Control Performance Monitoring Research Articles

QC Constellation: a cutting-edge solution for risk and patient-based quality control in clinical laboratories.

Clinical laboratories face limitations in implementing advanced quality control (QC) methods with existing systems. This study aimed to develop a web-based application to addresses this gap, and improve QC practices. QC Constellation, a web application built using Python 3.11, integrates various statistical QC modules. These include Levey-Jennings charts with Westgard rules, sigma-metric calculations, exponentially weighted moving average (EWMA) and cumulative sum (CUSUM) charts, and method decision charts. Additionally, it offers a risk-based QC section and a patient-based QC module aligning with modern QC practices. The codes and the web application links for QC Constellation were shared at https://github.com/hikmetc/QC_Constellation, and http://qcconstellation.com, respectively. Using synthetic data, QC Constellation demonstrated effective implementation of Levey-Jennings charts with user-friendly features like checkboxes for Westgard rules and customizable moving averages graphs. Sigma-metric calculations for hypothetical performance values of serum total cholesterol were successfully performed using allowable total error and maximum allowable measurement uncertainty goals, and displayed on method decision charts. The utility of the risk-based QC module was exemplified by assessing QC plans for serum total cholesterol, showcasing the application's capability in calculating risk-based QC parameters including maximum unreliable final patient results, risk management index, and maximum run size andoffering risk-based QC recommendations. Similarly, the patient-based QC and optimization modules were demonstrated using simulated sodium results. In conclusion, QC Constellation emerges as a pivotal tool for laboratory professionals, streamlining the management of quality control and analytical performance monitoring, while enhancing patient safety through optimized QC processes.

Design Optimization of Solar Power Inverter

Optimizing the design of solar power inverters aims to improve efficiency, dependability, and performance. Effective circuit design, component selection, and advanced power electronics design are all involved. Effective methods for trouble identification and heat control guarantee long-term dependability. Complex control algorithms and filtering techniques enable the generation of high-quality AC output. Different system sizes can be integrated thanks to scalability. Monitoring and communication features enable remote control and real-time performance monitoring. Safety features adhere to all relevant standards and laws. For the highest return on investment, cost-effective design selections balance performance and affordability. Check to see if any specific aspects, such as dependability, power quality, scalability, cost-effectiveness, or efficiency, need to be improved. data collection Collect all necessary data, such as details on solar panels, load specifications, grid standards, environmental factors, and any other details that might affect the design. Modelling and simulation an electronic or mathematical model of the solar power inverter system should be created. This model should be able to accurately represent the inverter's electrical properties, control structures, and operating behavior. Utilise simulation tools to assess the system's performance under various conditions. Utilizing optimization techniques, algorithms are used to discover the optimal design solution. These algorithms could be based on methods for machine learning, evolutionary algorithms, or mathematical optimization methods. Define the objective function and constraints of the optimization process. Iterative Refinement for Modify the design in response to the optimization's results. Iterate through the design exploration, performance evaluation, and optimization stages to reach a superior design solution. The parameters of the model are continuously updated and modified as required. Taken as alternate parameter is Air Conditioner, Fan, Iron Box, Laptop Charger, Microwave Oven, UPS, Cooler. Taken as evaluation parameter is Battery, Filter capacitor (C1), Filter inductor (L1), Switching Frequency, DC link Capacitor (C2) 2 nos.

Manifold-constrained trace ratio optimization for nonstationary process performance monitoring

Data-driven control performance monitoring (CPM) is becoming increasingly important in assessing and diagnosing changes in intricate industrial processes that are challenging to investigate with precise prior knowledge. The existing data-driven CPM methods mostly set covariance or other autocorrelation indexes intended to reflect the global deviation from benchmarks and subsequently diagnose the loops or variables responsible for these changes. However, as these methods assume stationary sequences, only steady-state fluctuation is assessed and diagnosed. This limits the application to industrial processes where transient processes are negligible. A comprehensive performance index integrating the transient performance and the steady-state performance is defined in this paper. The manifold constraint is investigated to mine the transient features so that it can be merged into the variance-based assessment framework. Moreover, to address the issue of information redundancy in non-orthogonal directions, which is present in most existing methods, we proposed a trace ratio-driven framework for enhancing the accuracy of assessment and diagnosis. A numerical example and a simulated industrial cascaded continuous stirred tank heater process are used to test the assessment results and demonstrate the effectiveness of the proposed diagnosis strategy.

