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  • 1.
    Galar, Diego
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Luleå University of Technology, Luleå, Sweden.
    Kans, Mirka
    Linnaeus University, Kalmar, Sweden.
    Schmidt, Bernard
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Big Data in Asset Management: Knowledge Discovery in Asset Data by the Means of Data Mining2016In: Proceedings of the 10th World Congress on Engineering Asset Management (WCEAM 2015) / [ed] Kari T. Koskinen, Helena Kortelainen, Jussi Aaltonen,Teuvo Uusitalo, Kari Komonen, Joseph Mathew & Jouko Laitinen, Springer, 2016, 161-171 p.Conference paper (Refereed)
    Abstract [en]

    Assets are complex mixes of complex systems, built from components which, over time, may fail. The ability to quickly and efficiently determine the cause of failures and propose optimum maintenance decisions, while minimizing the need for human intervention is necessary. Thus, for complex assets, much information needs to be captured and mined to assess the overall condition of the whole system. Therefore the integration of asset information is required to get an accurate health assessment of the whole system, and determine the probability of a shutdown or slowdown. Moreover, the data collected are not only huge but often dispersed across independent systems that are difficult to access, fuse and mine due to disparate nature and granularity. If the data from these independent systems are combined into a common correlated data source, this new set of information could add value to the individual data sources by the means of data mining. This paper proposes a knowledge discovery process based on CRISP-DM for failure diagnosis using big data sets. The process is exemplified by applying it on railway infrastructure assets. The proposed framework implies a progress beyond the state of the art in the development of Big Data technologies in the fields of Knowledge Discovery algorithms from heterogeneous data sources, scalable data structures, real-time communications and visualizations techniques.

  • 2.
    Givehchi, Mohammad
    et al.
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Schmidt, Bernard
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Wang, Lihui
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre. Department of Production Engineering Royal Institute of Technology, Stockholm, Sweden.
    Knowledge-based Operation Planning and Machine Control by Function Blocks in Web-DPP2013In: Advances in Sustainable and Competitive Manufacturing Systems: 23rd International Conference on Flexible Automation & Intelligent Manufacturing / [ed] Américo Azevedo, Springer, 2013, 665-679 p.Conference paper (Refereed)
    Abstract [en]

    Today, the dynamic market requires manufacturing firms to possess high degree of adaptability and flexibility to deal with shop-floor uncertainties. Specifically, targeting SMEs active in the machining and metal cutting sector who normally deal with complex and intensive process planning problems, researchers have tried to address the subject. Among proposed solutions, Web-DPP elaborates a two-layer distributed adaptive process planning system based on function-block technology. Function-block enabled machine controllers are one of the elements of this system. In addition, intensive reasoning based on the features data of the products models, machining knowledge, and resource data is needed to be performed inside the function blocks in machine controller side. This paper reports the current state of design and implementation of a knowledge-based operation planning module using a rule-engine embedded in machining feature function blocks, and also the design and implementation of a common interface (for CNC milling machine controller and its specific implementation for a specific commercial controller) embedded in the machining feature function blocks for controlling the machine. The developed prototype is validated through a case-study.

  • 3.
    Mohammed, Abdullah
    et al.
    Department of Production Engineering, KTH Royal Institute of Technology, Stockholm, Sweden.
    Schmidt, Bernard
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Wang, Lihui
    Department of Production Engineering, KTH Royal Institute of Technology, Stockholm, Sweden.
    Active collision avoidance for human-robot collaboration driven by vision sensors2017In: International journal of computer integrated manufacturing (Print), ISSN 0951-192X, E-ISSN 1362-3052, Vol. 30, no 9, 970-980 p.Article in journal (Refereed)
    Abstract [en]

    Establishing safe human-robot collaboration is an essential factor for improving efficiency and flexibility in today's manufacturing environment. Targeting safety in human-robot collaboration, this paper reports a novel approach for effective online collision avoidance in an augmented environment, where virtual three-dimensional (3D) models of robots and real images of human operators from depth cameras are used for monitoring and collision detection. A prototype system is developed and linked to industrial robot controllers for adaptive robot control, without the need of programming by the operators. The result of collision detection reveals four safety strategies: the system can alert an operator, stop a robot, move away the robot, or modify the robot's trajectory away from an approaching operator. These strategies can be activated based on the operator's existence and location with respect to the robot. The case study of the research further discusses the possibility of implementing the developed method in realistic applications, for example, collaboration between robots and humans in an assembly line.

