351 research outputs found
A multi-source feature-level fusion approach for predicting strip breakage in cold rolling
As an undesired and instantaneous failure in the production of cold-rolled strip products, strip breakage results in yield loss, reduced work speed and further equipment damage. Typically, studies have investigated this failure in a retrospective way focused on root cause analyses, and these causes are proven to be multi-faceted. In order to model the onset of this failure in a predictive manner, an integrated multi-source feature-level approach is proposed in this work. Firstly, by harnessing heterogeneous data across the breakage-relevant processes, blocks of data from different sources are collected to improve the breadth of breakage-centric information and are pre-processed according to its granularity. Afterwards, feature extraction or selection is applied to each block of data separately according to the domain knowledge. Matrices of selected features are concatenated in either flattened or expanded manner for comparison. Finally, fused features are used as inputs for strip breakage prediction using recurrent neural networks (RNNs). An experimental study using real-world data instantaneouseffectiveness of the proposed approach
Strip snap analytics in cold rolling process using machine learning
Strip snap, also known as strip breakage or belt tearing, is an undesirable quality incident which results in yield loss and reduced work speed in the cold rolling process of strip products. Therefore, it is necessary to reveal a functional relationship between certain selected variables and strip snap event for the aim of quality improvement. In this study, the probability of strip snap occurrence was quantified by a selected measured variable. Several machine learning algorithms were adopted to predict this target probability. To validate this approach, a case study was conducted based on real-world data collected from an electrical steel reversing mill. The excessively good performance indicates several variables which are strongly correlated with the target
Characterizing strip snap in cold rolling process using advanced data analytics
Among the undesirable quality incidents in the cold rolling process of strip products, strip snap could result in yield loss and reduced work speed. Therefore, it is necessary to reveal the factors influencing the occurrence of this failure for quality improvement. In this study, a data analytics approach was applied with the aim of determining relevant variables affecting snap occurrence. To validate this approach, a case study was conducted based on real-world data collected from an electrical steel reversing mill. The results suggested a selection of variables to characterize the quality issue of strip snap in the cold rolling process. This quality characterization study was performed as the preliminary stage of a quality improvement task
Sliding window filter based strip breakage modelling for failure prediction
In the production of cold-rolled strip products, strip breakage is one of the most common failures during the cold rolling process. However, the existing prediction models on strip breakage use the conventional sliding window algorithm to process the time series data collected from the actual production, resulting in a massive amount of non-informative data, which increases the computational cost for data-driven modelling. In order to tackle this issue, this article proposed a sliding window filter method to optimise the data pre-processing of the strip breakage. Firstly, based on the existing research and understanding of strip breakage, the data characteristics in the process of strip breakage was analysed. Based on the analysis, sample variance (VAR) and length normalised complexity estimate (LNCE) were chosen to determine how informative the time window was related to strip breakage. Secondly, compared with the conventional sliding window approach, the sliding windows were classified through a filter using VAR and LNCE. Thirdly, the filtered data was fed into the Recurrent Neural Network (RNN) for strip breakage modelling. An experimental study based on actual production data collected by a cold-rolled strip manufacturer was conducted to verify this method's effectiveness. The results show that pre-processing data using the sliding window filter decreases the model's computational cost
Design criteria for rolling contact fatigue resistance in back-up rolls.
The demands placed on back-up rolls in hot strip mills have been investigated by a
combination of literature and industrial studies. The tribological operating
conditions have been established and the maximum local loads and pressure
distributions at the work roll/back-up roll interface have been obtained by processing
mill and roll schedule data using a computer program (commercial software
developed by V AI Industries (UK) Ltd) and applying the theories of contact
mechanics.
After a study of the responses of the rolls to these demands and possible failure
mechanisms, research has centred on surface initiated damage whereby cracks can
propagate into the roll substrate potentially reaching the internal residual stress fields
and leading to catastrophic failure. A proposed qualitative contact and fracture
mechanics model, for the rolling contact fatigue and spalling failure, has been
quantified theoretically using published methods for determining the stress intensity
factors at the tips of pressurised and water lubricated, inclined rolling contact fatigue
cracks. The predictions of the quantitative model in terms of crack directions and
lengths have been validated by microscopic observation of the morphologies cracks
produced in test discs used in the "SUROS" Rolling-Sliding Testing Machine and
also in a sample of material spalled from a back-up roll.
The quantitative failure model includes criteria for crack branching either upwards
leading to micro spalling or downwards (potentially catastrophic) and the link
between these two cases has been related quantitatively to the value of the mode I
threshold for the roll material.
After linking mechanics to microstructure and quantifying the interactions between
wear and rolling contact fatigue in this case, practical quantitative recommendations
have been made for the design of bainitic back-up roll materials, back-up roll
redressing procedures and the surface roughness of both the work rolls and back-up
rolls presented to the mill
A knowledge graph-supported information fusion approach for multi-faceted conceptual modelling
It has become progressively more evident that a single data source is unable to comprehensively capture the
variability of a multi-faceted concept, such as product design, driving behaviour or human trust, which has
diverse semantic orientations. Therefore, multi-faceted conceptual modelling is often conducted based on multi-sourced data covering indispensable aspects, and information fusion is frequently applied to cope with the high
dimensionality and data heterogeneity. The consideration of intra-facets relationships is also indispensable. In
this context, a knowledge graph (KG), which can aggregate the relationships of multiple aspects by semantic
associations, was exploited to facilitate the multi-faceted conceptual modelling based on heterogeneous and
semantic-rich data. Firstly, rules of fault mechanism are extracted from the existing domain knowledge repository, and node attributes are extracted from multi-sourced data. Through abstraction and tokenisation of
existing knowledge repository and concept-centric data, rules of fault mechanism were symbolised and integrated with the node attributes, which served as the entities for the concept-centric knowledge graph (CKG).
Subsequently, the transformation of process data to a stack of temporal graphs was conducted under the CKG
backbone. Lastly, the graph convolutional network (GCN) model was applied to extract temporal and attribute
correlation features from the graphs, and a temporal convolution network (TCN) was built for conceptual
modelling using these features. The effectiveness of the proposed approach and the close synergy between the
KG-supported approach and multi-faceted conceptual modelling is demonstrated and substantiated in a case
study using real-world data
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
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