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  • 1.
    Ahmed, Mobyen Uddin
    et al.
    Mälardalen University, School of Innovation, Design and Engineering.
    Begum, Shahina
    Mälardalen University, School of Innovation, Design and Engineering.
    Olsson, Erik
    Mälardalen University, School of Innovation, Design and Engineering.
    Xiong, Ning
    Mälardalen University, School of Innovation, Design and Engineering.
    Funk, Peter
    Mälardalen University, School of Innovation, Design and Engineering.
    Case-Based Reasoning for Medical and Industrial Decision Support Systems2010In: Successful Case-based Reasoning Applications, Springer, 2010, p. 7-52Chapter in book (Other academic)
    Abstract [en]

    The amount of medical and industrial experience and knowledge is rapidly growing and it is almost impossible to be up to date with everything. The demand of decision support system (DSS) is especially important in domains where experience and knowledge grow rapidly. However, traditional approaches to DSS are not always easy to adapt to a flow of new experience and knowledge and may also show a limitation in areas with a weak domain theory. This chapter explores the functionalities of Case-Based Reasoning (CBR) to facilitate experience reuse both in clinical and in industrial decision making tasks. Examples from the field of stress medicine and condition monitoring in industrial robots are presented here to demonstrate that the same approach assists both for clinical applications as well as for decision support for engineers. In the both domains, DSS deals with sensor signal data and integrate other artificial intelligence techniques into the CBR system to enhance the performance in a number of different aspects. Textual information retrieval, Rule-based Reasoning (RBR), and fuzzy logic are combined together with CBR to offer decision support to clinicians for a more reliable and efficient management of stress. Agent technology and wavelet transformations are applied with CBR to diagnose audible faults on industrial robots and to package such a system. The performance of the CBR systems have been validated and have shown to be useful in solving such problems in both of these domains.

  • 2.
    Ahmed, Mobyen Uddin
    et al.
    Mälardalen University, Department of Computer Science and Electronics.
    Olsson, Erik
    Mälardalen University, Department of Computer Science and Electronics.
    Funk, Peter
    Mälardalen University, Department of Computer Science and Electronics.
    Xiong, Ning
    Mälardalen University, Department of Computer Science and Electronics.
    A Case-Based Reasoning System for Knowledge and Experience Reuse2007In: Proceedings of the 24th annual workshop of the Swedish Artificial Intelligence Society, 2007, p. 70-80Conference paper (Refereed)
    Abstract [en]

    Experience is one of the most valuable assets technicians and engineer have and may have been collected during many years and both from successful solutions as well as from very costly mistakes. Unfortunately industry rarely uses a systematic approach for experience reuse. This may be caused by the lack of efficient tools facilitating experience distribution and reuse. We propose a case-based approach and tool to facilitate experience reuse more systematically in industry. It is important that such a tool allows the technicians to give the problem case in a flexible way to increase acceptance and use. The proposed tool enables more structured handling of experience and is flexible and can be adapted to different situations and problems. The user is able to input text in a structured way and possibly in combination with other numeric or symbolic features. The system is able to identify and retrieve relevant similar experiences for reuse.

  • 3.
    Ahmed, Mobyen Uddin
    et al.
    Mälardalen University, Department of Computer Science and Electronics.
    Olsson, Erik
    Mälardalen University, Department of Computer Science and Electronics.
    Funk, Peter
    Mälardalen University, Department of Computer Science and Electronics.
    Xiong, Ning
    Mälardalen University, Department of Computer Science and Electronics.
    Efficient Condition Monitoring and Diagnosis Using a Case-Based Experience Sharing System2007In: The 20th International Congress and Exhibition on Condition Monitoring and Diagnostics Engineering Management, COMADEM 2007, Faro, Portugal, 2007, p. 305-314Conference paper (Refereed)
    Abstract [en]

    Industry has to adjust quickly to changes in their surroundings, for example reducing staff during recession and increasing staff when the market demands it. These factors may cause rapid loss of experience, collected during many years, or require experienced staff to spend considerable resources in training new staff, instead of focusing on production. This is recognised as very costly for companies and organisations today and also reduces competitiveness and productivity. Condition Monitoring, diagnostics and selection of efficient preventive or corrective actions is a task that often requires a high degree of expertise. This expertise is often gained through sometimes very expensive mistakes and can take many years to acquire leading to a few skilled experts. When they are not available due to changes in staff or retirements the company often faces serious problems that may be very expensive, e.g. leading to a reduced productivity.

