The current state of control loop performance monitoring – A survey of application in industry

Bauer, Margret; Horch, Alexander; Xie, Lei; Jelali, Mohieddine; Thornhill, Nina F.

doi:10.1016/j.jprocont.2015.11.002

Cited by 125 publications

(74 citation statements)

References 10 publications

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“…1. fusion CPA measures using sensor combination [109] or the Exponentially Weighted Moving Averages (EWMA) evaluated for other indexes [110]; 2. graphic visualization and patter recognition methods [91,111,112]; and 3. case-specific business Key Performance Indicators (KPIs), e.g., number of alarms or human interventions, time in manual mode [113], and many other currency-based units [67].…”

Section: Data-driven Methodsmentioning

confidence: 99%

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Performance Assessment of Predictive Control—A Survey

Domański

2020

Algorithms

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Model Predictive Control constitutes an important element of any modern control system. There is growing interest in this technology. More and more advanced predictive structures have been implemented. The first applications were in chemical engineering, and now Model Predictive Control can be found in almost all kinds of applications, from the process industry to embedded control systems or for autonomous objects. Currently, each implementation of a control system requires strict financial justification. Application engineers need tools to measure and quantify the quality of the control and the potential for improvement that may be achieved by retrofitting control systems. Furthermore, a successful implementation of predictive control must conform to prior estimations not only during commissioning, but also during regular daily operations. The system must sustain the quality of control performance. The assessment of Model Predictive Control requires a suitable, often specific, methodology and comparative indicators. These demands establish the rationale of this survey. Therefore, the paper collects and summarizes control performance assessment methods specifically designed for and utilized in predictive control. These observations present the picture of the assessment technology. Further generalization leads to the formulation of a control assessment procedure to support control application engineers.

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Section: Data-driven Methodsmentioning

confidence: 99%

“…Industrial control systems frequently do not perform effectively [65] due to many reasons, for instance: inadequate supervision, process non-stationarity, instrumentation failures, incorrect design, feeble tuning, changing operating points, lack of engineering expertise, disturbances, noises, or human factor [66,67].…”

Section: Control Performance Assessmentmentioning

confidence: 99%

Performance Assessment of Predictive Control—A Survey

Domański

2020

Algorithms

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“…A recently conducted survey showed that this subject has been a major research topic for the last 25 years and will stay important in the future due to its important role and benefits in the industry, more details can be found in [10].…”

Section: Loop Performance Monitoringmentioning

confidence: 99%

Towards autonomous synthesis of building substation control loops

Birk

Atta

2016

2016 IEEE International Conference on Smart Grid Communications (SmartGridComm)

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Abstract-The main contribution of this paper is to study the feasibility of an automated approach to the assessment and synthesis of control loops in substations of a district heating and cooling system. The low-level controllers in substation are usually PID-type controllers and can not easily be changed. Moreover, the tuning of these controllers is often done in an ad-hoc manner and remains in the initial tuning despite changes in the substation over their operational life. Consequently, insufficient control performance due to false tuning can be misinterpreted as a hardware issue and lead to unnecessary replacement of parts.In order to solve this problem, the paper proposes an approach to assess the control loop performance based on logged data from substations, and to subsequently tune the current substation controllers, when no hardware issues are detected. The approach is applied to a well-known building substation model in simulation, which indicates that the approach is feasible.Analysis of acquired data from a number of substations in the DHC system of Luleå Energi AB, provides evidence for the problem and how the approach can be applied in its future implementation. Moreover, a number of challenges and future work are indicated.

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“…Control loop performance degradation occurred in up to 60 % of control loops in the process industry according to a report conducted by the Honeywell process solution . Since hundreds, and even thousands, of control loops are deployed in the process industry, such as in oil refineries, paper mills, etc., it is costly to monitor these control loops with personnel; therefore, online control loop performance monitoring (CPM) technology has been an urgent demand …”

Section: Introductionmentioning

confidence: 99%

Control loop performance monitoring based on weighted permutation entropy and control charts

Guo

Duan

et al. 2018

Can J Chem Eng

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The simple calculated permutation entropy is an intuitive index to measure the complexity of a time series by a comparison of neighbouring values, therefore, it can be used to detect dynamical changes in a time series. However, in permutation entropy, the amplitude information of a time series is ignored. By incorporating the variance information, weighted permutation entropy (WPE) can derive stable results, and enable the detection of abrupt changes in a time series. In this paper, weighted permutation entropy is employed to build the performance index from the closed loop output time series to monitor the control loop performance. Furthermore, two control charts are established to define the control limits for sample estimations of the WPE-based performance index. In this study, the Shewhart control chart and the exponentially weighted moving average (EWMA) control chart are integrated to develop two control performance monitoring schemes, Shewhart-WPE and EWMA-WPE. In addition, a numerical simulation is used to illustrate the ability and effectiveness of the developed Shewhart-WPE scheme. The proposed EWMA-WPE scheme is applied to monitor a natural gas pipeline transportation pressure control loop. The effectiveness of the proposed Shewhart-WPE and EWMA-WPE schemes is verified by the results.

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The current state of control loop performance monitoring – A survey of application in industry

Cited by 125 publications

References 10 publications

Performance Assessment of Predictive Control—A Survey

Performance Assessment of Predictive Control—A Survey

Towards autonomous synthesis of building substation control loops

Control loop performance monitoring based on weighted permutation entropy and control charts

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