  • 4.
    Mohammed, Abdullah
    et al.
    Department of Production Engineering, KTH Royal Institute of Technology, Stockholm, Sweden.
    Schmidt, Bernard
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Wang, Lihui
    Department of Production Engineering, KTH Royal Institute of Technology, Stockholm, Sweden.
    Energy-Efficient Robot Configuration for Assembly2017In: Journal of manufacturing science and engineering, ISSN 1087-1357, E-ISSN 1528-8935, Vol. 139, no 5, 051007Article in journal (Refereed)
    Abstract [en]

    Optimizing the energy consumption of robot movements has been one of the main focuses for most of today's robotic simulation software. This optimization is based on minimizing a robot's joint movements. In many cases, it does not take into consideration the dynamic features. Therefore, reducing energy consumption is still a challenging task and it involves studying the robot's kinematic and dynamic models together with application requirements. This research aims to minimize the robot energy consumption during assembly. Given a trajectory and based on the inverse kinematics and dynamics of a robot, a set of attainable configurations for the robot can be determined, perused by calculating the suitable forces and torques on the joints and links of the robot. The energy consumption is then calculated for each configuration and based on the assigned trajectory. The ones with the lowest energy consumption are selected. Given that the energy-efficient robot configurations lead to reduced overall energy consumption, this approach becomes instrumental and can be embedded in energy-efficient robotic assembly.

  • 5.
    Mohammed, Abdullah
    et al.
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Schmidt, Bernard
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Wang, Lihui
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Remote monitoring and controlling for robotic path following operations2012In: Proceedings of the SPS12 conference 2012, The Swedish Production Academy , 2012, 27-33 p.Conference paper (Refereed)
    Abstract [en]

    Controlling a robot's movement requires a prior knowledge about the needed path and configurations to accomplish the movement. The lack of this knowledge causes limitations in the robot's adaptability in dynamic environments. The objectives of this paper are: (1) to improve the ability of the robot to follow any arbitrary path defined by an operator, and (2) to provide the ability for an authorized distant operator to access the system for monitoring and controlling both the robot and the stages of the process. The system developed in this research consists of a calibrated network camera, an industrial robot and an application server. The process starts by having a sketch drown by an operator representing the paths that the robot needs to follow, then the operator can remotely take a snapshot of the paths and retrieve the contours that represent the paths; after that the system sends them to the robot controller to perform the task of path following. The results have shown that the system can perform the required task within a relatively short time and with a reasonable level of quality. This research proves that it is possible to build an adaptive robotic system that can follow efficiently any arbitrary path without the need for defining it in advance.

  • 6.
    Mohammed, Abdullah
    et al.
    Department of Production Engineering, KTH Royal Institute of Technology, 100 44 Stockholm, Sweden.
    Schmidt, Bernard
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Wang, Lihui
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Department of Production Engineering Royal Institute of Technology 100 44 Stockholm, Sweden.
    Gao, Liang
    Huazhong University of Science and Technology, Hubei, China.
    Minimizing Energy Consumption for Robot Arm Movement2014In: Procedia CIRP, ISSN 2212-8271, E-ISSN 2212-8271, Vol. 25, 400-405 p.Article in journal (Refereed)
    Abstract [en]

    Robots are widely used in industry due to their efficiency and high performance. Many of them are operating in the manufacturing stage of the production line where the highest percentage of energy is consumed. Therefore, their energy consumption became a major focus for many robots manufacturers and academic research groups. Nevertheless, the optimisation of that consumption is still a challenging task which requires a deep understanding of the robot’s kinematic and dynamic behaviours. This paper proposes an approach to develop an optimisation module using Matlab® to minimise the energy consumptions of the robot’s movement. With the help of Denavit-Hartenberg notation, the approach starts first by solving the inverse kinematics of the robot to find a set of feasible joint configurations required to perform the task, solving the inverse kinematics is usually a challenging step which requires in-depth analyses of the robot. The module then solves the inverse dynamics of the robot to analyse the forces and torques applied on each joint and link in the robot. Furthermore, a calculation for the energy consumption is performed for each configuration. The final step of the process represents the optimisation of the calculated configurations by choosing the one with the lowest power consumption and sends the results to the robot controller. Three case studies are used to evaluate the performance of the module. The experimental results demonstrate the developed module as a successful tool for energy efficient robot path planning. Further analyses for the results have been done by comparing them with the ones from commercial simulation software. The case studies show that the optimisation of the location for the target path could reduce the energy consumption effectively.

