Home » Performance Monitoring Fundamentals: Demystifying Performance Assessment Techniques
Performance Monitoring Fundamentals: Demystifying Performance Assessment Techniques
Real-time performance monitoring to identify poorly or under-performing loops has become an integral part of preventative maintenance. Among others, rising energy costs and increasing demand for improved product quality are driving forces. Automatic process control solutions that incorporate real-time monitoring and performance analysis are fulfilling this market need. While many software solutions display performance metrics, however, it is important to understand the purpose and limitations of the various performance assessment techniques since each metric signifies very specific information about the nature of the process.
This paper reviews performance measures from simple statistics to complicated model-based performance criteria. By understanding the underlying concepts of the various techniques, readers will gain an understanding of the proper use of performance criteria. Basic algorithms for computing performance measures are presented using example data sets. An evaluation of techniques with tips and suggestions provides readers with guidance for interpreting the results.
Over the past two decades, process control performance monitoring software has become an important tool in the control engineer's toolbox. Still, the number of performance tests and statistics that can be calculated for any given control loop can be overwhelming. The problem with controller performance monitoring is not the lack of techniques and methods. Rather, the problem is the lack of guidance as to how to turn statistics into meaningful and actionable information that can be applied to improve performance.
The performance analysis techniques discussed in this paper are separated into three sections. The first section details methods for identifying process characteristics using batches of existing data. The second section outlines methods used for real-time or dynamic analysis of streaming process data. These are vital techniques for the timely identification and interpretation of changing process behavior and deteriorating loop performance. The third section outlines techniques that aid in the identification of interacting control loops.
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