Process industries have to operate in a very competitive and globalized environment, requiring efficient and sustainable production processes. As a result, production targets need to be translated into control objectives which are usually formulated as performance specifications of the process, i.e. tracking of references or rejection of process disturbances. This is often a hard and difficult task which involves assumptions and simplications because of the process complexity. Complexity arises often due to the large scale character of a process, i.e. a pulp and paper can host thousands of control loops. A critical step in the design of these loops is the choice of the structure of the control, which means that controllers need to be placed between sensors and actuators.Current methods for control structure selection include the Interaction Measures (IMs). The IMs help the designer to select a subset of the most significant input-output channels, which will form a reduced model on which the control design will be based. The IMs are traditionally evaluated using a nominal model of the process. However, all process models are affected by uncertainties as simplifications and approximations are unavoidable during modeling. Thus, the validity of the control structure suggested by the IMs cannot be assessed by only analyzing the nominal model. The first part of this thesis focuses in analyzing the sensitivity of the IMs to model uncertainties in order to determine a robust control structure which is feasible for all the uncertainty set.It also becomes clear that, control structure selection requires extensive knowledge about how the multiple process variables are interconnected. The second part of this thesis focuses on creating IMs which can help the control designers to understand the propagation of effects in the process, and express this propagation in directed graphs for an intuitive understanding of the process which will help to design a feasible control structure. These methods have been inspired by coherence analysis used in brain connectivity.Neurons and neural populations interact with each other in different brain processes related to events as perception, or cognition. Electroencephalography (EEG) is a measure of electrical activity in the brain which is acquired from sensors positioned on the surface of the head, each of the electrodes collects the aggregated voltage of a neuron population. Analyzing the flow of information between populations of neurons allows to understand the communication between different parts of the brain in different brain processes. In a very similar way, analyzing the flow of information between variables in an industrial process will provide designers with the required information to understand the behavior of the plant.

Process industries have to operate in a very competitive and globalized environment, requiring efficient and sustainable production processes. Production targets need to be translated into control objectives and are usually formulated as performance specifications of the process. The controller design is a difficult task which involves assumptions and simplifications because of the process complexity. Complexity arises often due to the large scale character of a process, i.e. a pulp and paper mill which can be composed by thousands of control loops. A critical step is the choice of the control configuration, which involves choosing a set of measurements to be used to calculate the control action for each actuator.Current methods for Control Configuration Selection (CCS) include Interaction Measures (IMs). The probably most widely used IM dates back to 1966 when the Relative Gain Array (RGA) was introduced by Bristol. However, these methods rarely become applied in industry, where control structures are often designed based on previous experience or common sense in interpreting process knowledge, but without the support of theoretical and systematic tools.The work in this thesis is oriented towards the development of these tools for industry application. Several topics on CCS are addressed to deal with this lack of practical use, including the robustness to model uncertainty, the need of parametric process models of the complex process, the lack of tools which present the information in connection to the process layout, and the delay from research to education and finally industry application.The main contribution of this thesis is on the consideration of model uncertainty in the CCS problem. Since uncertainty is an intrinsic property of all process models, the validity of the control configuration suggested by the IMs cannot be assessed by only analyzing the nominal model. This thesis introduces methods for the computation of the uncertainty bounds of two gramian-based IMs, which can be used to design robust control configurations.The requirement of process models is an important limitation for the use of the IMs, and the complexity of modeling increases with the number of process variables. This thesis presents novel results in the estimation of IMs, which aim to remove the need of parametric process models for the design of control configurations.CCS using IMs is a heuristic approach, being interpretation needed to select the process interconnections on which control will be based. The traditional IMs present information as an array of real numbers which is disjoint from the process layout. This thesis describes new methods for the interaction analysis of complex processes using weighted graphs, allowing integrating the analysis with process visualization for an increased process understanding.As final contribution, this thesis describes the development of the software tool ProMoVis (Process Modeling and Visualization), which is a platform in which state-of-the-art research in CCS is implemented for facilitating its use in industry applications.