  • 7.
    Sandberg, Ulf
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Schmidt, Bernard
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Wang, Lihui
    Kungliga Tekniska Högskolan.
    Management of factory and maintenance information for multiple production and product life-cycle phases2014In: / [ed] B.K.N.Rao, 2014, 7 s- p.Conference paper (Refereed)
    Abstract [en]

    Maintenance is crucial for future manufacturing systems. An extended local knowledge is essential to increase precision and efficiency, but also for improvements of the maintained object itself. Approaches exist that closes the loop from end-user to vendor, but intra loops are not so well developed.

    This article discusses ways to interconnect and manage data and knowledge flow between work processes in user and vendor life-cycles. It aims to inspire improvements in existing approaches, closer connections between producer and customer, between users, and improved quality of maintenance work via factory-, company-, or group-wide data and knowledge about similar types of equipment.

  • 8.
    Schmidt, Bernard
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Engineering Science.
    Information Fusion processes in Prognostics and Health Management2014Conference paper (Other academic)
    Abstract [en]

    Information Fusion plays important role in Prognostics and Health Management, where data and informations from different sources need to be combined, analyzed and finally used or presented for proper maintenance decisions. The objective of this paper is to outline and analyzed the relation between Information Fusion process and data/information processing in Prognostics Condition Based Maintenance. The Data Fusion Information Group Model (DFIGM) is presented as well as distinction between two levels of information fusion: (1) low-level information fusion (LLIF), which addresses the signal processing, object state estimation and characterization, and (2) high-level information fusion (HLIF) focused on control and situational understanding. All this processes are aligned with condition based maintenance processes from data acquisition and processing, through diagnostics, prognostics, up to health management. Presented work is one of the first steps in the research project toward improvements in Condition Based Maintenance with focus on its implementation in the manufacturing industry.

  • 9.
    Schmidt, Bernard
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Diego, Galar
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Luleå University of Technology, Sweden.
    Wang, Lihui
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. KTH.
    Asset management evolution: from taxonomies toward ontologies2015In: Maintenance, Condition Monitoring and Diagnostics, Maintenance Performance Measurement and Management / [ed] Sulo Lahdelma and Kari Palokangas, Oulu, Finland: POHTO , 2015Conference paper (Refereed)
    Abstract [en]

    This paper addresses the evolution that can be observed in Asset Management in modelling approach. Most traditional Condition Monitoring systems use hierarchical representations of monitored the integration of data from disparate source toward context awareness and Big Data utilization there is a need to include and model more complicated dependencies than hierarchical. Ontology based modelling is gaining recently on popularity in the domain of Condition Monitoring and Asset Management.

  • 10.
    Schmidt, Bernard
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Galar, Diego
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Luleå University of Technology, Luleå, Sweden.
    Wang, Lihui
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Engineering Science. KTH Royal Institute of Technology, Stockholm, Sweden.
    Big data in maintenance decision support systems: aggregation of disparate data types2016In: Euromaintenance 2016 ConferenceProceedings, 2016, 503-512 p.Conference paper (Other academic)
    Abstract [en]

    There is need to obtain reliable information on current and future asset health status to support maintenance decision making process. Within maintenance two main sources of data can be distinguished: Computerized Maintenance Management System (CMMS) for asset registry and maintenance work records; and Condition Monitoring Systems (CM) for direct asset components health state monitoring. There are also other sources of information like SCADA (Supervisory Control and Data Acquisition) for process and control monitoring that can provide additional contextual information leading to better decision making. However data produced acquired and processed and in those system are of disparate types, nature and granularity. This variety includes: event data about failures or performed maintenance work mostly descriptions in unstructured natural language; process variables obtained from different types of sensors and different physical variables from transducers, acquired with different sampling frequencies. Indeed, condition monitoring data are so disparate in nature that maintainers deal with scalars (temperature) through waveforms (vibration) to 2D thermography images and 3D data from machine geometry measuring. Integration and aggregation of those data is not a trivial task and requires modelling of knowledge about those data types, their mutual dependencies and dependencies with monitored processes. There are some attempts of standardisation that try to enable integration of CBM data from different sources. The conversion of those amount of data in meaningful data sets is required for better machine health assessment and tracking within the specific operational context for the asset. This will also enhance the maintenance decision support system with information on how different operational condition can affect the reliability of the asset for concrete contextual circumstances.

