| 1. |
- Olsson, Rolf, 1969-
(författare)
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MANAGING PROJECT UNCERTAINTY BY USING AN ENHANCED RISK MANAGEMENT PROCESS
- 2006
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- An increasing number of companies are focusing their efforts on project management. Project management is frequently used as an enabler for meeting an uncertain and turbulent environment. Consequently, the overall effectiveness of the project management process is essential for long-term profitability. The aim and final effects of project management are to predict the outcome, i.e. cost, time and quality. However, uncertainty is inherent in the objectives of the project itself, as we use assumptions and expectations in defining and realizing the outcome of the project. A project’s ability to identify and react to uncertainty will influence the outcome of the project. Presently, risk management processes exist in several forms and are often used to manage uncertainty. However, it is frequently argued in academia as well as for the practitioner that risk management does not live up to expected results. The overall objective of this research is to improve the process for managing risks and opportunities within a project organization. The research starts from the single project view, followed by the strategic link to business strategy by including the project portfolio management perspective. Finally, the research focuses on opportunities and the ability of a project to realize them. Thus, the research questions addressed concern how risk is conceived in a theoretical global context and how this would assist in developing a methodology for risk management in an international project organization. They also involve how risk management within a project portfolio could be conducted and its effectiveness measured. Finally, the research questions also address how the management of opportunities could be improved. This research includes the development of four methodologies, based on industrial need. A holistic approach with a systems perspective has been used in order to handle the complexity of the research task. Both empirical and theoretical material has been used for developing the proposed methodologies. The developed methodologies for project risk management and the measures of its effectiveness have been tested and improved over a five-year period within the complete case company. Subsequently, two of them were implemented. The developed methodologies show that the risk management process in a single project does not foster learning and is not directly applicable within a portfolio of projects. Furthermore, the risk management process is not able to address all types of uncertainty. The project manager is a major factor in an effective management of uncertainty. When identifying and managing opportunity, having the ability to create a holistic view, to oversee both customer expectations, and to communicate project related information are important factors. Furthermore, the implementation also showed that it is actually possible, through the consistent use of a risk management process, to develop a cultural behavior within an organization that is much more preventive and proactive than before.
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| 2. |
- Andersson, Tim, et al.
(författare)
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Comparison of Machine Learning’s- and Humans’- Ability to Consistently Classify Anomalies in Cylinder Locks
- 2022
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Ingår i: IFIP Advances in Information and Communication Technology. - Cham : Springer Science and Business Media Deutschland GmbH. - 9783031164064 ; , s. 27-34, s. 27-34
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Konferensbidrag (refereegranskat)abstract
- Historically, cylinder locks’ quality has been tested manually by human operators after full assembly. The frequency and the characteristics of the testing procedure for these locks wear the operators’ wrists and lead to varying results of the quality control. The consistency in the quality control is an important factor for the expected lifetime of the locks which is why the industry seeks an automated solution. This study evaluates how consistently the operators can classify a collection of locks, using their tactile sense, compared to a more objective approach, using torque measurements and Machine Learning (ML). These locks were deliberately chosen because they are prone to get inconsistent classifications, which means that there is no ground truth of how to classify them. The ML algorithms were therefore evaluated with two different labeling approaches, one based on the results from the operators, using their tactile sense to classify into ‘working’ or ‘faulty’ locks, and a second approach by letting an unsupervised learner create two clusters of the data which were then labeled by an expert using visual inspection of the torque diagrams. The results show that an ML-solution, trained with the second approach, can classify mechanical anomalies, based on torque data, more consistently compared to operators, using their tactile sense. These findings are a crucial milestone for the further development of a fully automated test procedure that has the potential to increase the reliability of the quality control and remove an injury-prone task from the operators.
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| 3. |
- Andersson, Tim, et al.
