Comparing statistical process control charts for fault detection in wastewater treatment

Abstract

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.

A Review of Control Charts and Exploring Their Utility for Regional Environmental Monitoring Programs

“…Control charting is well established in manufacturing [2] and has been adopted in many disciplines, including environmental monitoring, e.g., [97][98][99]. Control charts have been used in many environmental studies, including studies of water quality, air quality, sediment quality, and many others [21,30,37,41,72,[100][101][102][103][104][105][106][107][108][109][110][111]. Control charts have also been suggested and used as tools in conservation science [31,112], and the concept has been used in other work, e.g., [96,113].…”

Enhanced data-driven monitoring of wastewater treatment plants using the Kolmogorov–Smirnov test

“…In ref. 16, Marais et al evaluated the detection performance of Shewhart charts, CUSUM charts, and EWMA charts in detecting different sensor faults, including drift and bias, that occurred in WWTPs. The EWMA method outperforms the others, particularly for drift faults, showing low false alarms and efficient detection times.…”

Long-Term Statistical Process Monitoring of an Ultrafiltration Water Treatment Process

“…Finally, an important factor to consider when comparing different fault detection approaches is the length of time of the study. Most literature focuses on short periods of time, ,− often with hand-picked case studies of a few days or weeks. − However, monitoring methods are implemented for real-time usage over long periods of time, and short-term performance may not always translate to long-term detection ability. Changes in influent quality, equipment, or operation are expected to occur over time and can affect model performance.…”