The future of control of process systems

This paper provides a perspective on the major challenges and directions in academic process control research over the next 5–10 years, and its industrial implementation. Large-scale systems control and identification, nonlinear model-based and model-free control, and controller performance monitoring and diagnosis are discussed as major directions for future research, along with control technology and industry workforce challenges and opportunities.

Analyzing characteristic and modeling of high-temperature proton exchange membrane fuel cells with CO poisoning effect

High-temperature proton exchange membrane fuel cells (HT-PEMFC) have strong resistance to CO poisoning. However, the published CO poisoning models for HT-PEMFC are based on the finite element analysis method, which are difficult to use for the performance prediction and development of system control strategies due to a large amount of calculation. In the beginning, a semi-empirical model of HT-PEMFC is deduced based on the analysis of the CO poisoning characteristics. Then the key parameters of ohmic impedance and concentration polarization advance correction factor are obtained by fitting the polarization curves, dissociation and adsorption activation energy of H2 and CO are obtained by the calculation, respectively. The slope of the straight line segment in the ohmic polarization interval of the polarization curve is linearly related to ln[H2/CO]. The change of limiting current density is the main factor for the advance of concentration polarization. The fitted ohmic impedance varies with temperature and CO concentration, but changes little above 175 °C. CO coverage depends primarily on dissociation and adsorption activation energy of CO. Subsequently, the proposed model is validated with experimental data and shows high similarity. The model can be used for studying the systematic control strategy development and performance monitoring of HT-PEMFC.

Comparison of Single Control Loop Performance Monitoring Methods

Well-performing control loops have an integral role in efficient and sustainable industrial production. Control performance monitoring (CPM) tools are necessary to establish further process optimization and preventive maintenance. Data-driven, model-free control performance monitoring approaches are studied in this research by comparing the performance of nine CPM methods in an industrially relevant process simulation. The robustness of some of the methods is considered with varying fault intensities. The methods are demonstrated on a simulator which represents a validated state-space model of a supercritical carbon dioxide fluid extraction process. The simulator is constructed with a single-input single-output unit controller for part of the process and a combination of relevant faults in the industry are introduced into the simulation. Of the demonstrated methods, Kullback–Leibler divergence, Euclidean distance, histogram intersection, and Overall Controller Efficiency performed the best in the first simulation case and could identify all the simulated fault scenarios. In the second case, integral-based methods Integral Squared Error and Integral of Time-weighted Absolute Error had the most robust performance with different fault intensities. The results highlight the applicability and robustness of some model-free methods and construct a solid foundation in the application of CPM in industrial processes.

A data-driven LQG method for linear time-varying systems to monitor the controller performance in the batch process

PurposeThe flexibility of batch process enables its wide application in fine-chemical, pharmaceutical and semi-conductor industries, whilst its complexity necessitates control performance monitoring to ensure high operation efficiency. This paper proposes a data-driven approach to carry out controller performance monitoring within batch based on linear quadratic Gaussian (LQG) method.Design/methodology/approachA linear time-varying LQG method is proposed to obtain the joint covariance benchmark for the stochastic part of batch process input/output. From historical golden operation batch, linear time-varying (LTV) system and noise models are identified based on generalized observer Markov parameters realization.FindingsOpen/closed loop input and output data are applied to identify the process model as well as the disturbance model, both in Markov parameter form. Then the optimal covariance of joint input and output can be obtained by the LQG method. The Hotelling's Tˆ2 control chart can be established to monitor the controller.Originality/value(1) An observer Markov parameter approach to identify the time-varying process and noise models from both open and closed loop data, (2) a linear time-varying LQG optimal control law to obtain the optimal benchmark covariance of joint input and output and (3) a joint input and output multivariate control chart based on Hotelling's T2 statistic for controller performance monitoring.