    If some deviation occurs in a machine, a fault report is often written; an engineer makes a diagnosis and may order spare parts to repair the machine. Fault report, spare parts, required time and statistics on performance after repair are often stored in different databases but so far not systematically reused. In this paper we present a Case-Based experience sharing system that enables reuse of experience in a more efficient way compared with what is mostly practiced in industry today. The system uses Case-Based-Reasoning (CBR) and limited Natural Language Processing. An important aspect of the experience management tool is that it is user-friendly and web-based to promote efficient experience sharing. The system should be able to handle both experiences that are only in house as well as sharing experience with other industries when there is no conflicting interest. Such a CBR based tool enables efficient experience gathering, management and reuse in production industries. The tool will facilitate the users with an interactive environment to communicate with each other for sharing their experiences. Depend on the user; the security level of the system will be varied to share knowledge among the collaborating companies.

    The system identifies the most relevant experiences to assess and resolve the current situation. The experience is stored and retrieved as a case in the collaborative space where experience from various companies may have been stored under many years. Reusing experience and avoiding expensive mistakes will increase the participating companies' competitiveness and also transfer valuable experience to their employees. One of the benefits is also the opportunity and facility to identify people with similar tasks and problems at different companies and enable them to share their experience, e.g. if a technician has solved a similar problem recently and is in the near, the most efficient solution may be to call the expert and ask for assistance. In future, one may access this tool through his/her mobile device via wireless or mobile communications using Global Positioning System, GPS, enables the system to suggest experts nearby, willing and able to share the knowledge and quickly assist in resolve the problem.

  • 4.
    Bengtsson, Marcus
    et al.
    Mälardalen University, School of Innovation, Design and Engineering.
    Fundin, Anders
    Mälardalen University, School of Innovation, Design and Engineering.
    Deleryd, Mats
    Mälardalen University, School of Innovation, Design and Engineering.
    Salonen, Antti
    Mälardalen University, School of Innovation, Design and Engineering.
    Olsson, Erik
    Mälardalen University, School of Innovation, Design and Engineering.
    Funk, Peter
    Mälardalen University, School of Innovation, Design and Engineering.
    Andersson, Carina
    Mälardalen University, School of Innovation, Design and Engineering.
    Qureshi, Hassan
    Mälardalen University, School of Innovation, Design and Engineering.
    Integrating Quality and Maintenance Development: Opportunities and Implications2010In: Proceedings of the 23rd International Congress on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2010): Advances in Maintenance and Condition Diagnosis Technologies towards Sustainable Society / [ed] S. Okumura, T. Kawai, P. Chen, and R. B. K. N. Rao, 2010, p. 821-828Conference paper (Refereed)
    Abstract [en]

    Today, the drive in many organizations is to focus on reducing production costs while increasing customer satisfaction. One key to succeed with these goals is to develop and improve both quality and maintenance in production as well as quality and maintenance in early phases of the development processes. The purpose of this paper is to discuss how and motivate why research within quality and maintenance development may interact, in order to help companies meet customer demand while at the same time increase productivity. The paper is based on ideas and research perspectives of the newly formed competence group on ‘Quality- and Maintenance Development’ at the School of Innovation, Design and Engineering at the Malardalen University, Sweden. This paper elaborates on the concepts of Quality and Maintenance, its important integration, and provides some examples of ongoing research projects within the competence group.

  • 5.
    Bengtsson, Marcus
    et al.
    Mälardalen University, School of Innovation, Design and Engineering.
    Olsson, Erik
    Mälardalen University, School of Innovation, Design and Engineering.
    Funk, Peter
    Mälardalen University, School of Innovation, Design and Engineering.
    Jackson, Mats
    Mälardalen University, School of Innovation, Design and Engineering.
    Technical Design of Condition Based Maintenance Systems - A Case Study Using Sound Analysis and Case-Based Reasoning2004Conference paper (Refereed)
    Abstract [en]

