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Schmidt, B. & Wang, L. (2018). Cloud-enhanced predictive maintenance. The International Journal of Advanced Manufacturing Technology, 99(1-4), 5-13
Open this publication in new window or tab >>Cloud-enhanced predictive maintenance
2018 (English)In: The International Journal of Advanced Manufacturing Technology, ISSN 0268-3768, E-ISSN 1433-3015, Vol. 99, no 1-4, p. 5-13Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Springer, 2018
Keywords
Predictive maintenance, Condition-based maintenance, Context awareness, Cloud manufacturing
National Category
Reliability and Maintenance
Research subject
Technology; Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-12331 (URN)10.1007/s00170-016-8983-8 (DOI)000445800600002 ()2-s2.0-85061379865 (Scopus ID)
Available from: 2016-06-07 Created: 2016-06-07 Last updated: 2019-02-22Bibliographically approved
Schmidt, B., Gandhi, K. & Wang, L. (2018). Diagnosis of machine tools: assessment based on double ball-bar measurements from a population of similar machines. Paper presented at 51st CIRP Conference on Manufacturing Systems, Stockholm, May 16-18, 2018. Procedia CIRP, 72, 1327-1332
Open this publication in new window or tab >>Diagnosis of machine tools: assessment based on double ball-bar measurements from a population of similar machines
2018 (English)In: Procedia CIRP, ISSN 2212-8271, E-ISSN 2212-8271, Vol. 72, p. 1327-1332Article in journal (Refereed) Published
Abstract [en]

The presented work is toward population-based predictive maintenance of manufacturing equipment with consideration of the automaticselection of signals and processing methods. This paper describes an analysis performed on double ball-bar measurement from a population ofsimilar machine tools. The analysis is performed after aggregation of information from Computerised Maintenance Management System,Supervisory Control and Data Acquisition, NC-code and Condition Monitoring from a time span of 4 years. Economic evaluation is performedwith use of Monte Carlo simulation based on data from real manufacturing setup.

Keywords
population based maintenance, condition monitoring, automatic signal selection
National Category
Reliability and Maintenance
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-15117 (URN)10.1016/j.procir.2018.03.208 (DOI)2-s2.0-85049577336 (Scopus ID)
Conference
51st CIRP Conference on Manufacturing Systems, Stockholm, May 16-18, 2018
Available from: 2018-05-02 Created: 2018-05-02 Last updated: 2018-10-31Bibliographically approved
Wang, L., Mohammed, A., Wang, X. V. & Schmidt, B. (2018). Energy-efficient robot applications towards sustainable manufacturing. International journal of computer integrated manufacturing (Print), 31(8), 692-700
Open this publication in new window or tab >>Energy-efficient robot applications towards sustainable manufacturing
2018 (English)In: International journal of computer integrated manufacturing (Print), ISSN 0951-192X, E-ISSN 1362-3052, Vol. 31, no 8, p. 692-700Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Taylor & Francis, 2018
Keywords
energy-efficiency, robot configuration, trajectory, robotic assembly, cloud manufacturing
National Category
Robotics
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-14146 (URN)10.1080/0951192X.2017.1379099 (DOI)000436966300003 ()2-s2.0-85029705478 (Scopus ID)
Available from: 2017-09-22 Created: 2017-09-22 Last updated: 2018-09-25Bibliographically approved
Schmidt, B. & Wang, L. (2018). Predictive Maintenance of Machine Tool Linear Axes: A Case from Manufacturing Industry. Paper presented at 28th International Conference on Flexible Automation and Intelligent Manufacturing (FAIM2018) June 11-14, 2018, Columbus, OH, USA. Procedia Manufacturing, 17, 118-125
Open this publication in new window or tab >>Predictive Maintenance of Machine Tool Linear Axes: A Case from Manufacturing Industry
2018 (English)In: Procedia Manufacturing, E-ISSN 2351-9789, Vol. 17, p. 118-125Article in journal (Refereed) Published
Abstract [en]

In sustainable manufacturing, the proper maintenance is crucial to minimise the negative environmental impact. In the context of Cloud Manufacturing, Internet of Things and Big Data, amount of available information is not an issue, the problem is to obtain the relevant information and process them in a useful way. In this paper a maintenance decision support system is presented that utilises information from multiple sources and of a different kind. The key elements of the proposed approach are processing and machine learning method evaluation and selection, as well as estimation of long-term key performance indicators (KPIs) such as a ratio of unplanned breakdowns or a cost of maintenance approach. Presented framework is applied to machine tool linear axes. Statistical models of failures and Condition Based Maintenance (CBM) are built based on data from a population of 29 similar machines from the period of over 4 years and with use of proposed processing approach. Those models are used in simulation to estimate the long-term effect on selected KPIs for different strategies. Simple CBM approach allows, in the considered case, a cost reduction of 40% with the number of breakdowns reduced 6 times in respect to an optimal time-based approach.