Building a transdisciplinary expert consensus on the cognitive drivers of performance under pressure: An international multi-panel Delphi study.

The ability to perform optimally under pressure is critical across many occupations, including the military, first responders, and competitive sport. Despite recognition that such performance depends on a range of cognitive factors, how common these factors are across performance domains remains unclear. The current study sought to integrate existing knowledge in the performance field in the form of a transdisciplinary expert consensus on the cognitive mechanisms that underlie performance under pressure. International experts were recruited from four performance domains [(i) Defense; (ii) Competitive Sport; (iii) Civilian High-stakes; and (iv) Performance Neuroscience]. Experts rated constructs from the Research Domain Criteria (RDoC) framework (and several expert-suggested constructs) across successive rounds, until all constructs reached consensus for inclusion or were eliminated. Finally, included constructs were ranked for their relative importance. Sixty-eight experts completed the first Delphi round, with 94% of experts retained by the end of the Delphi process. The following 10 constructs reached consensus across all four panels (in order of overall ranking): (1) Attention; (2) Cognitive Control-Performance Monitoring; (3) Arousal and Regulatory Systems-Arousal; (4) Cognitive Control-Goal Selection, Updating, Representation, and Maintenance; (5) Cognitive Control-Response Selection and Inhibition/Suppression; (6) Working memory-Flexible Updating; (7) Working memory-Active Maintenance; (8) Perception and Understanding of Self-Self-knowledge; (9) Working memory-Interference Control, and (10) Expert-suggested-Shifting. Our results identify a set of transdisciplinary neuroscience-informed constructs, validated through expert consensus. This expert consensus is critical to standardizing cognitive assessment and informing mechanism-targeted interventions in the broader field of human performance optimization.

Controller Performance Monitoring: A Survey of Problems and a Review of Approaches from a Data-Driven Perspective with a Focus on Oscillations Detection and Diagnosis

Controller Performance Monitoring: A Survey of Problems and a Review of Approaches from a Data-Driven Perspective with a Focus on Oscillations Detection and Diagnosis

Modeling-on-demand-based multivariable control performance monitoring

The current work presents a modeling-on-demand-based multivariable control performance monitoring (CPM) strategy. The suggested approach addresses several challenging topics in multi input-multi output CPM area such as data-driven characteristics, recursive/adaptive philosophy, modeling, plant-wide control (PWC) performance/feasibility indicator, simple structure, minimum interference with the process operation and recovering actions suggestions. The new model-on-demand (MoD) algorithm combines partial least squares (block-wise and moving-window), model quality index, and the reference matrix concepts to improve the robustness characteristics of the recursive process, i.e. the model adaptation is only performed when it is needed. Recovery actions may involve changes on tuning parameters as well as control structure modifications. The proposed strategy allows to identify static and dynamic abnormal events in the process which could produce strong degradation of the PWC performance. The robustness of the proposed methodology is tested by dynamic simulations on the well-known Shell fractionator process and compared with some classical recursive algorithms.

Robust Control Performance Monitoring for Varying-Dimensional Time-Series Data Based on SCADA Systems

The supervisory control and data acquisition (SCADA) system provides information that can be used to free humans from laborious monitoring tasks, such as control performance monitoring (CPM). However, the existing CPM methods rely heavily on the quality of SCADA data. In practice, the missing of measurement and computed signals due to some random and man-induced factors will lead to failures of traditional CPM methods. This paper develops a robust CPM model for varying-dimensional time-series data resulting from the missing variables in SCADA systems. Two attractive advantages of the proposed model are noticed. First, SCADA data with various variable dimensions and missing patterns can be handled through a structural feature extraction module, which constructs specific graphs for input data and explicitly explores the inherent interaction mechanism among variables. A structural vector is then generated to characterize the interaction pattern of multiple variables. Second, the proposed model is designed with the generalization ability by developing parameters-shared node-effect and edge-effect graph neural networks. In this way, the method shows good robustness to the previously unseen missing patterns. Experiments on the simulated and real datasets demonstrate the feasibility of this method.