This paper discusses challenges which need to be addressed by the maintenance community such as availability of Big Data solutions, data connectivity and efficient enterprise communications. Addressing such challenges will increase enterprise revenue due to improved availability and reliability.

Styrsystemen i bruken består av tusentals reglerloopar och det är svårt att få en helhetssyn, och förstå de komplicerade sambanden mellan de olika processtegen. Med de nya verktygen blir det möjligt att analysera strukturen och generera helhetsstrategier för optimal styrning från ved till färdig pappersprodukt.

The PiCCS workshop took place at Luleå University of Technology on 16th and 17th of August 2017. In total 20 researcher and engineers from industry participated in the event.

The main aim of the workshop was to bring together expert in the field of Control Configuration Selection (CCS), which is a sub-field in the research area Automatic Control, to discuss the current state of the art and identify remaining challenges in the field. The identified challenges were formulated as future directions and are summarized in this report, together with an account of the discussion during these two days.

The workshop explored the following topics Implementing optimal operation using simple control elements, Real time optimisation approach in control structure design and benchmarking, Data driven control configuration selection, and Reconfiguration of control structures. Based on the outcome of the discussion, a group of participants proposed an invited session for the 2018 IFAC AdChem Symposium, which has been accepted at the time of the publication of this report and consists of 6 papers addressing challenges discussed during the workshop.

This paper presents a new application software for control conguration selection of interconnected industrial processes,called ProMoVis. Moreover, ProMoVis is able to visualize process models and process layout at the physical leveltogether with the control system dynamics. The software consists of a builder part where the visual representationof the interconnected process is created and an analyzer part where the process is analyzed using dierent controlconguration selection tools.The conceptual idea of the software is presented and the subsequent design and implementation of ProMoVis isdiscussed. The implemented analysis methods are briey described including their usage and implementation aspects.The use of ProMoVis is demonstrated by an application study on the stock preparation process at SCA Obbola AB,Sweden. The results of this study are compared with the currently used control strategy.The study indicates that ProMoVis introduces a systematic and comprehensive way to perform control congurationselection. ProMoVis has been released under the Apache Open Source license.

This paper discusses a new approach to interactive modeling, visualization and analysis of complex industrial processes. A theoretical framework based on signal flow graphs for modeling and visualization is presented. Using this framework a software tool is designed, called ProMoVis, which can be used to model a process, to visualize the models together with process construction and control system, and to perform analysis regarding e.g. feasible control strategies for the process. Moreover, a case study is conducted, where ProMoVis is used to model, visualize and analyze a stock preparation plant. The results indicate that the proposed methods and tools improve work flows, increase process understanding and simplify decision making on control strategies for complex process.

SCOPE Norra är ett samarbetskonsortium för forskning och utveckling tillsammans med massa- och pappersindustrin i Norrbotten och Västerbotten. Projektet koordineras av centrumbildningen ProcessIT Innovations.Inom SCOPE Norra pågår ett flertal delprojekt, uppdelat på ett antal fokusområden.Huvudfinansiär för konsortiet är Tillväxtverket genom medel från EU:s strukturfonder.

This paper deals with DRGA analysis for uncertain systems. Uncertainties in a process model can be translated into uncertainties in the DRGA which might invalidate the decision on the variable paring in decentralized control. Bounds for the uncertainties in the DRGA of a multivariable process are derived for a given nominal process model with known uncertainty region. The resulting uncertainty region for the DRGA is used to assess the validity of decisions based on the nominal DRGA. The methodology for the computation of the bounds is currently restricted to the 2-by-2 case. Beside an explanatory example, a case study on a coal injection vessel is conducted and discussed.

This paper reviews a method for approximating the bounds on a gramian-based Interaction Measure (IM) for systems described by uncertain parametric models. The considered IM is the Participation Matrix. The reviewed method is based on analyzing the uncertainty in the area enclosed by the Nyquist curve. Conditions for the exactitude of this method are derived, and implementation issues are discussed.