Place, publisher, year, edition, pages
Elsevier, 2018
Keywords
predictive maintenance, condition monitoring, machine tool
National Category
Reliability and Maintenance
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-16410 (URN)10.1016/j.promfg.2018.10.022 (DOI)000471035200015 ()2-s2.0-85060444616 (Scopus ID)
Conference
28th International Conference on Flexible Automation and Intelligent Manufacturing (FAIM2018) June 11-14, 2018, Columbus, OH, USA
Available from: 2018-11-19 Created: 2018-11-19 Last updated: 2019-06-27Bibliographically approved
Schmidt, B. (2018). Toward Predictive Maintenance in a Cloud Manufacturing Environment: A population-wide approach. (Doctoral dissertation). Skövde: University of Skövde
Open this publication in new window or tab >>Toward Predictive Maintenance in a Cloud Manufacturing Environment: A population-wide approach
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The research presented in this thesis is focused on improving industrial maintenance by using better decision support that is based on a wider range of input information. The core objective is to research how to integrate information from a population of similar monitored objects. The data to be aggregated comes from multiple disparate sources including double ball-bar circularity tests, the maintenance management system, and the machine tool’s controller. Various data processing and machine learning methods are applied and evaluated. Finally, an economic evaluation of the proposed approach is presented. The work performed is presented in five appended papers.

Paper I presents an investigation of cloud-based predictive maintenance concepts and their potential benefits and challenges.

Paper II presents the results of an investigation of available and potentially useful data from the perspective of predictive analytics with a focus on the linear axes of machine tools.

Paper III proposes a semantic framework for predictive maintenance, and investigates means of acquiring relevant information from different sources (i.e., ontology-based data retrieval).

Paper IV presents a method for data integration. The method is applied to data obtained from a real manufacturing setup. Simulation-based evaluation is used to compare results with a traditional time-based approach.

Paper V presents the results from additional simulation-based experiments based on the method from Paper IV. The aim is to improve the method and provide additional information that can support maintenance decision-making (e.g., determining the optimal interval for inspections).

The method developed in this thesis is applied to a population of linear axes from a set of similar multipurpose machine tools. The linear axes of machine tools are very important, as their performance directly affects machining quality. Measurements from circularity tests performed using a double ball-bar measuring device are combined with event and context information to build statistical failure and classification models. Based on those models, a decision-making process is proposed and evaluated. In the analysed case, the proposed approach leads to direct maintenance cost reduction of around 40 % compared to a time-based approach.

Place, publisher, year, edition, pages
Skövde: University of Skövde, 2018
Series
Dissertation Series ; 20 (2018)
Keywords
predictive maintenance, condition monitoring, population-wide approach, machine learning, double ball-bar measurement
National Category
Reliability and Maintenance
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-15120 (URN)978-91-984187-2-9 (ISBN)
Public defence
2018-05-21, Insikten, Kanikegränd 3A, Skövde, 10:00 (English)
Opponent
Supervisors
Projects
IPSI Research School
Available from: 2018-05-03 Created: 2018-05-02 Last updated: 2018-05-03Bibliographically approved
Gandhi, K., Schmidt, B. & Ng, A. H. C. (2018). Towards data mining based decision support in manufacturing maintenance. Paper presented at 51st CIRP Conference on Manufacturing Systems, Stockholm, May 16-18, 2018. Procedia CIRP, 72, 261-265
Open this publication in new window or tab >>Towards data mining based decision support in manufacturing maintenance
2018 (English)In: Procedia CIRP, ISSN 2212-8271, E-ISSN 2212-8271, Vol. 72, p. 261-265Article in journal (Refereed) Published
Abstract [en]

The current work presents a decision support system architecture for evaluating the features representing the health status to predict maintenance actions and remaning useful life of component. The evaluation is possible through pattern analysis of past and current measurements of the focused research components. Data mining visualization tools help in creating the most suitable patterns and learning insights from them. Estimations like features split values or measurement frequency of the component is achieved through classification methods in data mining. This paper presents how the quantitative results generated from data mining can be used to support decision making of domain experts.

