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Publications (7 of 7) Show all publications
Andersson, T., Bohlin, M., Olsson, T. & Ahlskog, M. (2022). Comparison of Machine Learning’s- and Humans’- Ability to Consistently Classify Anomalies in Cylinder Locks. In: Kim, Duck Young; von Cieminski, Gregor; Romero, David (Ed.), APMS 2022: Advances in Production Management Systems. Smart Manufacturing and Logistics Systems: Turning Ideas into Action (Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT,volume 663)): . Paper presented at APMS 2022: Advances in Production Management Systems. Smart Manufacturing and Logistics Systems: Turning Ideas into Action (pp. 27-34). Springer Nature Switzerland
Open this publication in new window or tab >>Comparison of Machine Learning’s- and Humans’- Ability to Consistently Classify Anomalies in Cylinder Locks
2022 (English)In: APMS 2022: Advances in Production Management Systems. Smart Manufacturing and Logistics Systems: Turning Ideas into Action (Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT,volume 663)) / [ed] Kim, Duck Young; von Cieminski, Gregor; Romero, David, Springer Nature Switzerland , 2022, p. 27-34Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
Springer Nature Switzerland, 2022
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:ri:diva-63141 (URN)10.1007/978-3-031-16407-1_4 (DOI)2-s2.0-85140472723 (Scopus ID)
Conference
APMS 2022: Advances in Production Management Systems. Smart Manufacturing and Logistics Systems: Turning Ideas into Action
Available from: 2023-01-25 Created: 2023-01-25 Last updated: 2023-01-25Bibliographically approved
Olsson, T., Ramentol, E., Rahman, M., Oostveen, M. & Kyprianidis, K. (2021). A data-driven approach for predicting long-term degradation of a fleet of micro gas turbines. Energy and AI, 4, Article ID 100064.
Open this publication in new window or tab >>A data-driven approach for predicting long-term degradation of a fleet of micro gas turbines
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2021 (English)In: Energy and AI, ISSN 2666-5468, Vol. 4, article id 100064Article in journal, Editorial material (Refereed) Published
Abstract [en]

Predictive health monitoring of micro gas turbines can significantly increase the availability and reduce the operating and maintenance costs. Methods for predictive health monitoring are typically developed for large-scale gas turbines and have often focused on single systems. In an effort to enable fleet-level health monitoring of micro gas turbines, this work presents a novel data-driven approach for predicting system degradation over time. The approach utilises operational data from real installations and is not dependent on data from a reference system. The problem was solved in two steps by: 1) estimating the degradation from time-dependent variables and 2) forecasting into the future using only running hours. Linear regression technique is employed both for the estimation and forecasting of degradation. The method was evaluated on five different systems and it is shown that the result is consistent (r>0.8) with an existing method that computes corrected values based on data from a reference system, and the forecasting had a similar performance as the estimation model using only running hours as an input.

Keywords
Fleet monitoring, Micro gas turbine, Machine learning, Health monitoring, Predictive maintenance, Power generation
National Category
Energy Engineering Computer Sciences
Identifiers
urn:nbn:se:ri:diva-52673 (URN)10.1016/j.egyai.2021.100064 (DOI)
Projects
FUDIPO
Funder
EU, Horizon 2020
Available from: 2021-04-01 Created: 2021-04-01 Last updated: 2021-06-07Bibliographically approved
Ramentol, E., Olsson, T. & Barua, S. (2021). Machine Learning Models for Industrial Applications. In: AI and Learning Systems: . IntechOpen
Open this publication in new window or tab >>Machine Learning Models for Industrial Applications
2021 (English)In: AI and Learning Systems, IntechOpen , 2021Chapter in book (Other academic)
Abstract [en]

More and more industries are aspiring to achieve a successful production using the known artificial intelligence. Machine learning (ML) stands as a powerful tool for making very accurate predictions, concept classification, intelligent control, maintenance predictions, and even fault and anomaly detection in real time. The use of machine learning models in industry means an increase in efficiency: energy savings, human resources efficiency, increase in product quality, decrease in environmental pollution, and many other advantages. In this chapter, we will present two industrial applications of machine learning. In all cases we achieve interesting results that in practice can be translated as an increase in production efficiency. The solutions described cover areas such as prediction of production quality in an oil and gas refinery and predictive maintenance for micro gas turbines. The results of the experiments carried out show the viability of the solutions.