This paper presents a method for the robust control structure selection based on the assessment of the H2-norm. For both uncertain parametric models and non parametric estimated frequency response functions (FRF) with confidence regions, the magnitude of the Bode diagram is analyzed and regions for the H2-norm are derived. The H2-norm has been successfully used to identify the significant input-output interconnections in multivariable system for the nominal case. The derived regions for the H2-norm of the interconnections are used to extent the H2-norm based method to the uncertain case, and enables robust control structure selection. The method is applied to theoretical examples and a quadruple tank setup to shows its feasibility.

The selection of the structure of a controller in large scale industry processes usually requires extensive process knowledge. The aim of this paper is to report new results on recently suggested methods for the analysis of complex processes. These methods aid the designers in comprehending a process by representing structural and functional relationships from actuators and process disturbances to measured or estimated variables. The methods are formulated in a flexible framework based on graph theory, which can also be used for closed-loop analysis. Additionally, the sensitivity of the methods to scaling and time delays are discussed and resolved. It is also proposed how filtering can be used to restrict the analysis to a frequency region of interest. The feasibility of the methods is shown by the use of three case studies. A quadruple tank process is used to exemplify the methods and their use. Then the methods are applied on a real-life process, the stock preparation plant of a pulp and paper mill. The third study case analyzes a previously published example in closed loop. It is shown that the methods can be used to take efficient decisions on decentralized and sparse control structures, as well as assessing the channel interactions in a closed-loop system.

The selection of the structure of a controller in large scale industry processes requires extensive process knowledge. The aim of this paper is to present methods which help designers to comprehend a process by representing structural and functional relationships from actuators and process disturbances to measured or estimated variables. This paper describes similarities between brain connectivity theory and interaction analysis in multivariable processes. Results from both theories are combined to create new methods for the analysis of industry processes. The developed methods are applied to an illustrative example.

Recently, methods for Control Configuration Selection (CCS) using weighted graphs have been introduced. These methods were integrated in a software tool which allows systematic CCS in industry processes by choosing a simplified model with a reduced structure on which control will be based. This paper addresses the extension of these graph-based methods to be applied to integrating systems, which often appear in industry processes, e.g. at storage vessels. The extension is based on restricting the analysis to a set of frequencies of interest which omits the infinite DC-gain. The results are validated and illustrated using a real-life case, namely the secondary heating system of a pulp & paper mill.

This paper deals with the estimation of a gramian-based interaction measure from logged process data, and thereby removing the need of creating parametric models prior to the selection of the significant input-output interconnections. Moreover, the resulting confidence regions of the estimates can be used to perform a robust control structure selection.

critical step in the control design of industrial processes is the Control Configuration Selection (CCS), where each actuator is associated with a set of measurements which will be used in the calculation of the control action.

A possible solution to the CCS problem is given by the gramian-based Interaction Measures (IMs), which are derived from nominal process models. This paper introduces the derivation of uncertainty bounds for a gramian-based IM using models with uncertainty described in multiplicative form. An alternative to this model-based approach is presented, where uncertainty bounds are estimated from a tailored experiment.

In addition, a procedure for robust CCS is introduced. This procedure integrates the calculated uncertainty bounds in the design of the control configuration.

This paper presents a complete method for automatic and robust control configuration selection for linear systems which relies upon acquired process data under gaussian noise excitation.

The selection of the configuration  is based on the estimation of the Interaction Measure named Participation Matrix. This estimation is derived with uncertainty bounds, which allows to  determine online whether the uncertainty is sufficiently low to derive a robust decision on the control configuration to be used or if the uncertainty should be reduced  by e.g. prolonging the experiment to obtain more data.

This paper introduces an Interaction Measure named Prediction Error Index Array (PEIA), which can be applied both to linear and non-linear systems. The linear PEIA is constructed as an extension of previous results using the H2-norm. The non-linear PEIA is an extension for systems represented by Volterra series. Additionally, the paper gives an interpretation of both linear and nonlinear PEIA based on the prediction error of the structurally reduced model which results from the control configuration selection. Examples illustrate and compare the interaction measure with established methodologies, like the Relative Gain Array, participation matrix, and Hankel Interaction Index array.