  • 11.
    Schmidt, Bernard
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Galar, Diego
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Luleå University of Technology, Luleå, Sweden.
    Wang, Lihui
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Engineering Science. KTH Royal Institute of Technology, Stockholm, Sweden.
    Context Awareness in Predictive Maintenance2016In: Current Trends in Reliability, Availability, Maintainability and Safety: An Industry Perspective / [ed] Uday Kumar, Alireza Ahmadi, Ajit Kumar Verma & Prabhakar Varde, Springer, 2016, 197-211 p.Chapter in book (Refereed)
    Abstract [en]

    Maintenance of assembly and manufacturing equipment is crucial to ensure productivity, product quality, on-time delivery, and a safe working environment. Predictive Maintenance approach utilizes the condition monitoring (CM) data to predict the future machine conditions and makes decisions upon this prediction. Recent development in CM leads to context aware approach where in parallel with CM measurements also data and information related to the context are gathered. Context could be operational condition, history of machine usage and performed maintenance actions. In general more obtained information gives better accuracy of prediction. It is important to track operational context in dynamically changing environment. Today in manufacturing we can observe shift from mass production to mass customisation. This leads to changes from long series of identical products to short series of different variants. Therefore implies changing operational conditions for manufacturing equipment. Moreover, where asset consist of multiple identical or similar equipment the context aware method can be used to combine in reliable way information. This should allow to increase accuracy of prediction for population as a whole as well as for each equipment instances. Same of those data have been already recorded and stored in industrial IT systems. However, it is distributed over different IT systems that are used by different functional units (e.g. maintenance department, production department, quality department, tooling department etc.). This paper is a conceptual paper based on initial research work and investigation in two manufacturing companies from automotive industry.

  • 12.
    Schmidt, Bernard
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Gandhi, Kanika
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Wang, Lihui
    School of Engineering Science, Kungliga Tekniska Högskolan, Stockholm, Sweden.
    Galar, Diego
    Department of Civil, Environmental and Natural Resources Engineering, Luleå Tekniska Universitet, Luleå, Sweden.
    Context preparation for predictive analytics – a case from manufacturing industry2017In: Journal of Quality in Maintenance Engineering, ISSN 1355-2511, E-ISSN 1758-7832, Vol. 23, no 3, 341-354 p.Article in journal (Refereed)
    Abstract [en]

    Purpose

    The purpose of this paper is to exemplify and discuss the context aspect for predictive analytics where in parallel condition monitoring (CM) measurements data and information related to the context are gathered and analysed.

    Design/methodology/approach

    This paper is based on an industrial case study, conducted in a manufacturing company. The linear axis of a machine tool has been selected as an object of interest. Available data from different sources have been gathered and a new CM function has been implemented. Details about performed steps of data acquisition and selection are provided. Among the obtained data, health indicators and context-related information have been identified.

    Findings

    Multiple sources of relevant contextual information have been identified. Performed analysis discovered the deviations in operational conditions when the same machining operation is repeatedly performed.

    Originality/value

    This paper shows the outcomes from a case study in real word industrial setup. A new visualisation method of gathered data is proposed to support decision-making process.

  • 13.
    Schmidt, Bernard
    et al.
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Mohammed, Abdullah
    Royal Institute of Technology 100 44 Stockholm, Sweden.
    Wang, Lihui
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre. Department of Production Engineering Royal Institute of Technology 100 44 Stockholm, Sweden.
    Minimising Energy Consumption for Robot Arm Movement2013In: Proceedings of the International Conference on Advanced Manufacturing Engineering and Technologies / [ed] Andreas Archenti, Antonio Maffei, Stockholm, Sweden: KTH Royal Institute of Technology, 2013, 125-134 p.Conference paper (Refereed)
    Abstract [en]

    Optimising the energy consumption of robot movements has been one of the main focuses for most of today’s robotic simulation software. This optimisation is based on minimising a robot’s joints movements. In many cases, it does not take into consideration the dynamic features. Therefore, reducing energy consumption is still a challenging task and it involves studying the robot’s kinematic and dynamic models together with application requirements. The primary focus of this research is to develop an optimisation model to reduce the energy consumption in robotic applications. An energy optimisation module reported in this paper was developed using Matlab. By solving the kinematics and dynamics equations of the robot, the module is able to optimise towards the minimum energy consumption of the robot’s movements. Moreover, placement of the targets in robot’s working area that minimise the energy consumption can be suggested. The results show the value of the reported approach as a tool for energy efficient robot path planning.

