Process monitoring or fault detection and diagnosis have gained tremendous attention over the past decade in order to achieve better product quality, minimise downtime and maximise profit in process industries. Among various process monitoring techniques, data-based machine learning approaches have become immensely popular in the past decade. However, a promising machine learning technique Gaussian process regression has not yet received adequate attention for process monitoring. In this work, Gaussian process regression (GPR)-based process monitoring approach is applied to the benchmark Tennessee Eastman challenge problem. Effect of various GPR hyper-parameters on monitoring efficiency is also thoroughly investigated. The results of GPR model is found to be better than many other techniques which is reported in a comparative study in this work.

在过去的十年中，过程监控或故障检测与诊断已获得了极大的关注，以实现更好的产品质量，最小化停机时间并最大程度地提高过程工业的利润。在各种过程监控技术中，基于数据的机器学习方法在过去的十年中变得非常流行。但是，一种有前途的机器学习技术高斯过程回归尚未得到足够的重视，以进行过程监控。在这项工作中，将基于高斯过程回归（GPR）的过程监视方法应用于基准田纳西伊士曼挑战问题。还彻底研究了各种GPR超参数对监视效率的影响。发现GPR模型的结果比这项工作的比较研究中报道的许多其他技术要好。