(författare)
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Sample size prediction for anomaly detection in locks
- 2023
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Ingår i: Procedia CIRP. - : Elsevier B.V.. ; , s. 870-874
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Konferensbidrag (refereegranskat)abstract
- Artificial intelligence in manufacturing systems is currently most used for quality control and predictive maintenance. In the lock industry, quality control of final assembled cylinder lock is still done by hand, wearing out the operators' wrists and introducing subjectivity which negatively affects reliability. Studies have shown that quality control can be automated using machine-learning to analyse torque measurements from the locks. The resulting performance of the approach depends on the dimensionality and size of the training dataset but unfortunately, the process of gathering data can be expensive so the amount collected data should therefore be minimized with respect to an acceptable performance measure. The dimensionality can be reduced with a method called Principal Component Analysis and the training dataset size can be estimated by repeated testing of the algorithms with smaller datasets of different sizes, which then can be used to extrapolate the expected performance for larger datasets. The purpose of this study is to evaluate the state-of-the-art methods to predict and minimize the needed sample size for commonly used machine-learning algorithms to reach an acceptable anomaly detection accuracy using torque measurements from locks. The results show that the learning curve with the best fit to the training data does not always give the best predictions. Instead, performance depends on the amount of data used to create the curve and the particular machine-learning algorithm used. Overall, the exponential and power-law functions gave the most reliable predictions and the use of principal component analysis greatly reduced the learning effort for the machine-learning algorithms. With torque measurements from 50-150 locks, we predicted a detection accuracy of over 95% while the current method of using the human tactile sense gives only 16% accuracy.
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| 4. |
- Bengtsson, Marcus, et al.
(författare)
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Integrating Quality and Maintenance Development : Opportunities and Implications
- 2010
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Ingår i: 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. - 9784883254194 ; , s. 821-828
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 5. |
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| 6. |
- Bengtsson, Marcus, et al.
(författare)
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Technical Design of Condition Based Maintenance Systems - A Case Study Using Sound Analysis and Case-Based Reasoning
- 2004
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 7. |
- Olsson, Erik, et al.
(författare)
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Fault Diagnosis of Industrial Robots using Acoustic Signals and Case-Based Reasoning
- 2004
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Ingår i: Case-Based Reasoning. ECCBR 2004. Lecture Notes in Computer Science, vol 3155. - Berlin, Heidelberg : Springer Berlin Heidelberg. ; , s. 686-701
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 8. |
- Olsson, Ella, et al.
(författare)
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Graph-Based Knowledge Representation and Algorithms for Air and Maintenance Operations
- 2022
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Ingår i: ICAS Proceedings 33rd Congress of the International Council of the Aeronautical Sciences, Stockholm, Sweden. - : International Council of the Aeronautical Sciences. - 9781713871163
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Konferensbidrag (refereegranskat)abstract
- This work presents an approach for information exchange between adjacent air operations domains by means of graph technologies. The approach has the ability to leverage interoperability and collaboration between air- and ground-based systems and stakeholders in respective domains. In its foundation, it provides a means for relevant actors to access and assess relevant data, information and knowledge, and thus provide input in terms of viable action alternatives in a complex and dynamic operational context. As a proof-of-concept, we have utilizeda full-stack application framework to implement a decision support demonstrator for operational aircraft maintenance. Our solution facilitates a lightweight and dynamic representation of relevant domain knowledge,readily available for exploitation by graph algorithms, adapted to our domain. We have based our implementation on the full-stack application framework Grand-Stack, which is an architecture designed to exploit the power of graphs throughout its stack.
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| 9. |
- Olsson, Mats, et al.
(författare)
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The UGT2B17 gene deletion is not associated with prostate cancer risk
- 2008
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Ingår i: The Prostate. - : Wiley-Blackwell. - 0270-4137 .- 1097-0045. ; 68:5, s. 571-575
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Deletion polymorphism of the UDP-glucuronosyltransferase 2B17 (UGT2B17) gene has been associated with an increased prostate cancer risk in two previous independent studies. Here we determine the risk in a large-scale population-based case-control study.METHODS: Genotyping was conducted with a 5'-nuclease activity assay to distinguish those with one or two UGT2B17 gene copies (ins/del and ins/ins) from individuals homozygous for the deletion (del/del) allele.RESULTS: In contrast to previous findings, no association between the UGT2B17 deletion polymorphism and prostate cancer risk was found. Furthermore the UGT2B17 gene deletion did not affect the risk for prostate cancer specific death.CONCLUSION: The UGT2B17 deletion polymorphism does not play a major role in prostate cancer susceptibility as previously indicated.
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