  • 14.
    Schmidt, Bernard
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Sandberg, Ulf
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Wang, Lihui
    Department of Production Engineering Royal Institute of Technology, Sweden.
    Next Generation Condition Based Predictive Maintenance2014In: Proceedings of The 6th International Swedish Production Symposium 2014 / [ed] Johan Stahre, Björn Johansson, Mats Björkman, 2014Conference paper (Refereed)
    Abstract [en]

    Maintenance of assembly and manufacturing equipment is crucial to ensure productivity, product quality, on-time delivery, and a safe working environment. Predictive Maintenance is an approach that utilises the condition monitoring data to predict the future machine conditions and make decisions upon this prediction. The main aim of the presented research is to achieve an improvement in condition based Predictive Maintenance through the Cloud-based approach with usage of the largest information content possible. The objective of this paper is to outline the first steps of a framework to handle and process maintenance, production and factory related data from the first life-cycle phase to the operation and maintenance phase.

  • 15.
    Schmidt, Bernard
    et al.
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Wang, Lihui
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Active collision avoidance for human-robot collaborative manufacturing2012In: Proceedings of the SPS12 conference 2012, The Swedish Production Academy , 2012, 81-86 p.Conference paper (Refereed)
    Abstract [en]

    In the human-robot collaborative manufacturing environment where humans and robots coexist, safety protection of human operators in real time is of paramount importance. This paper presents an approach for real-time active collision avoidance in augmented environment, where virtual 3D models of robots and real camera images of operators are used for monitoring and collision detection. A cost-effective depth camera is chosen for surveillance of any mobile foreign objects, including operators, which are not presented in the virtual 3D models. Two redundant Kinect sensors using structured light are used as the depth cameras for better area coverage and for eliminating possible blind spots in the surveillance area. Collision detection is performed by minumum distance. Processing applied on depth images includes background removal, filtering, labeling and points cloud generation. A prototype system is developed and linked to robot controllers for real-time robot control, with zero robot programming. According to the result of collision detection, it can alert an operator, stop a robot, or even move a robot away from an approaching operator. The results of a case study show that this approach can be applied to real-world applications such as human-robot collaborative assembly to safeguard human operators.

  • 16.
    Schmidt, Bernard
    et al.
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Wang, Lihui
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Automatic Robot Calibration via a Global-Local Camera System2012In: Proceedings of FAIM 2012, Tampere University of Technology, 2012Conference paper (Refereed)
    Abstract [en]

    In a human-robot collaborative manufacturing application where working object can be placed in an arbitrary position, there is a need to calibrate the actual position of the work object. This paper presents an approach for automatic calibration in flexible robotic systems. It consists of two subsystems: a global positioning system based on fixed cameras mounted around robotic workspace, and a local positioning system based on the camera mounted on the robot arm. The aim of the global positioning is to detect work object in working area and roughly estimate the position, whereas the local positioning is to define the object frame according to the 3D position and orientation of the work object with higher accuracy. For object detection and localization, coded visual markers have been utilized. For each object, several markers have been used to increase the robustness and accuracy of localization and calibration procedure. This approach can be used in robotic welding or assembly applications.

  • 17.
    Schmidt, Bernard
    et al.
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Engineering Science.
    Wang, Lihui
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Engineering Science. Department of Production Engineering, KTH Royal Institute of Technology, 100 44 Stockholm, Sweden.
    Automatic work objects calibration via a global-local camera system2014In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 30, no 6, 678-683 p.Article in journal (Refereed)
    Abstract [en]

    In a human–robot collaborative manufacturing application where a work object can be placed in an arbitrary position, there is a need to calibrate the actual position of the work object. This paper presents an approach for automatic work-object calibration in flexible robotic systems. The approach consists of two modules: a global positioning module based on fixed cameras mounted around robotic workspace, and a local positioning module based on the camera mounted on the robot arm. The aim of the global positioning is to detect the work object in the working area and roughly estimate its position, whereas the local positioning is to define an object frame according to the 3D position and orientation of the work object with higher accuracy. For object detection and localization, coded visual markers are utilized. For each object, several markers are used to increase the robustness and accuracy of the localization and calibration procedure. This approach can be used in robotic welding or assembly applications.