Place, publisher, year, edition, pages
Elsevier, 2018
Keywords
Maintenance, Decision Support System, Data Mining, Classification Methods, Knowledge Extraction
National Category
Information Systems
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-15901 (URN)10.1016/j.procir.2018.03.076 (DOI)2-s2.0-85049600893 (Scopus ID)
Conference
51st CIRP Conference on Manufacturing Systems, Stockholm, May 16-18, 2018
Available from: 2018-07-01 Created: 2018-07-01 Last updated: 2018-10-31Bibliographically approved
Mohammed, A., Schmidt, B. & Wang, L. (2017). Active collision avoidance for human-robot collaboration driven by vision sensors. International journal of computer integrated manufacturing (Print), 30(9), 970-980
Open this publication in new window or tab >>Active collision avoidance for human-robot collaboration driven by vision sensors
2017 (English)In: International journal of computer integrated manufacturing (Print), ISSN 0951-192X, E-ISSN 1362-3052, Vol. 30, no 9, p. 970-980Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Taylor & Francis, 2017
Keywords
collision detection, collaborative assembly, safety, vision sensor
National Category
Robotics
Research subject
Production and Automation Engineering; INF201 Virtual Production Development
Identifiers
urn:nbn:se:his:diva-13825 (URN)10.1080/0951192X.2016.1268269 (DOI)000402991300006 ()2-s2.0-85006100717 (Scopus ID)
Available from: 2017-06-22 Created: 2017-06-22 Last updated: 2019-01-24Bibliographically approved
Schmidt, B., Gandhi, K., Wang, L. & Galar, D. (2017). Context preparation for predictive analytics – a case from manufacturing industry. Journal of Quality in Maintenance Engineering, 23(3), 341-354
Open this publication in new window or tab >>Context preparation for predictive analytics – a case from manufacturing industry
2017 (English)In: Journal of Quality in Maintenance Engineering, ISSN 1355-2511, E-ISSN 1758-7832, Vol. 23, no 3, p. 341-354Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Emerald Publishing Limited, 2017
Keywords
Predictive maintenance, Context awareness, Condition monitoring
National Category
Reliability and Maintenance
Research subject
Production and Automation Engineering; INF201 Virtual Production Development
Identifiers
urn:nbn:se:his:diva-14033 (URN)10.1108/JQME-10-2016-0050 (DOI)000412478700007 ()2-s2.0-85027991065 (Scopus ID)
Available from: 2017-08-24 Created: 2017-08-24 Last updated: 2019-11-21Bibliographically approved
Mohammed, A., Schmidt, B. & Wang, L. (2017). Energy-Efficient Robot Configuration for Assembly. Journal of manufacturing science and engineering, 139(5), Article ID 051007.
Open this publication in new window or tab >>Energy-Efficient Robot Configuration for Assembly
2017 (English)In: Journal of manufacturing science and engineering, ISSN 1087-1357, E-ISSN 1528-8935, Vol. 139, no 5, article id 051007Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
The American Society of Mechanical Engineers, 2017
Keywords
industrial robots, machine-tools, consumption, optimization, constraints
National Category
Robotics
Research subject
Production and Automation Engineering; INF201 Virtual Production Development
Identifiers
urn:nbn:se:his:diva-13555 (URN)10.1115/1.4034935 (DOI)000399395000009 ()2-s2.0-84995900489 (Scopus ID)
Available from: 2017-05-11 Created: 2017-05-11 Last updated: 2019-11-25Bibliographically approved
Schmidt, B., Wang, L. & Galar, D. (2017). Semantic Framework for Predictive Maintenance in a Cloud Environment. In: Roberto Teti, Doriana M D'Addona (Ed.), 10th CIRP Conference on Intelligent Computation in Manufacturing Engineering - CIRP ICME '16: . Paper presented at 10th CIRP Conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME '16, Ischia, Italy, 20-22 July 2016 (pp. 583-588). Elsevier, 62
Open this publication in new window or tab >>Semantic Framework for Predictive Maintenance in a Cloud Environment
2017 (English)In: 10th CIRP Conference on Intelligent Computation in Manufacturing Engineering - CIRP ICME '16 / [ed] Roberto Teti, Doriana M D'Addona, Elsevier, 2017, Vol. 62, p. 583-588Conference paper, Published 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.

Place, publisher, year, edition, pages
Elsevier, 2017
Series
Procedia CIRP, ISSN 2212-8271 ; 62
Keywords
Predictive maintenance, Knowledge management, Cloud manufacturing
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Production and Automation Engineering; INF201 Virtual Production Development
Identifiers
urn:nbn:se:his:diva-13568 (URN)10.1016/j.procir.2016.06.047 (DOI)000414525400101 ()2-s2.0-85020714569 (Scopus ID)
Conference
10th CIRP Conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME '16, Ischia, Italy, 20-22 July 2016
Available from: 2017-05-19 Created: 2017-05-19 Last updated: 2019-01-24Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-8906-630X

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