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

Closed-loop control of product properties in metal forming

Metal forming processes operate in conditions of uncertainty due to parameter variation and imperfect understanding. This uncertainty leads to a degradation of product properties from customer specifications, which can be reduced by the use of closed-loop control. A framework of analysis is presented for understanding closed-loop control in metal forming, allowing an assessment of current and future developments in actuators, sensors and models. This leads to a survey of current and emerging applications across a broad spectrum of metal forming processes, and a discussion of likely developments.Engineering and Physical Sciences Research Council (Grant ID: EP/K018108/1)This is the final version of the article. It first appeared from Elsevier via https://doi.org/10.1016/j.cirp.2016.06.00

Electromagnetic measurements of steel phase transformations

This thesis describes the development of electromagnetic sensors to measure the phase transformation in steel as it cools from the hot austenite phase to colder ferritic based phases. The work initially involved investigating a variety of sensing configurations including ac excited coils, C-core arrangements and the adaptation of commercial eddy current proximity sensors. Finally, two prototype designs were built and tested on a hot strip mill. The first of these, the T-meter was based on a C-shaped permanent magnet with a Gaussmeter measuring the magnetic field at the pole ends. Laboratory tests indicated that it could reliably detect the onset of transformation. However, the sensor was sensitive to both the steel properties and the position of the steel. To overcome this, an eddy current sensor was incorporated into the final measurement head. The instrument gave results which were consistent with material property variations, provided the lift off variations were below 3Hz. The results indicated that for a grade 1916 carbon- manganese steel, the signal variation was reduced from 37% to 2%, and the resulting output was related to the steel property variations. The second of these prototypes was based on a dc electromagnetic E-core, with Hall probes in each of the three poles. 'Cold' calibration tests were used to decouple the steel and the lift-off. The results indicated that there was an error of 3-4% ferrite/mm at high ferrite fractions. At lower fractions the error was higher due to the instrument’s insensitivity to lift-off. The resulting output again showed a relationship with varying steel strip properties. ft was also shown that a finite element model could be calibrated to experimental results for a simple C-core geometry such that the output was sensitive to 0.2% of the range. This is required to simulate the sensor to resolve to 10% ferrite

Hot mill process parameters impacting on hot mill tertiary scale formation.

For high end steel applications surface quality is paramount to deliver a suitable product. A major cause of surface quality issues is from the formation of tertiary scale. The scale formation depends on numerous factors such as thermo-mechanical processing routes, chemical composition, thickness and rolls used. This thesis utilises a collection of data mining techniques to better understand the influence of Hot Mill process parameters on scale formation at Port Talbot Hot Strip Mill in South Wales. The dataset to which these data mining techniques were applied was carefully chosen to reduce process variation. There are several main factors that were considered to minimise this variability including time period, grade and gauge investigated. The following data mining techniques were chosen to investigate this dataset: Partial Least Squares (PLS); Logit Analysis; Principle Component Analysis (PCA); Multinomial Logistical Regression (MLR); Adaptive Neuro Inference Fuzzy Systems (ANFIS). The analysis indicated that the most significant variable for scale formation is the temperature entering the finishing mill. If the temperature is controlled on entering the finishing mill scale will not be formed. Values greater than 1070 °C for the average Roughing Mill and above 1050 °C for the average Crop Shear temperature are considered high, with values greater than this increasing the chance of scale formation. As the temperature increases more scale suppression measures are required to limit scale formation, with high temperatures more likely to generate a greater amount of scale even with fully functional scale suppression systems in place. Chemistry is also a significant factor in scale formation, with Phosphorus being the most significant of the chemistry variables. It is recommended that the chemistry specification for Phosphorus be limited to a maximum value of 0.015 % rather than 0.020 % to limit scale formation. Slabs with higher values should be treated with particular care when being processed through the Hot Mill to limit scale formation

