Parameter Identification and Control Scheme for Monitoring Automatic Thickness Control System with Measurement Delay

The thickness of the steel strip is an important indicator of the overall strip quality. Deviations in thickness are primarily controlled using the automatic gauge control (AGC) system of each rolling stand. At the last stand, the monitoring AGC system is usually used, where the deviations in thickness can be directly measured by the X-ray thickness gauge device and used as the input to the AGC system. However, due to the physical distance between the thickness detection device and the rolling stand, time delay is unavoidably present in the thickness control loop, which can affect control performance and lead to system oscillations. Furthermore, the parameters of the system can change due to perturbations from external disturbances. Therefore, this paper proposes an identification and control scheme for monitoring AGC system that can handle time delay and parameter uncertainty. The cross-correlation function is used to estimate the time delay of the system, while the system parameters are identified using a recursive least squares method. The time delay and parameter estimates are then further refined using the Levenberg-Marquardt algorithm, so as to provide the most accurate parameter estimates for the complete system. Simulation results show that, compared with the standard Proportion Integration Differentiation (PID) controller approach, the proposed approach is not affected by changes in the time delay and parameter uncertainties

ESSE 2017. Proceedings of the International Conference on Environmental Science and Sustainable Energy

Environmental science is an interdisciplinary academic field that integrates physical-, biological-, and information sciences to study and solve environmental problems. ESSE - The International Conference on Environmental Science and Sustainable Energy provides a platform for experts, professionals, and researchers to share updated information and stimulate the communication with each other. In 2017 it was held in Suzhou, China June 23-25, 2017

Strip tracking in hot strip mills

In the finishing mill, steel strip is rolled from thick slabs through pairs of rollers housed in a continuous train of seven roll stands. As the strip is rolled, unwanted lateral movement, known as strip tracking, can cause the strip to collide with the edge of the mill. Strip tracking control is currently a manual operation, relying on the skill of the operators. When tracking is observed, the stand tilt is adjusted asymmetrically, causing a camber in the strip, steering it towards the centreline. Tracking control can be automated if a reliable measurement of position is available. A vision-based system was developed to measure strip position. Cooling water, steam, high temperatures and electrical noise create a hazardous environment for electronic equipment and hamper image analysis. Hardware was specified to protect all equipment against the environment. A novel image analysis method combining predictive elements, filtering and Bezier curve fitting was created to allow measurements to be made with large amounts of cooling water obscuring the strip edges. The measurement system was designed to integrate with the existing mill systems, using the OPC protocol for communication. The system was created as a development system with only two cameras included, but allowed for additional cameras to be easily added and automatically detected. The results of the system showed that the image analysis techniques were effective, providing an estimated final resolution of 3.5mm/pixel, with measurements ±2mm within 60% confidence. Hardware performance provided good protection of the equipment against the environment but poor quality installation limited overall system performance. A computer model was developed to simulate tracking behaviour in the mill with non-linear variations of strip properties across the strip. The model was not completed to a satisfactory standard capable of producing useful results but the theories described could be developed further.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Explainable Predictive Maintenance

Explainable Artificial Intelligence (XAI) fills the role of a critical interface fostering interactions between sophisticated intelligent systems and diverse individuals, including data scientists, domain experts, end-users, and more. It aids in deciphering the intricate internal mechanisms of ``black box'' Machine Learning (ML), rendering the reasons behind their decisions more understandable. However, current research in XAI primarily focuses on two aspects; ways to facilitate user trust, or to debug and refine the ML model. The majority of it falls short of recognising the diverse types of explanations needed in broader contexts, as different users and varied application areas necessitate solutions tailored to their specific needs. One such domain is Predictive Maintenance (PdM), an exploding area of research under the Industry 4.0 \& 5.0 umbrella. This position paper highlights the gap between existing XAI methodologies and the specific requirements for explanations within industrial applications, particularly the Predictive Maintenance field. Despite explainability's crucial role, this subject remains a relatively under-explored area, making this paper a pioneering attempt to bring relevant challenges to the research community's attention. We provide an overview of predictive maintenance tasks and accentuate the need and varying purposes for corresponding explanations. We then list and describe XAI techniques commonly employed in the literature, discussing their suitability for PdM tasks. Finally, to make the ideas and claims more concrete, we demonstrate XAI applied in four specific industrial use cases: commercial vehicles, metro trains, steel plants, and wind farms, spotlighting areas requiring further research.Comment: 51 pages, 9 figure

Strip tracking in hot strip mills

In the finishing mill, steel strip is rolled from thick slabs through pairs of rollers housed in a continuous train of seven roll stands. As the strip is rolled, unwanted lateral movement, known as strip tracking, can cause the strip to collide with the edge of the mill. Strip tracking control is currently a manual operation, relying on the skill of the operators. When tracking is observed, the stand tilt is adjusted asymmetrically, causing a camber in the strip, steering it towards the centreline. Tracking control can be automated if a reliable measurement of position is available. A vision-based system was developed to measure strip position. Cooling water, steam, high temperatures and electrical noise create a hazardous environment for electronic equipment and hamper image analysis. Hardware was specified to protect all equipment against the environment. A novel image analysis method combining predictive elements, filtering and Bezier curve fitting was created to allow measurements to be made with large amounts of cooling water obscuring the strip edges. The measurement system was designed to integrate with the existing mill systems, using the OPC protocol for communication. The system was created as a development system with only two cameras included, but allowed for additional cameras to be easily added and automatically detected. The results of the system showed that the image analysis techniques were effective, providing an estimated final resolution of 3.5mm/pixel, with measurements ±2mm within 60% confidence. Hardware performance provided good protection of the equipment against the environment but poor quality installation limited overall system performance. A computer model was developed to simulate tracking behaviour in the mill with non-linear variations of strip properties across the strip. The model was not completed to a satisfactory standard capable of producing useful results but the theories described could be developed further

Nonterrestrial utilization of materials: Automated space manufacturing facility

Four areas related to the nonterrestrial use of materials are included: (1) material resources needed for feedstock in an orbital manufacturing facility, (2) required initial components of a nonterrestrial manufacturing facility, (3) growth and productive capability of such a facility, and (4) automation and robotics requirements of the facility

Embedded Sensors and Controls to Improve Component Performance and Reliability Conceptual Design Report

The objective of this project is to demonstrate improved reliability and increased performance made possible by deeply embedding instrumentation and controls (I&C) in nuclear power plant (NPP) components and systems. The project is employing a highly instrumented canned rotor, magnetic bearing, fluoride salt pump as its I&C technology demonstration platform. I&C is intimately part of the basic millisecond-by-millisecond functioning of the system; treating I&C as an integral part of the system design is innovative and will allow significant improvement in capabilities and performance. As systems become more complex and greater performance is required, traditional I&C design techniques become inadequate and more advanced I&C needs to be applied. New I&C techniques enable optimal and reliable performance and tolerance of noise and uncertainties in the system rather than merely monitoring quasistable performance. Traditionally, I&C has been incorporated in NPP components after the design is nearly complete; adequate performance was obtained through over-design. By incorporating I&C at the beginning of the design phase, the control system can provide superior performance and reliability and enable designs that are otherwise impossible. This report describes the progress and status of the project and provides a conceptual design overview for the platform to demonstrate the performance and reliability improvements enabled by advanced embedded I&C