more challenging because the fault information may be buried by nonstationary trends. For nonstationary processes, many methods have been proposed for fault detection based on continuous variables. However, binary variables may appear together with continuous variables in modern industrial processes. To address the issue of process monitoring with hybrid variables and nonstationarity, a model named recursive hybrid variable monitoring (RHVM) is proposed in this paper. For RHVM, recursive strategy is utilized to suppress nonstationary trend and to reveal fault information. In addition, RHVM has the ability of model self-updating with arriving samples. The closed-form updates of required parameters are derived in detail and the improvement of performance is analyzed. At last, the superiority of the proposed model is demonstrated by a simulation example and a practical nonstationary process of a power plant.

中文翻译：

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用于非平稳工业过程中故障检测的递归混合变量监控

实际的工业过程通常具有非平稳特性，这使得监控更具挑战性，因为故障信息可能被非平稳趋势所掩盖。对于非平稳过程，已经提出了许多基于连续变量的故障检测方法。然而，在现代工业过程中，二元变量可能与连续变量一起出现。针对混合变量和非平稳过程监控的问题，本文提出了一种递归混合变量监控（RHVM）模型。对于RHVM，利用递归策略抑制非平稳趋势并揭示故障信息。此外，RHVM 具有模型自更新与到达样本的能力。详细推导了所需参数的闭式更新，并分析了性能的提升。最后通过仿真算例和电厂的实际非平稳过程证明了所提模型的优越性。