Approach for Improved Signal-Based Fault Diagnosis of Hot Rolling Mills

Der hier vorgestellte Ansatz ist in der Lage, zwei spezifische schwere Fehler zu erkennen, sie zu identifizieren, zwischen vier verschiedenen Systemzuständen zu unterscheiden und eine Prognose bezüglich des Systemverhaltens zu geben. Die vorliegende Arbeit untersucht die Zustandsüberwachung des komplexen Herstellungsprozesses eines Warmbandwalzwerks. Eine signalbasierte Fehlerdiagnose und ein Fehlerprognoseansatz für den Bandlauf werden entwickelt. Eine Literaturübersicht gibt einen Überblick über die bisherige Forschung zu verwandten Themen. Es wird gezeigt, dass die große Anzahl vorheriger Arbeiten diese Thematik nicht gelöst hat und dass weitere Untersuchungen erforderlich sind, um eine zufriedenstellende Lösung der behandelten Probleme zu erhalten. Die Entwicklung einer neuen Signalverarbeitungskette und die Signalverarbeitungsschritte sind detailliert dargestellt. Die Klassifikationsaufgabe wird in Fehlerdiagnose, Fehleridentifikation und Fehlerprognose differenziert. Der vorgeschlagene Ansatz kombiniert fünf verschiedene Methoden zur Merkmalsextraktion, nämlich Short-Time Fourier Transformation, kontinuierliche Wavelet Transformation, diskrete Wavelet Transformation, Wigner-Ville Distribution und Empirical Mode Decomposition, mit zwei verschiedenen Klassifikationsalgorithmen, nämlich Support-Vektor Maschine und eine Variation der Kreuzkorrelation, wobei letztere in dieser Arbeit entwickelt wurde. Kombinationen dieser Merkmalsextraktion und Klassifikationsverfahren werden an Walzkraft-Daten aus einer Warmbreitbandstraße angewendet.The approach introduced here is able to detect two specific severe faults, to identify them, to distinguish between four different system states, and to give a prognosis on the system behavior. The presented work investigates the condition monitoring of the complex production process of a hot strip rolling mill. A signal-based fault diagnosis and fault prognosis approach for strip travel is developed. A literature review gives an overview about previous research on related topics. It is shown that the great amount of previous work does not cope with the problems treated in this work and that further investigation is necessary to provide a satisfactory solution. The design of a new signal processing chain is presented and the signal processing steps are detailed. The classification task is differentiated into fault detection, fault identification and fault prognosis. The proposed approach combines five different methods for feature extraction, namely short time Fourier transform, continuous wavelet transform, discrete wavelet transform, Wigner-Ville distribution, and empirical mode decomposition, with two different classification algorithms, namely support vector machine and a variation of cross-correlation, the latter developed in this work. Combinations of these feature extraction and classification methods are applied to rolling force data originating from a hot strip mill

Artificial Intelligence Approaches for Predictive Maintenance in the Steel Industry: A Survey

Predictive Maintenance (PdM) emerged as one of the pillars of Industry 4.0, and became crucial for enhancing operational efficiency, allowing to minimize downtime, extend lifespan of equipment, and prevent failures. A wide range of PdM tasks can be performed using Artificial Intelligence (AI) methods, which often use data generated from industrial sensors. The steel industry, which is an important branch of the global economy, is one of the potential beneficiaries of this trend, given its large environmental footprint, the globalized nature of the market, and the demanding working conditions. This survey synthesizes the current state of knowledge in the field of AI-based PdM within the steel industry and is addressed to researchers and practitioners. We identified 219 articles related to this topic and formulated five research questions, allowing us to gain a global perspective on current trends and the main research gaps. We examined equipment and facilities subjected to PdM, determined common PdM approaches, and identified trends in the AI methods used to develop these solutions. We explored the characteristics of the data used in the surveyed articles and assessed the practical implications of the research presented there. Most of the research focuses on the blast furnace or hot rolling, using data from industrial sensors. Current trends show increasing interest in the domain, especially in the use of deep learning. The main challenges include implementing the proposed methods in a production environment, incorporating them into maintenance plans, and enhancing the accessibility and reproducibility of the research.Comment: Preprint submitted to Engineering Applications of Artificial Intelligenc

Hot mill interstand model and practical applciations.