Place, publisher, year, edition, pages
IntechOpen, 2021
Keywords
Machine learning, Prediction, Regression methods, Maintenance, Degradation prediction, Engineering and Technology, Teknik och teknologier, Computer Systems, Datorsystem
National Category
Computer Systems
Identifiers
urn:nbn:se:ri:diva-56323 (URN)10.5772/intechopen.93043 (DOI)978-1-78985-878-5 (ISBN)
Available from: 2021-09-03 Created: 2021-09-03 Last updated: 2021-09-03Bibliographically approved
Källström, E., Olsson, T., Lindström, J., Håkansson, L. & Larsson, J. (2018). On-board clutch slippage detection and diagnosis in heavy duty machine. International Journal of Prognostics and Health Management, 9(1), Article ID 007.
Open this publication in new window or tab >>On-board clutch slippage detection and diagnosis in heavy duty machine
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2018 (English)In: International Journal of Prognostics and Health Management, E-ISSN 2153-2648, Vol. 9, no 1, article id 007Article in journal (Refereed) Published
Abstract [en]

In order to reduce unnecessary stops and expensive downtime originating from clutch failure of construction equipment machines; adequate real time sensor data measured on the machine in combination with feature extraction and classification methods may be utilized. This paper presents a framework with feature extraction methods and an anomaly detection module combined with Case-Based Reasoning (CBR) for on-board clutch slippage detection and diagnosis in heavy duty equipment. The feature extraction methods used are Moving Average Square Value Filtering (MASVF) and a measure of the fourth order statistical properties of the signals implemented as continuous queries over data streams. The anomaly detection module has two components, the Gaussian Mixture Model (GMM) and the Logistics Regression classifier. CBR is a learning approach that classifies faults by creating a new solution for a new fault case from the solution of the previous fault cases. Through use of a data stream management system and continuous queries (CQs), the anomaly detection module continuously waits for a clutch slippage event detected by the feature extraction methods, the query returns a set of features, which activates the anomaly detection module. The first component of the anomaly detection module trains a GMM to extracted features while the second component uses a Logistic Regression classifier for classifying normal and anomalous data. When an anomaly is detected, the Case-Based diagnosis module is activated for fault severity estimation.

Keywords
Case-based reasoning, Fourth order statistics, Gaussian mixture model, Linear regression and moving average square value filtering
National Category
Natural Sciences
Identifiers
urn:nbn:se:ri:diva-34480 (URN)2-s2.0-85044281699 (Scopus ID)
Note

Funding text: This work has been partially supported by the FP7 EU Large Scale Integrating Project SMART VORTEX (Scalable Semantic Product Data Stream Management for Collaboration and Decision Making in Engineering) co-financed by the European Union. For more details, visit http://www.smartvortex.eu/. Further, the Swedish Knowledge Foundation (KK-stiftelsen) through ITS-EASY Research School and Swedish Governmental Agency for Innovation Systems (VINNOVA) grant no 10020 and JU grant no 100266 making this research possible.