    Productivity is a key weapon for manufacturing companies to stay competitive in a continuous growing global market. Increased productivity can be achieved through increased availability. This has directed focus on different maintenance types and maintenance strategies. Increased availability through efficient maintenance can be achieved through less corrective maintenance actions and more accurate preventive maintenance intervals. Condition Based Maintenance (CBM) is a technology that strives to identify incipient faults before they become critical which enables more accurate planning of the preventive maintenance. CBM can be achieved by utilizing complex technical systems or by humans manually monitoring the condition by using their experience, normally a mixture of both is used. Although CBM holds a lot of benefits compared to other maintenance types it is not yet commonly utilized in industry. One reason for this might be that the maturity level in complex technical CBM system is too low. This paper will acknowledge this possible reason, although not trying to resolve it, but focusing on system technology with component strategy and an open approach to condition parameters as the objective is fulfilled. This paper will theoretically discuss the technical components of a complete CBM system approach and by a case study illustrate how a CBM system for industrial robot fault detection/diagnosis can be designed using the Artificial Intelligence method Case-Based Reasoning and sound analysis.

  • 6.
    Funk, Peter
    et al.
    Mälardalen University, Department of Computer Science and Electronics.
    Olsson, Erik
    Mälardalen University, Department of Computer Science and Electronics.
    Bengtsson, Marcus
    Mälardalen University, Department of Computer Science and Electronics.
    Xiong, Ning
    Mälardalen University, Department of Computer Science and Electronics.
    Case-Based Experience Reuse and Agents for Efficient Health Monitoring, Prevention and Corrective Actions2006In: Proceedings of the 19th International Congress on Condition, COMADEM 2006, Luleå, Sweden, 2006, p. 445-453Conference paper (Refereed)
    Abstract [en]

    Experienced staffs acquire their experience during many years of practice, and sometimes also through expensive mistakes. This knowledge is often lost when technicians retire, or if companies need to downsize during periods of reduced sale. When scaling up production, new staff requires training and may repeat similar mistakes. Another issue that may be costly is when monitoring systems repeatedly give false alarms, causing expensive loss of production capacity and resulting in technicians losing trust in the systems and in worst case, switch them off. If monitoring systems could learn from previous experience for both correct and false alarms, the reliability and trust in the monitoring systems would increase. Moreover, connecting alarms to either equipment taking automatic actions or recommend actions based on the current situations and previous experience would be valuable.

    An engineer repeating the same task a second time is often able to perform the task in 1/3 of the time it took at the first time. Most corrective and preventive actions for a particular machine type have been carried out before. This past experience holds a large potential for time savings, predictability and reduced risk if an efficient experience transfer can be accomplished. But building large complex support system is not always the ideal way. We propose instead localized intelligent agents, able to either autonomously perform the necessary actions or aid a human in the decision making process by providing the necessary information needed to make an informed and validated decision.

  • 7.
    Karlsson, Christer
    et al.
    Mälardalen University, School of Innovation, Design and Engineering.
    Olsson, Erik
    Mälardalen University, School of Innovation, Design and Engineering.
    Funk, Peter
    Mälardalen University, School of Innovation, Design and Engineering.
    HYBRID EARLY WARNING SYSTEMS2009In: COMADEM 2009 (in press), Spain, 2009Conference paper (Refereed)
    Abstract [en]
    New tools are needed to reach high goals for uptime and availability in industrial processes. Early warning of developing faults is one part of the strategy to reach these goals. A single method rarely meets all requirements, but combining methods and techniques in a hybrid system offers advantages and can overcome limitations in the individual approaches. Methods considered are physical models, artificial neural networks, and case-based reasoning. The paper discusses the pros and cons, strengths and weaknesses of the three methods and three combinations of hybrid solutions in order to assist in select a suitable combination for a specific early warning challenge ahead.
  • 8.
    Olsson, Ella
    et al.
    Saab AB Aerosystems, Sweden.
    Funk, Peter
    Mälardalen University, School of Innovation, Design and Engineering.
    Andersson, Alf
    Volvo Car Corporation Manufacturing Engineering, Sweden.
    Case-based reasoning applied to geometric measurements for decision support in manufacturing2013In: International Journal of Systems Assurance Engineering and Management, ISSN 0975-6809, E-ISSN 0976-4348, Vol. 4, no 3, p. 223-230Article in journal (Refereed)
    Abstract [en]

    Measurements from products are continuously collected to allow adjustments in the production line to certify a feasible product quality. Case-based reasoning is a promising methodology for this type of quality assurance. It allows product measurements and its related adjustments to the production line to be stored as cases in a case-based reasoning system. The idea is to describe an event of adjustments based on deviations in geometric measurement points on a product and connect these measurements to their correlated adjustments done to the production line. Experience will implicitly be stored in each case in the form of uniquely weighted measurement points according to their positive influence on adjustments. Methods have been developed in order to find these positive correlations between measurements and adjustments by analysing a set of historical product measurement and their following adjustments. Each case saved in the case base will be “quality assured” according to this methods and only cases containing strong positive correlations will be used by the system. The correlations will be used to supply each case with its own set individual weights.