Hot rolling is a highly complex physical problem. The difficult geometry and the hot deformation behaviour of Carbon strip steels during hot rolling render this process difficult to investigate during normal operations or within the laboratory. Numerical models can therefore be used to further understanding of hot rolling with their ability to predict variables that are difficult or even impossible to measure during normal hot rolling operations. A numerical model has been developed using the commercial ABAQUS Finite-Element software package to consider the effect of process variables such as temperature and microstructural evolution with their consequential effects upon the mechanical behaviour of the strip within a seven stand commercial finishing mill. The roll-gap has been described as a thermal-mechanical coupled plane-strain problem with thermal and microstructural algorithms describing the interstand periods. The hot deformation characteristics of a high Carbon material have also been investigated using multi-deformation testing methods within the laboratory and numerically described using constitutive modelling techniques. The numerical results include multi-pass thermal predictions and the calculation of microstructural evolution between successive deformations for high Carbon, Carbon Manganese and low Carbon strip steels. Rolling parameters such as rolling loads have been predicted as functions of strain, strain rate, temperature and retained strain from previous deformations. Rolling forces and thermal results have been shown to be in reasonable agreement with measured data from trials at the Corus strip mills at Port Talbot and Llanwern, Wales, UK. The research programme has developed constitutive relationships for a high Carbon steel and demonstrated that coupled thermal-mechanical and microstructural algorithms can create sensitive and accurate numerical simulations of commercial hot rolling

Soluciones analíticas en teoría de retroalimentación cuantitativa: aplicación a un molino de laminación en caliente

En este trabajo se propone un procedimiento para el diseño de controladores robustos mediante técnicas analíticas usando teoría de retroalimentación cuantitativa (QFT, por sus siglas en ingles), dicho procedimiento puede ser aplicado a sistemas con incertidumbre paramétrica multilineal representados mediante subplantas con incertidumbre intervalo o afín. El procedimiento propuesto es implementado en dos ejemplos de aplicación, el primero es el control de velocidad en un motor de corriente continua y el segundo es el control para la salida de tensión de un molino de laminación en caliente MLC. También, se presenta el modelo multivariable en el dominio de la frecuencia y en el espacio de estados para un MLC, mostrando la función de transferencia para la salida de tensión σi representada mediante subplantas intervalo o afín, ya que se usa como un caso de estudio para el diseño de controladores QFT mediante técnicas analíticas. Por otro lado, se presentan los conceptos de plantillas analíticas, cotas analíticas y se propone un procedimiento para el ajuste de plantillas analíticas en plantillas discretas convexas y no convexas. Mas aun, se propone aproximar a las plantillas abiertas descritas por un arco, con curvas de Bezier dada su simplicidad. Asimismo, se presentan los procedimientos para operar plantillas analíticas, que en este trabajo se llevan a cabo usando las representaciones analíticas de la misma manera en que se operan los números complejos, ya que anteriormente se realizaban operaciones mediante puntos en el plano complejo de las plantillas discretas. A su vez, se presentan los procedimientos para calcular cotas analíticas, las cuales se representan mediante series de Fourier y se ajustan para incluir completamente al conjunto de curvas que las forman, calculando cotas analíticas para las funciones sensibilidad S(s), sensibilidad complementaria T (s) y seguimiento. Finalmente se comparan los resultados obtenidos con la técnica analítica y con la técnica clásica QFT comúnmente usada

Cognitive Control Systems in Steel Processing Lines for Minimised Energy Consumption and Higher Product Quality (Cognitive Control) : Final Report

The aim of Cognitive Control was to create cognitive automation systems with the capabilities automatic control performance monitoring (CPM), self-detection and automatic diagnosis of faults (sensors, actuators, controller) and self-adaptation in control system environments to optimise the product quality and minimise energy consumption in steel during the whole life cycle. In this project several software tools for online Control Performance Monitoring (CPM), monitoring energy efficiency, diagnosis of poor performance root-causes and control re-tuning for univariable and multivariable, linear and nonlinear processes were developed. The software tools were Graphical User Interface (GUI) that provided interface to access process data. The implemented methodologies were subsequently published as conference and journal papers. The methods were tested at hot strip mills, annealing furnaces and galvanizing lines

Book of Gobi, Book 3: Mountains and Desert

This book is the third in a series of books about the history of Siskiyou Smokejumper Base in Cave Junction, Oregon (a.k.a., Gobi). It consists of the written memories of those who worked at the base. The following is from the book\u27s introduction. The Book of Gobi, Book 3: Mountains and Desert is likely the last in this series. As the two previous books, this one continues to answer the what and the why of the Gobi. In so many of the stories throughout the three volumes, a young person arrives at the Gobi and undergoes a transformation, and in later years tries to explain what happened in those mountains and on that desert.https://dc.ewu.edu/smokejumping_pubs/1007/thumbnail.jp