Available from: 2018-08-08 Created: 2018-08-08 Last updated: 2023-07-24Bibliographically approved
Aslanidou, I., Zaccaria, V., Rahman, M., Kyprianidis, K. G., Oostveen, M. & Olsson, T. (2018). Towards an Integrated Approach for Micro Gas Turbine Fleet Monitoring, Control and Diagnostics. In: : . Paper presented at Global Power and Propulsion Society (GPPS), Zurich (10th-12th January 2018).
Open this publication in new window or tab >>Towards an Integrated Approach for Micro Gas Turbine Fleet Monitoring, Control and Diagnostics
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2018 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Real-time engine condition monitoring and fault diagnostics results in reduced operating and maintenance costs and increased component and engine life. Prediction of faults can change the maintenance model of a system from a fixed maintenance interval to a condition based maintenance interval, further decreasing the total cost of ownership of a system. Technologies developed for engine health monitoring and advanced diagnostic capabilities are generally developed for larger gas turbines, and generally focus on a single system; no solutions are publicly available for engine fleets. This paper presents a concept for fleet monitoring finely tuned to the specific needs of micro gas turbines. The proposed framework includes a physics-based model and a data-driven model with machine learning capabilities for predicting system behaviour, combined with a diagnostic tool for anomaly detection and classification. The integrated system will develop advanced diagnostics and condition monitoring for gas turbines with a power output under 100 kW.

National Category
Engineering and Technology
Identifiers
urn:nbn:se:ri:diva-37596 (URN)
Conference
Global Power and Propulsion Society (GPPS), Zurich (10th-12th January 2018)
Available from: 2019-01-28 Created: 2019-01-28 Last updated: 2020-01-29Bibliographically approved
Aslanidou, I., Zaccaria, V., Rahman, M., Oostveen, M., Olsson, T. & Kyprianidis, K. (2018). Towards an Integrated Approach for Micro Gas Turbine Fleet Monitoring, Control and Diagnostics. In: Proceedings of the Global Power and Propulsion Society (GPPS) Forum 2018: . Paper presented at Global Power and Propulsion Society (GPPS) Forum 2018. Zurich, Switzerland
Open this publication in new window or tab >>Towards an Integrated Approach for Micro Gas Turbine Fleet Monitoring, Control and Diagnostics
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2018 (English)In: Proceedings of the Global Power and Propulsion Society (GPPS) Forum 2018, Zurich, Switzerland, 2018Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
Zurich, Switzerland: , 2018
National Category
Energy Engineering Computer Sciences
Identifiers
urn:nbn:se:ri:diva-52738 (URN)
Conference
Global Power and Propulsion Society (GPPS) Forum 2018
Funder
EU, Horizon 2020
Note

GPPS-2018-0021

Available from: 2021-04-01 Created: 2021-04-01 Last updated: 2021-04-06Bibliographically approved
Olsson, T. & Holst, A. (2015). A Probabilistic Approach to Aggregating Anomalies for Unsupervised Anomaly Detection with Industrial Applications (7ed.). In: Proceedings of the 28th International Florida Artificial Intelligence Research Society Conference (FLAIRS 2015): . Paper presented at 28th International Florida Artificial Intelligence Research Society Conference (FLAIRS 2015), May 18-20, 2015, Hollywood, US (pp. 434-439).
Open this publication in new window or tab >>A Probabilistic Approach to Aggregating Anomalies for Unsupervised Anomaly Detection with Industrial Applications
2015 (English)In: Proceedings of the 28th International Florida Artificial Intelligence Research Society Conference (FLAIRS 2015), 2015, 7, p. 434-439Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents a novel, unsupervised approach to detecting anomalies at the collective level. The method probabilistically aggregates the contribution of the individual anomalies in order to detect significantly anomalous groups of cases. The approach is unsupervised in that as only input, it uses a list of cases ranked according to its individual anomaly score. Thus, any anomaly detection algorithm can be used for scoring individual anomalies, both supervised and unsupervised approaches. The applicability of the proposed approach is shown by applying it to an artificial data set and to two industrial data sets — detecting anomalously moving cranes (model-based detection) and anomalous fuel consumption (neighbour-based detection).

National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:ri:diva-24430 (URN)2-s2.0-84958181138 (Scopus ID)9781577357308 (ISBN)
Conference
28th International Florida Artificial Intelligence Research Society Conference (FLAIRS 2015), May 18-20, 2015, Hollywood, US
Projects
STREAM
Available from: 2016-10-31 Created: 2016-10-31 Last updated: 2023-05-09Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-9890-4918

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