This paper discusses the guided and automatic control configuration selection in large scale complex systems. Due to the trend of increasesd level of automation and connectedness which is promoted by the Industry 4.0 strategy and supported by technologies relating to cyber-physical systems and the industrial internet of things, selecting appropriate control strategies becomes increasingly important and complex. This is especially important as a control strategies will limit the achievable performance of the process system, and there are  trade-offs between complexity of the control strategies, achievable performance, vulnerability and maintainability.

The paper reviews the state of the art of methodologies that support the practitioners in taking decisions on control strategies, where two main approaches are considered, the guided one and a fully automatic one. It is shown how both approached can be conducted and examples are used to clarify the selection process.

This technical report provides supplementary materialto the research paper entitled ”On Guided and AutomaticControl Configuration Selection”, presented at the ETFA 2017.In that paper, different Control Configuration Selection (CCS)tools are reviewed and integrated into guided and automaticCCS methodologies. The guided CCS is a heuristic step-by-stepmethodology to be applied by practitioners, while the automaticCCS methodologies target the adaptation of such heuristicsinto algorithms which can be run in a computer and assist thepractitioners in the decision making. This report summarizesthe results of applying the introduced methodologies to a reallifeprocess: the Secondary Heating System. For an introductorybackground, preliminaries, and details on the methodologies,the reader is referred to the original research paper.

This survey on Control Configuration Selection (CCS) includes methods based on relative gains, gramian-based interaction measures, methods based on optimization schemes, plantwide control, and methods for the reconfiguration of control systems. The CCS problem is discussed, and a set of desirable properties of a CCS method are defined. Open questions and research tracks are discussed, with the focus on new challenges in relation to the emerging area of Wireless Sensors and Actuator Networks.

The challenge of generalizing Data Quality assessment is hindered by the fact that Data Quality requisites depend on the purpose for which the data will be used and on the subjectivity of the data consumer. The approach proposed in this paper to address this challenge is to employ a semi-automated user-guided Data Quality assessment. This paper introduces a generic framework for data quality analytics which is mainly composed by a set of software units to perform semi-automated Data Quality analytics and a set of Graphical User Interfaces to enable the user to guide the Data Quality assessment. The framework has been implemented and can be customized according to the needs of the purpose and of the consumer. The framework has been instantiated in a case study on Long-hole drill rigs, where several Data Quality issues have been discovered and their root cause investigated.

A critical step in the control design of industrial processes is the Control Configuration Selection (CCS), where each actuator is grouped with a set of measurements to be used in the computation of its control action.Tools for CCS include gramian-based Interaction Measures (IMs), initially defined for linear systems. Since a trending research topic is the derivation of IMs for non-linear systems, a decision of the designer is therefore the approach to the problem in the linear or non-linear framework. For this end, a method is discussed that determines the degree of nonlinearity of a system based on a specially tailored experiment, and thus enables the selection of the correct framework for the analysis. The novelty is in the estimation of two gramian-based IMs with confidence bounds from the tailored experiment which is applicable if the process is revealed to be weakly non-linear. If the process is found to be strongly non-linear, then alternative approaches for the interaction analysis have to be considered.

This paper presents a technique for in-situ non-destructive testing of materials with applications in railway crossings. The novelty is in successfully applying the Virtual Source (VS) concept using water jet coupling for a large transducer. By focusing the sound field at the surface of the sample, the water jet probe can be built with a small nozzle opening, limiting the water consumption and making it viable for field applications. The annular geometry of the large transducer ensures the spherical wavefront assumed in the application of the SAFT algorithm, which usually limits the size of the transducer