Property prediction of continuous annealed steels

To compete in the current economic climate steel companies are striving to reduce costs and tighten process windows. It was with this in mind that a property prediction model for continuous annealed steels produced at Tata Steel’s plants in South Wales was developed. As continuous annealing is one of the final processes that strip steel undergoes before being dispatched to the customer the final properties of the strip are dependent on many factors. These include the annealing conditions, previous thermo-­‐mechanical processing and the steel chemistry. Currently these properties, proof stress, ultimate tensile strength, elongation, strain ratio and strain hardening exponent, are found using a tensile test at the tail end of the coil. This thesis describes the development of a model to predict the final properties of continuous annealed steel. Actual process data along with mechanical properties derived using tensile testing were used to create the model. A generalised regression network was used as the main predictive mechanism. The non-­‐linear generalised regression approach was shown to exceed the predictive accuracy of multiple regression techniques. The use of a genetic algorithm to reduce the number of inputs was shown to increase the accuracy of the model when compared to those trained with all available inputs and those trained using correlation derived inputs. Further work is shown where the fully trained models were used to predict the relationships that exist between the processing conditions and mechanical properties. This was extended to predict the interaction between two process conditions varying at the same time. Using this approach produced predictions that mirrored known relationships within continuous annealed steels and gives predictions specific to the plant that could be used to optimise the process.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Property prediction of continuous annealed steels

To compete in the current economic climate steel companies are striving to reduce costs and tighten process windows. It was with this in mind that a property prediction model for continuous annealed steels produced at Tata Steel’s plants in South Wales was developed. As continuous annealing is one of the final processes that strip steel undergoes before being dispatched to the customer the final properties of the strip are dependent on many factors. These include the annealing conditions, previous thermo-­‐mechanical processing and the steel chemistry. Currently these properties, proof stress, ultimate tensile strength, elongation, strain ratio and strain hardening exponent, are found using a tensile test at the tail end of the coil. This thesis describes the development of a model to predict the final properties of continuous annealed steel. Actual process data along with mechanical properties derived using tensile testing were used to create the model. A generalised regression network was used as the main predictive mechanism. The non-­‐linear generalised regression approach was shown to exceed the predictive accuracy of multiple regression techniques. The use of a genetic algorithm to reduce the number of inputs was shown to increase the accuracy of the model when compared to those trained with all available inputs and those trained using correlation derived inputs. Further work is shown where the fully trained models were used to predict the relationships that exist between the processing conditions and mechanical properties. This was extended to predict the interaction between two process conditions varying at the same time. Using this approach produced predictions that mirrored known relationships within continuous annealed steels and gives predictions specific to the plant that could be used to optimise the process

Thermomechanical simulation and process optimization for hot rolling of steel

Hot rolling is a manufacturing process that involves large material deformation, complicated geometries, contact conditions and non-uniform temperature gradients. Steel industries are motivated to prevent hot rolled steel products to be defect free and with desired shape and size. In order to simulate the process accurately, it is essential that the material model for steel accounts for the viscoplasticity and changes in properties that occur in steel at elevated temperatures as grain growth and recrystallization. The healing of existing voids during hot rolling was investigated using finite element simulations. Voids are highly undesirable as they not only degrade the product quality but also serve to initiate cracks and fissures. During rolling most of the voids are expected to close due to deformation of the rolled material at high temperature. The influence of various rolling parameters on void closure were predicted using simulations. During multi-pass hot rolling of steel microstructural changes occur due recrystallization and temperature. Elevated temperatures result in grain coarsening, while recrystallization triggers grain refinement. The parameters governing the static recrystallization kinetics were determined using the double hit compression test. Various steel grades were characterized to determine the change in grain size at elevated temperature. The effect of grain size on the flow stress was also found using a set of experiments. These findings helped to create a new plasticity model based on the classical Johnson-Cook model that included the influence of grain size on the flow stress and static softening due to recrystallization --Abstract, page iv