  • 18.
    Schmidt, Bernard
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Wang, Lihui
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Department of Production Engineering, KTH.
    Cloud-based Predictive Maintenance2015In: Proceedings of the 25th International Conference on Flexible Automation and Intelligent Manufacturing: Volume I - Designing for Advanced, High Value Manufacturing and Intelligent Systems for the 21st Century / [ed] Chike F. Oduoza, Wolverhampton, UK: The Choir Press , 2015, Vol. 1, 224-231 p.Conference paper (Refereed)
    Abstract [en]

    Maintenance of assembly and manufacturing equipment is crucial to ensure productivity, product quality, on-time delivery, and a safe working environment. Predictive Maintenance is an approach that utilises the condition monitoring data to predict the future machine conditions and makes decisions upon this prediction. The main aim of the presented research is to achieve an improvement in Predictive Condition-based Maintenance Decision Making through the Cloud-based approach with usage of wide information content. For the improvement it is crucial to identify and track not only condition related data but also context data. Context data allows better utilisation of condition monitoring data as well as analysis based on a machine population. The objective of this paper is to outline the first steps of a framework and methodology to handle and process maintenance, production and factory related data from the first lifecycle phase to the operation and maintenance phase.

  • 19.
    Schmidt, Bernard
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Wang, Lihui
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Department of Production Engineering, KTH Royal Institute of Technology, Stockholm, Sweden.
    Cloud-enhanced predictive maintenance2016In: The International Journal of Advanced Manufacturing Technology, ISSN 0268-3768, E-ISSN 1433-3015Article in journal (Refereed)
    Abstract [en]

    Maintenance of assembly and manufacturing equipment is crucial to ensure productivity, product quality, on-time delivery, and a safe working environment. Predictive maintenance is an approach that utilises the condition monitoring data to predict the future machine conditions and makes decisions upon this prediction. The main aim of the present research is to achieve an improvement in predictive condition-based maintenance decision making through a cloud-based approach with usage of wide information content. For the improvement, it is crucial to identify and track not only condition related data but also context data. Context data allows better utilisation of condition monitoring data as well as analysis based on a machine population. The objective of this paper is to outline the first steps of a framework and methodology to handle and process maintenance, production, and factory related data from the first lifecycle phase to the operation and maintenance phase. Initial case study aims to validate the work in the context of real industrial applications.

  • 20.
    Schmidt, Bernard
    et al.
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Wang, Lihui
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre. Department of Production Engineering, Royal Institute of Technology, Stockholm, Sweden.
    Contact-less and programming-less human-robot collaboration2013In: Forty Sixth CIRP Conference on Manufacturing Systems 2013 / [ed] Pedro F. Cunha, Elsevier, 2013, Vol. 7, 545-550 p.Conference paper (Refereed)
    Abstract [en]

    In today's manufacturing environment, safe human-robot collaboration is of paramount importance, to improve efficiency and flexibility. Targeting the safety issue, this paper presents an approach for human-robot collaboration in a shared workplace in close proximity, where real data driven 3D model of a robot and multiple depth images of the workplace are used for monitoring and decision-making to perform a task. The strategy for robot control depends on the current task and the information about the operator's presence and position. A case study of assembly is carried out in a robotic assembly cell with human collaboration. The results show that this approach can be applied in real-world applications such as human-robot collaborative assembly with human operators safeguarded at all time.

  • 21.
    Schmidt, Bernard
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Wang, Lihui
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Department of Production Engineering, Royal Institute of Technology, 100 44 Stockholm, Sweden.
    Depth camera based collision avoidance via active robot control2014In: Journal of manufacturing systems, ISSN 0278-6125, Vol. 33, no 4, 711-718 p.Article in journal (Refereed)
    Abstract [en]

    A new type of depth cameras can improve the effectiveness of safety monitoring in human–robot collaborative environment. Especially on today's manufacturing shop floors, safe human–robot collaboration is of paramount importance for enhanced work efficiency, flexibility, and overall productivity. Within this context, this paper presents a depth camera based approach for cost-effective real-time safety monitoring of a human–robot collaborative assembly cell. The approach is further demonstrated in adaptive robot control. Stationary and known objects are first removed from the scene for efficient detection of obstacles in a monitored area. The collision detection is processed between a virtual model driven by real sensors, and 3D point cloud data of obstacles to allow different safety scenarios. The results show that this approach can be applied to real-time work cell monitoring.