The aim of this article is to present an example of a novel cloud computing infrastructure for big data analytics in the Process Control Industry. Latest innovations in the field of Process Analyzer Techniques (PAT), big data and wireless technologies have created a new environment in which almost all stages of the industrial process can be recorded and utilized, not only for safety, but also for real time optimization. Based on analysis of historical sensor data, machine learning based optimization models can be developed and deployed in real time closed control loops. However, still the local implementation of those systems requires a huge investment in hardware and software, as a direct result of the big data nature of sensors data being recorded continuously. The current technological advancements in cloud computing for big data processing, open new opportunities for the industry, while acting as an enabler for a significant reduction in costs, making the technology available to plants of all sizes. The main contribution of this article stems from the presentation for a fist time ever of a pilot cloud based architecture for the application of a data driven modeling and optimal control configuration for the field of Process Control. As it will be presented, these developments have been carried in close relationship with the process industry and pave a way for a generalized application of the cloud based approaches, towards the future of Industry 4.0

Bounds of two Gramian-based Interaction Measures (IM:s) induced by model uncertainty are presented in this paper. The connection between the considered IM:s (the Hankel Interaction Index Array (HIIA) and the Participation Matrix (PM)) is explored, showing that it is possible in certain cases to translate the bounds of one into bounds of the other. The first method is a tightening of previously suggested uncertainty bounds for the HIIA. The second method is based on a novel exploration of the relationship between the PM and the area enclosed by the Nyquist diagram. The latter method is a numerical approximation of the analytical bounds of the PM, whilst the former one provides very loose bounds for the examples presented here .

The steel industry involves energy-intensive processessuch as combustion processes whose accurate modellingvia first principles is both challenging and unlikely to leadto accurate models let alone cast time-varying dynamics anddescribe the inevitable wear and tear. In this paper we addressthe main objective which is the reduction of energy consumptionand emissions along with the enhancement of the autonomy ofthe controlled process by online modelling and uncertaintyawarepredictive control. We propose a risk-sensitive modelselection procedure which makes use of the modern theoryof risk measures and obtain dynamical models using processdata from our experimental setting: a walking beam furnaceat Swerea MEFOS. We use a scenario-based model predictivecontroller to track given temperature references at the threeheating zones of the furnace and we train a classifier whichpredicts possible drops in the excess of Oxygen in each heatingzone below acceptable levels. This information is then used torecalibrate the controller in order to maintain a high qualityof combustion, therefore, higher thermal efficiency and loweremissions

The aim of this article is to introduce a novel sparse controller design for the temperature control of an experimental walking beam furnace in steel industry. Adequate tracking of temperature references is essential for the quality of the heated slabs. However, the design of the temperature control is hindered by the multivariable (non-square) dynamic behavior of the furnace. These dynamics include significant loop interactions and time delays. Furthermore, a novel data-driven model, based on real life experimental data that relies on a subspace state representation in a closed loop approach is introduced. In the sequel, the derived model is utilized to investigate the controller's structure. By applying the relative gain array approach a decentralized feedback controller is designed. However, in spite of the optimal and sparse design of the controller, there exists interaction between loops. By analyzing the interaction between the inputs-outputs with the Σ2 Gramian-based interaction methodology, a decoupled multi-variable controller is implied. The simulation result, based on the experimental modeling of the furnace, shows that the controller can successfully decrease the interaction between the loops and track the reference temperature set-points.

Imaging through anisotropic or highly heterogeneous materials is challenging for the existence of strong boundary and volume reverberations. To image small cracks or flaws in a reverberating thin layers, high resolution techniques are needed in both temporal and spatial domain, so that the reverberation can be suppressed to some level. In this paper, the reverberation suppression performance of the total focusing beamforming method (TFM) was evaluated by simulation and real data processing. The results showed that the more the focusing point moves away from the array central line, the more multi-reflections can be suppressed. Furthermore, TFM combined with adaptive processing greatly improves the small flaw detection performance. Test results on real samples confirmed the robustness and reverberation suppression capability of the TFM imaging method.

This article presents a procedure that utilizes the local polynomial approximation approach in the estimation of the Dynamic Relative Gain Array (DRGA) matrix and its uncertainty bounds for weakly nonlinear systems. This procedure offers enhanced frequency resolution and noise reduction when random excitation is used. It also allows separation of nonlinear distortions with shorter measuring time when multisine excitation is imposed. The procedure is illustrated using the well-known quadruple tank process as a case study in simulation and in real life. Besides, a comparison with the pairing results of the static RGA, nonlinear RGA and DRGA based on linearized quadruple tank model for different simulation cases is performed.