Data mining for fault diagnosis in steel making process under industry 4.0

The concept of Industry 4.0 (I4.0) refers to the intelligent networking of machines and processes in the industry, which is enabled by cyber-physical systems (CPS) - a technology that utilises embedded networked systems to achieve intelligent control. CPS enable full traceability of production processes as well as comprehensive data assignments in real-time. Through real-time communication and coordination between "manufacturing things", production systems, in the form of Cyber-Physical Production Systems (CPPS), can make intelligent decisions. Meanwhile, with the advent of I4.0, it is possible to collect heterogeneous manufacturing data across various facets for fault diagnosis by using the industrial internet of things (IIoT) techniques. Under this data-rich environment, the ability to diagnose and predict production failures provides manufacturing companies with a strategic advantage by reducing the number of unplanned production outages. This advantage is particularly desired for steel-making industries. As a consecutive and compact manufacturing process, process downtime is a major concern for steel-making companies since most of the operations should be conducted within a certain temperature range. In addition, steel-making consists of complex processes that involve physical, chemical, and mechanical elements, emphasising the necessity for data-driven approaches to handle high-dimensionality problems. For a modern steel-making plant, various measurement devices are deployed throughout this manufacturing process with the advancement of I4.0 technologies, which facilitate data acquisition and storage. However, even though data-driven approaches are showing merits and being widely applied in the manufacturing context, how to build a deep learning model for fault prediction in the steel-making process considering multiple contributing facets and its temporal characteristic has not been investigated. Additionally, apart from the multitudinous data, it is also worthwhile to study how to represent and utilise the vast and scattered distributed domain knowledge along the steel-making process for fault modelling. Moreover, state-of-the-art does not iv Abstract address how such accumulated domain knowledge and its semantics can be harnessed to facilitate the fusion of multi-sourced data in steel manufacturing. In this case, the purpose of this thesis is to pave the way for fault diagnosis in steel-making processes using data mining under I4.0. This research is structured according to four themes. Firstly, different from the conventional data-driven research that only focuses on modelling based on numerical production data, a framework for data mining for fault diagnosis in steel-making based on multi-sourced data and knowledge is proposed. There are five layers designed in this framework, which are multi-sourced data and knowledge acquisition, data and knowledge processing, KG construction and graphical data transformation, KG-aided modelling for fault diagnosis and decision support for steel manufacturing. Secondly, another of the purposes of this thesis is to propose a predictive, data-driven approach to model severe faults in the steel-making process, where the faults are usually with multi-faceted causes. Specifically, strip breakage in cold rolling is selected as the modelling target since it is a typical production failure with serious consequences and multitudinous factors contributing to it. In actual steel-making practice, if such a failure can be modelled on a micro-level with an adequately predicted window, a planned stop action can be taken in advance instead of a passive fast stop which will often result in severe damage to equipment. In this case, a multifaceted modelling approach with a sliding window strategy is proposed. First, historical multivariate time-series data of a cold rolling process were extracted in a run-to-failure manner, and a sliding window strategy was adopted for data annotation. Second, breakage-centric features were identified from physics-based approaches, empirical knowledge and data-driven features. Finally, these features were used as inputs for strip breakage modelling using a Recurrent Neural Network (RNN). Experimental results have demonstrated the merits of the proposed approach. Thirdly, among the heterogeneous data surrounding multi-faceted concepts in steelmaking, a significant amount of data consists of rich semantic information, such as technical documents and production logs generated through the process. Also, there Abstract v exists vast domain knowledge regarding the production failures in steel-making, which has a long history. In this context, proper semantic technologies are desired for the utilisation of semantic data and domain knowledge in steel-making. In recent studies, a Knowledge Graph (KG) displays a powerful expressive ability and a high degree of modelling flexibility, making it a promising semantic network. However, building a reliable KG is usually time-consuming and labour-intensive, and it is common that KG needs to be refined or completed before using in industrial scenarios. In this case, a fault-centric KG construction approach is proposed based on a hierarchy structure refinement and relation completion. Firstly, ontology design based on hierarchy structure refinement is conducted to improve reliability. Then, the missing relations between each couple of entities were inferred based on existing knowledge in KG, with the aim of increasing the number of edges that complete and refine KG. Lastly, KG is constructed by importing data into the ontology. An illustrative case study on strip breakage is conducted for validation. Finally, multi-faceted modelling is often conducted based on multi-sourced data covering indispensable aspects, and information fusion is typically applied to cope with the high dimensionality and data heterogeneity. Besides the ability for knowledge management and sharing, KG can aggregate the relationships of features from multiple aspects by semantic associations, which can be exploited to facilitate the information fusion for multi-faceted modelling with the consideration of intra-facets relationships. In this case, process data is transformed into a stack of temporal graphs under the faultcentric KG backbone. Then, a Graph Convolutional Networks (GCN) model is applied to extract temporal and attribute correlation features from the graphs, with a Temporal Convolution Network (TCN) to conduct conceptual modelling using these features. Experimental results derived using the proposed approach, and GCN-TCN reveal the impacts of the proposed KG-aided fusion approach. This thesis aims to research data mining in steel-making processes based on multisourced data and scattered distributed domain knowledge, which provides a feasibility study for achieving Industry 4.0 in steel-making, specifically in support of improving quality and reducing costs due to production failures