  • 22.
    Schmidt, Bernard
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Wang, Lihui
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Predictive Maintenance: Literature Review and Future Trends2015In: Proceedings of the 25th International Conference on Flexible Automation and Intelligent Manufacturing: Volume I - Designing for Advanced, High Value Manufacturing and Intelligent Systems for the 21st Century / [ed] Chike F. Oduoza, Wolverhampton, UK: The Choir Press , 2015, Vol. 1, 232-239 p.Conference paper (Refereed)
    Abstract [en]

    In manufacturing industry machines and systems become more advanced and complicated. Proper maintenance is crucial to ensure productivity, product quality, on-time delivery, and safe working environment. Recently, the importance of the predictive maintenance has been growing rapidly. Well applied predictive maintenance can be in many cases more cost effective than traditional corrective and preventive approaches to maintenance. Targeting this vibrant field, this paper reviews the literature of Predictive Maintenance (PdM). Published literature is systematically categorised and then methodically reviewed and analysed. Methodology for data acquisition, feature extraction, failure detection and prediction are presented. The connection between Maintenance field and Information Fusion has been highlighted. Statistical analysis based on Elsevier’s Scopus abstract and citation database has been performed. Various emerging trends in the field of Predictive Maintenance are identified to help specifying gaps in the literature and direct research efforts.

  • 23.
    Schmidt, Bernard
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Wang, Lihui
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. KTH Royal Institute of Technology, Stockholm, Sweden.
    Galar, Diego
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Semantic Framework for Predictive Maintenance in a Cloud Environment2017In: 10th CIRP Conference on Intelligent Computation in Manufacturing Engineering - CIRP ICME '16 / [ed] Roberto Teti, Doriana M D'Addona, Elsevier, 2017, Vol. 62, 583-588 p.Conference paper (Refereed)
    Abstract [en]

    Proper maintenance of manufacturing equipment is crucial to ensure productivity and product quality. To improve maintenance decision support, and enable prediction-as-a-service there is a need to provide the context required to differentiate between process and machine degradation. Correlating machine conditions with process and inspection data involves data integration of different types such as condition monitoring, inspection and process data. Moreover, data from a variety of sources can appear in different formats and with different sampling rates. This paper highlights those challenges and presents a semantic framework for data collection, synthesis and knowledge sharing in a Cloud environment for predictive maintenance.

  • 24.
    Wang, Lihui
    et al.
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Givehchi, Mohammad
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Schmidt, Bernard
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Adamson, Göran
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Robotic Assembly Planning and Control with Enhanced Adaptability2012In: 45th CIRP Conference on Manufacturing Systems 2012 / [ed] G. Chryssolouris, D. Mourtzis, Elsevier, 2012, Vol. 3, 173-178 p.Conference paper (Refereed)
    Abstract [en]

    The dynamic market today requires manufacturing companies to possess high degree of adaptability and flexibility in order to deal with shop-floor uncertainties. Such uncertainties as missing tools, part shortage, job delay, rush-order and unavailability of resources, etc. happen more often in assembly operations. Targeting this problem, this research proposes a function block enabled approach to achieving adaptability and flexibility in assembly planning and control. In particular, this paper presents our latest development using a robotic mini assembly cell for testing and validation of a function block enabled system capable of assembly and robot trajectory planning and control. It is expected that a better adaptability can be achieved by this approach.