  • 9.
    Olsson, Erik
    Mälardalen University, Department of Computer Science and Electronics.
    Diagnosis of Machines within Industry using Sensor Signals and Case-Based Reasoning2005Licentiate thesis, comprehensive summary (Other scientific)
  • 10.
    Olsson, Erik
    Mälardalen University, School of Innovation, Design and Engineering.
    Fault Diagnosis of Industrial Machines using Sensor Signals and Case-Based Reasoning2009Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Industrial machines sometimes fail to operate as intended. Such failures can be more or less severe depending on the kind of machine and the circumstances of the failure. E.g. the failure of an industrial robotcan cause a hold-up of an entire assembly line costing the affected company large amounts of money each minute on hold. Research is rapidly moving forward in the area of artificial intelligence providing methods for efficient fault diagnosis of industrial machines. The nature of fault diagnosis of industrial machines lends itself naturally to case-based reasoning. Case-based reasoning is a method in the discipline of artificial intelligence based on the idea of assembling experience from problems and their solutions as ”cases” for reuse in solving future problems. Cases are stored in a case library, available for retrieval and reuse at any time.By collecting sensor data such as acoustic emission and current measurements from a machine and representing this data as the problem part of a case and consequently representing the diagnosed fault as the solution to this problem, a complete series of the events of a machine failure and its diagnosed fault can be stored in a case for future use.

  • 11. Olsson, Erik
    Identifying Discriminating Features in Time Series Data for Diagnosis of Industrial Machines2007In: The 24th annual workshop of the Swedish Artificial Intelligence Society, May, 2007, Boras, Sweden,, 2007Conference paper (Refereed)
    Abstract [en]

    Reducing the inherent high dimensionality in time series data is a desirable goal. Algorithms used for classi¯cation can easily be misguided if presented with data of to high dimension. E.g. the k-nearest neighbor algorithm which is often used for case-based classi¯cation per- forms best on smaller dimensions with less than 20 attributes. In this paper we address the problem using a time series case base and a feature discrimination approach incorporating an unsupervised combination of a search function based on statistical feature discrimination and a crite- rion function ¯nding the global maximum of discriminating power in the range the search function. Feature vectors for case indexing is computed with respect to this information. For evaluation, previously classi¯ed cur- rent measurements from an electrical motor driving the gearbox of axis 4 on an industrial robot were used. The results were promising and we managed to correctly classify measurements from healthy and unhealthy gearboxes.

  • 12.
    Olsson, Erik
    Mälardalen University, School of Innovation, Design and Engineering.
    Using Cased-Based Reasoning Domain Knowledge to Train a Back Propagation NeuralNetwork in order to Classify Gear Faults in an Industrial Robot2008In: 21st International Congress andExhibition. Condition Monitoring and Diagnostic Engineering Management. COMADEM 2008., Prague: Czech Society for Non-Destructive Testing , 2008, p. 377-384Conference paper (Refereed)
    Abstract [en]

    The classification performance of a back propagation neural network classifier highly depends on itstraining process. In this paper we use the domain knowledge stored in a Case-based reasoning system inorder to train a back propagation neural network to classify gear faults in an industrial robot. Ourapproach is to compile domain knowledge from a Case-based reasoning system using attributes frompreviously stored cases. These attributes holds vital information usable in the training process. Ourapproach may be usable when a light-weight classifier is wanted due to e.g. lack of computing power orwhen only a part of the knowledge stored in the case base of a large Case-based reasoning system isneeded. Further, no use of the usual sensor signal classification steps such as filtering and featureextraction are needed once the neural network classifier is successfully trained.