This article presents a procedure to estimate the relative gain array (RGA) matrix for weakly nonlinear systems by means of nonparametric identification of the frequency response matrix (FRM). Specifically, the best linear approximation of nonlinear systems and the covariance of the nonlinear distortions are used in the relative gain array estimation. For the estimation neither process model nor model structure need to be known which is an advantage over methods that require accurate knowledge of a parametric process model. The proposed approach is compared with the original RGA and a nonlinear RGA calculation using the well-known quadruple tank process as a case

This paper proposes an automated pairing approach for configuration selection of decentralized controllers which considers system uncertainties. Following the Relative Interaction Array (RIA) pairing rules, the optimal control configuration, i.e. the configuration that fits best the pairing rules, is obtained automatically by formulating the control configuration selection problem as an Assignment Problem (AP). In this AP, the associated costs related to each input-output pairing are given by the RIA coefficients. The Push-Pull algorithm is used to solve the AP for the nominal system and to obtain the set of costs for which the resulting configuration remains optimal, also called the perturbation set. The introduction of uncertainty bounds on the RIA-based costs enables the testing of the possible violation of the optimality conditions. Examples to illustrate the proposed approach for a 3×3 system and 4×4 gasifier plant are given.

An approach to investigate the effect of system uncertainty on the optimal controlconfiguration selection in multivariable systems is proposed. An optimal control configuration,i.e the configuration which best agrees with input-output pairing rules according to certaininteraction measure (IM) can be obtained automatically by formulating the control configurationselection as a Transportation Problem (TP). The proposed approach then checks whetherthis optimal control configuration is valid for given system uncertainties or if a change in theconguration could be expected.

Degraded Subterranean environments are an attractive case for miniature aerial vehicles, since there is a constant need to increase the safety operations in underground mines. The starting point for integrating aerial vehicles in the mining process is the capability to reliably navigate along tunnels. Inspired by recent advancements, this paper presents a collection of different, experimentally verified, methods tackling the problem of MAVs heading regulation while navigating in dark and textureless tunnel areas. More specifically, four different methods are presented in this work with the common goal to identify open space in the tunnel and align the MAV heading using either visual sensor in methods a) single image depth estimation, b) darkness contour detection, c) Convolutional Neural Network (CNN) regression and 2D Lidar sensor in method d) range geometry. For the works a)-c) the dark scene in the middle of the tunnel is considered as open space and is processed and converted to yaw rate command, while d) examines the geometry of the range measurements to calculate the yaw rate command. Experimental results from real underground tunnel demonstrate the performance of the methods in the field, while setting the ground for further developments in the aerial robotics community.

The ongoing digital transformation is changing asset management in the railway industry. Emerging digital technologies and Artificial Intelligence is expected to facilitate decision-making in management, operation, and maintenance of railway by providing an integrated data-driven and model-driven solution. An important aspect when developing decision-support solutions based on AI and digital technology is the users’ experience. User experience design process aims to create relevance, context-awareness, and meaningfulness for the end-user. In railway contexts, it is believed that applying a human-centric design model in the development of AI-based artefacts, will enhance the usability of the solution, which will have a positive impact on the decision-making processes. In this research, the applicability of such advanced technologies i.e., Virtual Reality, Mixed Reality, and AI have been reviewed for the railway asset management. To carry out this research work, literature review has been conducted related to available Virtual Reality/Augmented Reality/Mixed Reality technologies and their applications within railway industry. It has been found that these technologies are available, but not applied in railway asset management. Thus, the aim of this paper is to propose a human-centric design model for the enhancement of railway asset management using Artificial Intelligence, Virtual Reality, and Mixed Reality technologies. The practical implication of the findings from this work will benefit in increased efficiency and effectiveness of the operation and maintenance processes in railway