A Hierarchical Approach to Monitoring Control Performance and Plant-Model Mismatch

Controllers are often tuned during plant commissioning, with a fixed process model. However, over time degradation can occur in the process, the process model and the controller, making it necessary to either re-tune the controller or re-identify the process model. Authors have proposed a variety of approaches to identify plant-model mismatch (PMM) and control performance degradation (CPD). While each approach may have its own advantages and disadvantages, they are generally designed to function on different timescales. The differing timescales result in the need for a multi-level hierarchical approach to monitor, detect, and manage PMM and CPD, as illustrated through a continuous pharmaceutical manufacturing application, i.e., a direct compression tablet manufacturing process. This work also highlights the requirement for index-based metrics, that enable the impact of PMM and CPD to be quantified and assessed from a control performance monitoring perspective, to aid fault diagnosis through root cause analysis to guide maintenance decisions for continuous manufacturing applications.

The AIUM Practice Parameter for the Performance of an Ultrasound Examination of the Extracranial Cerebrovascular System.

The AIUM Practice Parameter for the Performance of an Ultrasound Examination of the Extracranial Cerebrovascular System.

The AIUM Practice Parameter for the Performance of an Ultrasound Examination of the Neonatal and Infant Spine.

The AIUM Practice Parameter for the Performance of an Ultrasound Examination of the Neonatal and Infant Spine.

The AIUM Practice Parameter for the Performance of an Ultrasound Examination of the Abdomen and/or Retroperitoneum.

The AIUM Practice Parameter for the Performance of an Ultrasound Examination of the Abdomen and/or Retroperitoneum.

Control performance monitoring and degradation recovery in automatic control systems: A review, some new results, and future perspectives

This paper addresses control performance monitoring (CPM) and degradation recovering in automatic control systems. It begins with a re-visit of CPM techniques and a summary of the major limitations of the existing CPM methods. They are (i) deficit in assessing control performance degradation caused by different types of disturbances and environment uncertainties, (ii) incapability for predicting performance degradation, and (iii) deficiency of efficient performance degradation recovering methods. In order to meet increasing demands of next generation automatic control systems for higher system performance, novel CPM methods have been developed in recent years, including performance assessment of control systems with deterministic disturbances and uncertainties, prediction of control performance degradation, and recovery of control performance degradation. Some of these methods and algorithms are introduced in the second part of this paper. The basis of these methods is a so-called residual centred model of feedback control systems, which allows a unified handling of control, monitoring and diagnosis in feedback control systems corrupted by disturbances and uncertainties. The focuses of these methods are on (i) introduction of the loop performance degradation index for the assessment and prediction of performance degradation in automatic control systems, (ii) predictive detection and estimation of loop performance degradation, and (iii) a data-driven performance degradation recovering scheme. The paper is concluded by a short summary of three future perspective topics, (i) prediction of economic system performance monitoring and estimation, (ii) reinforcement learning aided system performance recovery, and (iii) CPM digital twin.

AIUM Practice Parameter for the Performance of Sonohysterography and Hysterosalpingo-Contrast Sonography.

AIUM Practice Parameter for the Performance of Sonohysterography and Hysterosalpingo-Contrast Sonography.

AIUM Practice Parameter for the Performance of Ultrasound Evaluations of the Prostate (and Surrounding Structures).

AIUM Practice Parameter for the Performance of Ultrasound Evaluations of the Prostate (and Surrounding Structures).

AIUM Practice Parameter for the Performance of Duplex Sonography of Native Renal Vessels

AIUM Practice Parameter for the Performance of Duplex Sonography of Native Renal Vessels

AIUM Practice Parameter for the Performance of an Ultrasound Examination of Solid-Organ Transplants.

AIUM Practice Parameter for the Performance of an Ultrasound Examination of Solid-Organ Transplants.