# Comparison of Single Control Loop Performance Monitoring Methods
## 1 Introduction  
In industrial applications, processes are automatically controlled for the purposes of increasing production efficiency and reducing wasted resources. Many processes also require continuous control to stay within the operational limits. Well-performing control loops have an integral role in these tasks. However, the control loops require regular maintenance for keeping up with disturbances and decay present in industrial applications. Thus, the effectiveness of each control loop should be monitored to identify the maintenance needs. The primary objective of control loop monitoring is to identify the control loops with inadequate performance. For this aim, a plethora of performance estimation methods is available in the current literature [1; 2; 3]. A well-performing control loop creates a solid foundation for further process optimization and preventive maintenance. Poorly performing control loops may be caused by normal process deterioration over time or by disturbances and failures in sensors, controllers, actuators, and the process itself. In a performed analysis [4], some of the most common issues for control, process, and signal processing include manually overridden loops, control element out of range, and step outor quantization. In [5], the most common faults according to control engineers in the industry comprised controller saturation, oscillations, manual control, sluggish behavior, and quantization. The most applied automatic control strategy is the proportional, integral, and derivative controller (PID controller). PID control loops are usually tuned at the time of installation but could receive less attention in the continuous maintenance work. This could result in poor control and consequently declining process performance over time. Control loop performance monitoring tools are widely used in industry. In a survey [5], it was found that approximately two-thirds of control engineers use control performance monitoring (CPM) tools or packages. The use of CPM tools has been on the rise and automation companies provide solutions for use in industrial plants. Some companies may develop their own internal solutions for control performance monitoring. For example, ABB developed a control loop performance monitoring application, ServicePort, which allows for the monitoring of plant-wide control loop performance and provides an automated procedure for disturbance analysis [4]. Control performance measurement methods can be categorized based on the method's required a priori knowledge [1]; model-based methods require modeling of the monitored process and utilize the model as a reference to assess the current control loop performance. Model-free methods require no initial knowledge of the process but are instead based on the data collected during process operation. In this paper, data-driven, model-free approaches are prioritized for the purpose of obtaining easily adaptable methodologies. Generally, the methods should be applicable to an industrial plant, where the modeling of countless numbers of sub-processes would require immense effort. Some commercial products are founded on similar aims. Non-invasiveness, utilization of existing sensors, minimal process knowledge, and simple algorithms are demanded from control loop performance monitoring tools [6]. Many of the demonstrated methods have been widely used in control loop tuning applications, and this work further utilizes these methods in a dynamic performance monitoring application. Machine learning and deep learning methods may also be used for control performance monitoring purposes [7; 8]; however, the training and validation of these methods may prove impractical in industrial applications with countless numbers of control loops. Thus, this work focuses on easily applicable model-free methods. This work evaluates the applicability of several conventional and machine learning, model-free CPM methods on a simulated dynamic process.