Managing assets in railway, including infrastructure and rolling stock, efficiently and effectively is challenging. The emerging digital technologies and Artificial Intelligence (AI) are expected to augment the decision making in Asset Management (AM) and Fleet Management (FM). The AI technologies need to be adapted to the specific needs of any industrial domain, e.g. railways, to facilitate the implementation and achievement of the overall business goals. This adaptation is denoted as ‘Industrial AI’(IAI). IAI for railways infrastructure and rolling stock, is dependent on an appropriate technology roadmap reflecting necessary know-hows. The IAI roadmap aims to provide a strategic and executive plan to augment managing railway assets i.e. ‘Augmented Asset Management (AAM)’. AAM can be applied through an end-to-end secure platform for e.g. data sharing among stakeholders, the development of analytics, and model sharing through distributed computing. AAM in railways can be enhanced through implementation of a generic fleet management (FM) approach. In the FM approach, any population of assets with common characteristics and also the relationship of the asset to the fleet is considered. This paper aims to develop and propose a concept for AAM enabled through IAI and digital technologies to provide augmented decision support through a secure platform, for AM in railways. A FM approach towards a holistic operation and maintenance of assets, based on a System of Systems thinking, for AAM in railways is applied for population of infrastructure assets and rolling stock assets with common characteristics. Finally, a taxonomy of issues and challenges, in the application of AAM to FM in railways is provided. The data for this taxonomy has been collected from railway organizations through iterative rounds of interviews. This taxonomy can be used for research and development of frameworks, approaches, technologies, and methodologies for AAM in railways.

This article considers a low-cost and light weight platform for the task of autonomous flying for inspection in underground mine tunnels. The main contribution of this paper is integrating simple, efficient and well-established methods in the computer vision community in a state of the art vision-based system for Micro Aerial Vehicle (MAV) navigation in dark tunnels. These methods include Otsu's threshold and Moore-Neighborhood object tracing. The vision system can detect the position of low-illuminated tunnels in image frame by exploiting the inherent darkness in the longitudinal direction. In the sequel, it is converted from the pixel coordinates to the heading rate command of the MAV for adjusting the heading towards the center of the tunnel. The efficacy of the proposed framework has been evaluated in multiple experimental field trials in an underground mine in Sweden, thus demonstrating the capability of low-cost and resource-constrained aerial vehicles to fly autonomously through tunnel confined spaces.

This article proposes a novel unsupervised learning framework for detecting the number of tunnel junctions in subterranean environments based on acquired 2D point clouds. The implementation of the framework provides valuable information for high level mission planners to navigate an aerial platform in unknown areas or robot homing missions. The  framework utilizes spectral clustering, which is capable of uncovering hidden structures from connected data points lying on non-linear manifolds. The spectral clustering algorithm computes a spectral embedding of the original 2D point cloud by utilizing the eigen decomposition of a matrix that is derived from the pairwise similarities of these points. We validate the developed framework using multiple data-sets, collected from multiple realistic simulations, as well as from real flights in underground environments, demonstrating the performance and merits of the proposed methodology. 

This work presents an approach for information exchange between adjacent air operations domains by means of graph technologies. The approach has the ability to leverage interoperability and collaboration between air- and ground-based systems and stakeholders in respective domains. In its foundation, it provides a means for relevant actors to access and assess relevant data, information and knowledge, and thus provide input in terms of viable action alternatives in a complex and dynamic operational context. As a proof-of-concept, we have utilizeda full-stack application framework to implement a decision support demonstrator for operational aircraft maintenance. Our solution facilitates a lightweight and dynamic representation of relevant domain knowledge,readily available for exploitation by graph algorithms, adapted to our domain. We have based our implementation on the full-stack application framework Grand-Stack, which is an architecture designed to exploit the power of graphs throughout its stack.

Enhacement of material impulse response buried in reverberating ultrasonic echoes from thin layered materials can be exploited in order to be able to detect possible flaws. One of the methods presented in this study is to enhance the impulse response of a material by training an adaptive filter that promotes and appropriate statistical characteristic such as asymmetry. The other approach is to employ a parametric linear model of reverberations that utilizes Maximum Likelihood Estimation on its parameters, to later suppress the reverberations and reveal possible flaws. Both approaches are investigated and shown to succeeed under certain conditions and supported with experiments.