  • 13.
    Olsson, Erik
    et al.
    Mälardalen University, Department of Computer Science and Electronics.
    Ahmed, Mobyen Uddin
    Mälardalen University, Department of Computer Science and Electronics.
    Funk, Peter
    Mälardalen University, Department of Computer Science and Electronics.
    Xiong, Ning
    Mälardalen University, Department of Computer Science and Electronics.
    A CASE STUDY OF COMMUNICATION IN A DISTRIBUTED MULTI-AGENT SYSTEM IN A FACTORY PRODUCTION ENVIRONMENT2007Conference paper (Refereed)
    Abstract [en]

    A Distributed Multi-Agent System representing the behaviour of a machine maintenance procedure in a factory production environment is modelled using the BRIC language. The model provides an overview and simplification of the communication in the maintenance procedure. The model involves two distributed factory environments, each equipped with a Maintenance Agent and an Experience Sharing Agent. Maintenance agents can be seen as experts in interpreting local sensor data from the machine being observed. They have some basic domain knowledge about when to bring the findings to the attention of an agent, human or system. An agent is also autonomous and may have the trust to shut down a process. The maintenance agent will ask other agents or humans for assistance if bringing the macine ito working order is beyond the agent's ability. Necessary information about what maintenance actions to perform is provided by an Experience Sharing Agent which has the ability to identify past experience relevant for the current situation and thus beeing able to help the human to make a better and more informed decision avoiding previously, sometimes very costly mistakes.

  • 14.
    Olsson, Erik
    et al.
    Mälardalen University, School of Innovation, Design and Engineering.
    Funk, Peter
    Mälardalen University, School of Innovation, Design and Engineering.
    Agent-Based Monitoring using Case-Based Reasoning for Experience Reuse and Improved Quality2009In: Journal of Quality in Maintenance Engineering, ISSN 1355-2511, Vol. 15, no 2, p. 179-192Article in journal (Refereed)
    Abstract [en]

    Purpose – The purpose with this paper is to propose an agent-based condition monitoringsystem for use in industrial applications. An intelligent maintenance agent is described that isable to autonomously perform necessary actions and/or aid a human in the decision makingprocess. An example is presented as a case-study from manufacturing of industrial robots.Design/methodology/approach – The paper is mainly based on a case-study performed at alarge multi-national company aiming to explore the usefulness of case-based experience reusein production.Findings – This paper presents a concept of case-based experience reuse in production. Amaintenance agent using a Case-Based Reasoning approach to collect, preserve and reuseavailable experience in the form of sound recordings exemplifies this concept. Sound fromnormal and faulty robot gearboxes are recorded during the production end test and stored in acase library together with their diagnosis results. Given an unclassified sound signal, relevantcases are retrieved to aid a human in the decision making process. The maintenance agentdemonstrated good performance by making right judgments in 91% of all the tests, which isbetter than an inexperienced technician.Originality/value – The main focus of this paper is to show how to perform efficientexperience reuse in modern production industry to improve quality of products. Twoapproaches are used: a case-study describing an example of experience reuse in productionusing a fault diagnosis system recognizing and diagnosing audible faults on industrial robotsand an efficient approach on how to package such a system using the agent paradigm and agent architecture.

  • 15.
    Olsson, Erik
    et al.
    Mälardalen University, School of Innovation, Design and Engineering.
    Funk, Peter
    Mälardalen University, School of Innovation, Design and Engineering.
    Andersson, Alf
    Mälardalen University, School of Innovation, Design and Engineering.
    Case-Based Reasoning Applied to Geometric Production Measurements2010In: 1st International Workshop and Congress on eMaintenance June 22-24, 2010 in Lulea, Sweden, Lulea, Sweden, 2010Conference paper (Refereed)
    Abstract [en]

    Measurements from products are continuously collected to allow adjustments in the production line to certify a feasible product quality. Case-Based Reasoning is a promising methodology for this type of quality assurance. It allows product measurements and its related adjustments to the production line to be stored as cases in a Case-Based Reasoning system. The idea is to describe an event of adjustments based on deviations in geometric measurement points on a product and connect these measurements to their correlated adjustments done to the production line. Experience will implicitly be stored in each case in the form of uniquely weighted measurement points according to their positive influence on adjustments. Methods have been developed in order to find these positive correlations between measurements and adjustments by analysing a set of historical product measurement and their following adjustments. Each case saved in the case base will be "quality assured" according to this methods and only cases containing strong positive correlations will be used by the system. The correlations will be used to supply each case with its own set individual weights.

