Future Energy Center, Mälardalen University, Västerås SE-721 23, Sweden E-mail:
Water Sci Technol. 2022 Feb;85(4):1250-1262. doi: 10.2166/wst.2022.037.
Fault detection is an important part of process supervision, especially in processes where there are strict requirements on the process outputs like in wastewater treatment. Statistical control charts such as Shewhart charts, cumulative sum (CUSUM) charts, and exponentially weighted moving average (EWMA) charts are common univariate fault detection methods. These methods have different strengths and weaknesses that are dependent on the characteristics of the fault. To account for this the methods in their base forms were tested with drift and bias sensor faults of different sizes to determine the overall performance of each method. Additionally, the faults were detected using two different sensors in the system to see how the presence of active process control influenced fault detectability. The EWMA method performed best for both fault types, specifically the drift faults, with a low false alarm rate and good detection time in comparison to the other methods. It was shown that decreasing the detection time can effectively reduce excess energy consumption caused by sensor faults. Additionally, it was shown that monitoring a manipulated variable has advantages over monitoring a controlled variable as set-point tracking hides faults on controlled variables; lower missed detection rates are observed using manipulated variables.
故障检测是过程监控的一个重要部分，特别是在过程输出有严格要求的情况下，如在污水处理中。统计控制图，如休哈特图、累积和（CUSUM）图和指数加权移动平均（EWMA）图，是常见的单变量故障检测方法。这些方法具有不同的优缺点，这取决于故障的特点。为了考虑到这一点，以不同大小的漂移和偏差传感器故障对这些基本形式的方法进行了测试，以确定每种方法的整体性能。此外，使用系统中的两个不同传感器来检测故障，以观察主动过程控制的存在如何影响故障检测能力。EWMA 方法对两种故障类型（特别是漂移故障）的性能最佳，与其他方法相比，它具有较低的误报率和良好的检测时间。结果表明，减少检测时间可以有效地减少传感器故障引起的额外能耗。此外，还表明监测操纵变量优于监测被控变量，因为设定值跟踪会掩盖被控变量上的故障；使用操纵变量可以观察到较低的漏检率。