Railway overhead catenary (ROC) is a linear asset and spread over large area. Different regions of the linear asset are exposed to different climate conditions such as temperature, wind, and ice accretion and operating conditions. If these conditions disrupt the functionality, then it leads to failure resulting in line closure. Being ROC is a linear asset, condition monitoring (CM) is difficult due to large distances, climate conditions, costly due to requirement of special equipment at the location and effects the scheduled traffic by occupying the tracks. Hence, there is a need for technologies to monitor the condition of ROC through a cloud-based approach which has faster response time. Light Detection and Ranging (LiDAR) can be used for CM of ROC. It collects spatial data in the form of 3D point cloud in various domains such as construction, mining and railways. LiDAR devices will be mounted on locomotives on a regular traffic. The point cloud data is processed to extract the railway assets such as tracks, masts, catenary etc. and surrounding vegetation. Further, processing of point cloud data can be used to extract exact location and position of the assets. One of the failure modes for ROC, if the distance between the two wires is less than the specifications, then it leads to failure. This paper develops a cloud-based approach to measure the distance between specific wires, through processing of point cloud data. This approach forms the foundation for data augmentation and development of hybrid digital twins (DT) of railway overhead catenary.

Railway Overhead Catenary (ROC) system is critical for railways’ overall performance! ROC is a linear asset that is spread over a large geographical area. Insufficient performance of ROC has a significant impact on the overall railway operations, which leads to decreased availability and affects performance of the railway system. Prognostic and Health Management (PHM) of ROC is necessary to improve the dependability of the railway. PHM of ROC can be enhanced by implementing a data-driven approach. A data-driven approach to PHM is highly dependent on the availability and accessibility of data, data acquisition, processing and decision-support. Acquiring data for PHM of ROC can be used through various methods, such as manual inspections. Manual inspection of ROC is a time-consuming and costly method to assess the health of the ROC. Another approach for assessing the health of ROC is through condition monitoring using 3D scanning of ROC utilising LiDAR technology.Presently, 3D scanning systems like LiDAR scanners present new avenues for data acquisition of such physical assets. Large amounts of data can be collected from aerial, on-ground, and subterranean environments. Handling and processing this large amount of data require addressing multiple challenges like data collection, processing algorithms, information extraction, information representation, and decision support tools. Current approaches concentrate more on data processing but lack the maturity to support the end-to-end process. Hence, this paper investigates the requirements and proposes an architecture for a data-to-decision approach to PHM of ROC based on utilisation of LiDAR technology.

Creation, maintenance, and update of digital twins are costly and time-consuming mechanisms. The required effort can be optimized with the use of LiDAR technologies, which support the process of collecting data related to spatial information such as location, geometry, and position. Sharing such data in multi-stakeholder environments is hindered due to competition, confidentiality, and security requirements. Multi-stakeholder environments favor the use of decentralized creation and update mechanisms with reduced data exchange. Such mechanisms are facilitated by Federated Learning, where the learning process is performed at the data owner’s location. Two case studies are presented in this paper, where LiDAR is used to extract information from industrial equipment as a part of the creation of a digital twin.

The aim of this paper is to create a state-of-the-art review of the existing standards of open-source communications protocols which enables eMaintenance solutions in the industry. This review includes the explanation of Open O&M (Open Operations & Maintenance) that creates bridges between MIMOSA (Machinery Information Management Open Systems Alliance), the OPC Foundation (Open Protocol Communication), and the ISA SP95 (Instrumentation Systems and Automation Society's, SP95 Committee), in order to manage the problem under discussion. The understanding of the actual state-of-the-art reveals needs for further advancements of technologies and information standards for the exchange of Open O&M data and linked context. Furthermore, in order to be an active player in the industry, it is strategic to simplify the management and integration of enterprise information resources in a cooperating mode.