  • 16.
    Olsson, Erik
    et al.
    Mälardalen University, Department of Computer Science and Electronics.
    Funk, Peter
    Mälardalen University, Department of Computer Science and Electronics.
    Bengtsson, Marcus
    Mälardalen University, Department of Innovation, Design and Product Development.
    Fault Diagnosis of Industrial Robots using Acoustic Signals and Case-Based Reasoning2004In: Case-Based Reasoning. ECCBR 2004. Lecture Notes in Computer Science, vol 3155, 2004, p. 686-701Conference paper (Refereed)
    Abstract [en]

    In industrial manufacturing rigorous testing is used to ensure that the delivered products meet their specifications. Mechanical maladjustment or faults often show their presence as deviations compared to a normal sound pro-file. This is the case in robot assembly, the selected application domain for the system. Manual diagnosis based on sound requires extensive experience, and the experience is often acquired through costly mistakes and reduced production efficiency or quality loss caused by missed faults. The acquired experience is also difficult to preserve and transfer, and often lost if personnel leave the task of testing and fault diagnosis. We propose a Case-Based Reasoning approach to collect and preserve experience. The solution enables fast experience transfer and leads to less experienced personnel required to make more reliable and informed testing. Sounds from normal and faulty equipment are recorded and stored in a case library together with a diagnosis. Addition of new validated sound profiles continuously improves the system’s performance. The system can preserve and transfer experience between technicians, reducing overall fault identification time and increases quality by reduced number of missed faults. The original sound recordings are stored in form of the extracted features to-gether with other experience, e.g. instructions, additional tests, advice, user feedback etc.

  • 17.
    Olsson, Erik
    et al.
    Mälardalen University, Department of Computer Science and Electronics.
    Funk, Peter
    Mälardalen University, Department of Computer Science and Electronics.
    Xiong, Ning
    Mälardalen University, Department of Computer Science and Electronics.
    Fault Diagnosis in Industry Using Sensor Readings and Case-Based Reasoning2004In: Journal of Intelligent & Fuzzy Systems, ISSN 1064-1246, Vol. 15, no 1, p. 41-46Article in journal (Refereed)
    Abstract [en]

    Fault diagnosis of industrial equipments becomes increasingly important for improving the quality of manufacturing and reducing the cost for product testing. Developing a fast and reliable diagnosis system presents a challenge issue in many complex industrial scenarios. The major difficulties therein arise from contaminated sensor readings caused by heavy background noise as well as the unavailability of experienced technicians for support. In this paper we propose a novel method for diagnosis of faults by means of case-based reasoning and signal processing. The received sensor signals are processed by wavelet analysis to filter out noise and at the same time to extract a group of related features that constitutes a reduced representation of the original signal. The derived feature vector is then forwarded to a classification component that uses case-based reasoning to recommend a fault class for the probe case. This recommendation is based on previously classified cases in a case library. Case-based diagnosis has attractive properties in that it enables reuse of past experiences whereas imposes no demand on the size of the case base. The proposed approach has been applied to fault diagnosis of industrial robots at ABB Robotics and the results of experiments are very promising.

  • 18.
    Olsson, Erik
    et al.
    Mälardalen University, Department of Computer Science and Electronics.
    Hedelind, Mikael
    Mälardalen University, Department of Innovation, Design and Product Development.
    Ahmed, Mobyen Uddin
    Mälardalen University, Department of Computer Science and Electronics.
    Funk, Peter
    Mälardalen University, Department of Computer Science and Electronics.
    EXPERIENCE REUSE BETWEEN MOBILE PRODUCTION MODULES - AN ENABLER FOR THE FACTORY-IN-A-BOX CONCEPT2007Conference paper (Refereed)
    Abstract [en]

    Today's increased demand for flexible and fast reconfiguration of production systems is seen as one of the key factors for survival by many branches, especially small and medium sized enterprises. To enable adaptable and flexible production, we propose an integrated experience reuse system assisting in setup, operation, maintenance and repair. We present three subsystems that facilitate experience reuse between different engineers and operators working with standardised production modules. It is composed of three separate software components enabling: a) easy programming and control of robot cells, b) monitoring and condition based maintenance, c) distributed experience reuse. The results presented in this paper have been developed within the Factory-in-a-Box project, the ExAct project and the Eken project.

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