  • 25.
    Wang, Lihui
    et al.
    KTH Royal Institute of Technology, Stockholm, Sweden.
    Mohammed, Abdullah
    KTH Royal Institute of Technology, Stockholm, Sweden.
    Wang, Xi Vincent
    KTH Royal Institute of Technology, Stockholm, Sweden.
    Schmidt, Bernard
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Energy-efficient robot applications towards sustainable manufacturing2017In: International journal of computer integrated manufacturing (Print), ISSN 0951-192X, E-ISSN 1362-3052, TCIM 1379099Article in journal (Refereed)
    Abstract [en]

    The cloud technology provides sustainable solutions to the modern industrial robotic cells. Within the context, the objective of this research is to minimise the energy consumption of robots during assembly in a cloud environment. Given a robot path and based on the inverse kinematics and dynamics of the robot from the cloud, a set of feasible configurations of the robot can be derived, followed by calculating the desirable forces and torques on the joints and links of the robot. Energy consumption is then calculated for each feasible configuration along the path. The ones with the lowest energy consumption are chosen. Since the energy-efficient robot configurations lead to reduced overall energy consumption, this approach becomes instrumental and can be applied to energy-efficient robotic assembly. This cloud-based energy-efficient approach for robotic applications can largely enhance the current practice as demonstrated by the results of three case studies, leading towards sustainable manufacturing.

  • 26.
    Wang, Lihui
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. KTH Royal Institute of Technology, Stockholm, Sweden.
    Mohammed, Abdullah
    KTH Royal Institute of Technology, Stockholm, Sweden.
    Wang, Xi Vincent
    KTH Royal Institute of Technology, Stockholm, Sweden.
    Schmidt, Bernard
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Recent Advancements of Smart Manufacturing: An Example of Energy-Efficient Robot2016In: Proceedings of the 26th International Conference on Flexible Automation and Intelligent Manufacturing, 2016, 884-892 p.Conference paper (Refereed)
    Abstract [en]

    The cloud technology provides sustainable solutions to the modern industrial robotic cells. Within the context, the objective of this research is to minimise the energy consumption of robots during assembly in a cloud environment. Given a trajectory and based on the inverse kinematics and dynamics of a robot from the cloud, a set of feasible configurations of the robot can be derived, followed by calculating the desirable forces and torques on the joints and links of the robot. Energy consumption is then calculated for each feasible configuration along the trajectory. The ones with the lowest energy consumption are chosen. Since the energy-efficient robot configurations lead to reduced overall energy consumption, this approach becomes instrumental and can be applied to energy-efficient robotic assembly.

  • 27.
    Wang, Lihui
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Department of Production Engineering, Royal Institute of Technology, Stockholm, Sweden.
    Schmidt, Bernard
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Givehchi, Mohammad
    Department of Production Engineering, Royal Institute of Technology, Stockholm, Sweden.
    Adamson, Göran
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Robotic Assembly Planning and Control with Enhanced Adaptability through Function Blocks2015In: The International Journal of Advanced Manufacturing Technology, ISSN 0268-3768, E-ISSN 1433-3015, Vol. 77, no 1-4, 705-715 p.Article in journal (Refereed)
    Abstract [en]

    Manufacturing companies today need to maintain a high level of flexibility and adaptability to deal with uncertainties on dynamic shop floors, including e.g. cutting tool shortage, part supply interruption, urgent job insertion or delay, and machine unavailability. Such uncertainties are characteristic in component assembly operations. Addressing the problem, we propose a new method using function blocks to achieve much improved adaptability in assembly planning and robot control. In this paper, we propose to use event-driven function blocks for robotic assembly, aiming to plan trajectory and execute assembly tasks in real-time. It is envisioned that this approach will achieve better adaptability if applied to real-world applications.

  • 28.
    Wang, Lihui
    et al.
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre. Department of Production Engineering, KTH Royal Institute of Technology, 100 44 Stockholm, Sweden.
    Schmidt, Bernard
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Nee, Andrew Y. C.
    Department of Mechanical Engineering, National University of Singapore.
    Vision-guided active collision avoidance for human-robot collaborations2013In: Manufacturing Letters, ISSN 2213-8463, Vol. 1, no 1, 5-8 p.Article in journal (Refereed)
    Abstract [en]

    This paper reports a novel methodology of real-time active collision avoidance in an augmented environment, where virtual 3D models of robots and real camera images of operators are used for monitoring and collision detection. A prototype system is developed and linked to robot controllers for adaptive robot control, with zero robot programming for end users. According to the result of collision detection, the system can alert an operator, stop a robot, or modify the robot's trajectory away from an approaching operator. Through a case study, it shows that this method can be applied to real-world applications such as human-robot collaborative assembly to safeguard human operators.

1 - 28 of 28
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