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Publications (10 of 55) Show all publications
Martin del Campo Barraza, S., Schnabel, S., Sandin, F. & Marklund, P. (2019). Detection of particle contaminants in rolling element bearings with unsupervised acoustic emission feature learning. Tribology International, 132, 30-38
Open this publication in new window or tab >>Detection of particle contaminants in rolling element bearings with unsupervised acoustic emission feature learning
2019 (English)In: Tribology International, ISSN 0301-679X, E-ISSN 1879-2464, Vol. 132, p. 30-38Article in journal (Refereed) Published
Abstract [en]

The detection of contaminants in the lubricant of rolling element bearings using acoustic emission signals is a challenging problem, in particular at high rotational speeds. This problem calls for new analysis methods beyond the conventional amplitude- and frequency-based methods. Feature learning is successfully used in the machine learning field to characterize complex signals. Here we use an unsupervised feature learning approach to distinguish acoustic emission signals. We investigate the repetition rates of features identified with shift-invariant dictionary learning and find that the signature of contaminated lubricant is significantly stronger than the effect on conventional condition indicators like the RMS and the enveloped RMS at rotational speeds above 300 rpm and up to 3000 rpm.

Place, publisher, year, edition, pages
Elsevier, 2019
Keywords
Acoustic emission, Contamination, Dictionary learning, Unsupervised feature learning
National Category
Tribology (Interacting Surfaces including Friction, Lubrication and Wear) Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Machine Elements; Industrial Electronics
Identifiers
urn:nbn:se:ltu:diva-63112 (URN)10.1016/j.triboint.2018.12.007 (DOI)000456758700004 ()2-s2.0-85058395512 (Scopus ID)
Note

Validerad;2019;Nivå 2;2019-01-03 (svasva)

Available from: 2017-04-21 Created: 2017-04-21 Last updated: 2019-05-14Bibliographically approved
Dadhich, S., Sandin, F., Bodin, U., Andersson, U. & Martinsson, T. (2019). Field test of neural-network based automatic bucket-filling algorithm for wheel-loaders. Automation in Construction, 97, 1-12
Open this publication in new window or tab >>Field test of neural-network based automatic bucket-filling algorithm for wheel-loaders
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2019 (English)In: Automation in Construction, ISSN 0926-5805, E-ISSN 1872-7891, Vol. 97, p. 1-12Article in journal (Refereed) Published
Abstract [en]

Automation of earth-moving industries (construction, mining and quarry) require automatic bucket-filling algorithms for efficient operation of front-end loaders. Autonomous bucket-filling is an open problem since three decades due to difficulties in developing useful earth models (soil, gravel and rock) for automatic control. Operators make use of vision, sound and vestibular feedback to perform the bucket-filling operation with high productivity and fuel efficiency. In this paper, field experiments with a small time-delayed neural network (TDNN) implemented in the bucket control-loop of a Volvo L180H front-end loader filling medium coarse gravel are presented. The total delay time parameter of the TDNN is found to be an important hyperparameter due to the variable delay present in the hydraulics of the wheel-loader. The TDNN network successfully performs the bucket-filling operation after an initial period (100 examples) of imitation learning from an expert operator. The demonstrated solution show only 26% longer bucket-filling time, an improvement over manual tele-operation performance.

Place, publisher, year, edition, pages
Elsevier, 2019
Keywords
Neural-network, Bucket-filling, Wheel-loader, Automation, Construction
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering Media and Communication Technology
Research subject
Industrial Electronics; Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-71383 (URN)10.1016/j.autcon.2018.10.013 (DOI)000453623600001 ()2-s2.0-85055696994 (Scopus ID)
Note

Validerad;2018;Nivå 2;2018-11-07 (johcin) 

Available from: 2018-10-30 Created: 2018-10-30 Last updated: 2019-01-30Bibliographically approved
Nilsson, J., Sandin, F. & Delsing, J. (2019). Interoperability automation considered as machine learning tasks. In: : . Paper presented at 2nd Productive4.0 Consortium Conference, Budapest, March 12-14, 2019.
Open this publication in new window or tab >>Interoperability automation considered as machine learning tasks
2019 (English)Conference paper, Poster (with or without abstract) (Other academic)
Keywords
Interoperability, machine learning, optimization, translation, semantics
National Category
Other Computer and Information Science
Research subject
Industrial Electronics
Identifiers
urn:nbn:se:ltu:diva-73578 (URN)
Conference
2nd Productive4.0 Consortium Conference, Budapest, March 12-14, 2019
Funder
EU, Horizon 2020, 737459
Available from: 2019-04-11 Created: 2019-04-11 Last updated: 2019-09-06
Martin del Campo Barraza, S., Sandin, F. & Strömbergsson, D. (2018). Dataset concerning the vibration signals from wind turbines in northern Sweden.
Open this publication in new window or tab >>Dataset concerning the vibration signals from wind turbines in northern Sweden
2018 (English)Data set, Primary data
Alternative title[en]
Dataset of A dictionary learning approach to monitoring of wind turbine drivetrain bearings
Abstract [en]

In the manuscript, we investigate condition monitoring methods based on unsupervised dictionary learning.

The dataset includes the raw time-domain vibration signals from six turbines within the same wind farm (near geographical location). All the wind turbines are of the same type and possess a three-stage gearbox. All measurement data corresponds to the axial direction of an accelerometer mounted on the housing of the output shaft bearing of each turbine. The sampling rate is 12.8 kilosamples/second and each signal segment is 1.28 seconds long (16384 samples).

Keywords
dataset, wind turbine, condition monitoring
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering Tribology (Interacting Surfaces including Friction, Lubrication and Wear)
Research subject
Industrial Electronics; Machine Elements
Identifiers
urn:nbn:se:ltu:diva-70730 (URN)
Available from: 2018-09-03 Created: 2018-09-03 Last updated: 2019-05-14Bibliographically approved
Dadhich, S., Bodin, U., Sandin, F. & Andersson, U. (2018). From Tele-remote Operation to Semi-automated Wheel-loader. International Journal of Electrical and Electronic Engineering and Telecommunications, 7(4), 178-182
Open this publication in new window or tab >>From Tele-remote Operation to Semi-automated Wheel-loader
2018 (English)In: International Journal of Electrical and Electronic Engineering and Telecommunications, ISSN 2319-2518, Vol. 7, no 4, p. 178-182Article in journal (Refereed) Published
Abstract [en]

This paper presents experimental results with tele-remote operation of a wheel-loader and proposes a method to semi-automate the process. The different components of the tele-remote setup are described in the paper. We focus on the short loading cycle, which is commonly used at quarry and construction sites for moving gravel from piles onto trucks. We present results from short-loading-cycle experiments with three operators, comparing productivity between tele-remote operation and manual operation. A productivity loss of 42% with tele-remote operation motivates the case for more automation. We propose a method to automate the bucket-filling process, which is one of the key operations performed by a wheel-loader.

Keywords
automation, bucket-filling, construction, quarry, tele-operation, wheel-loader
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering Media and Communication Technology
Research subject
Industrial Electronics; Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-71381 (URN)10.18178/ijeetc.7.4.178-182 (DOI)
Available from: 2018-10-30 Created: 2018-10-30 Last updated: 2018-11-23Bibliographically approved
Dadhich, S., Sandin, F. & Bodin, U. (2018). Predicting bucket-filling control actions of a wheel-loader operator using aneural network ensemble. In: 2018 International Joint Conference on Neural Networks (IJCNN): . Paper presented at 2018 International Joint Conference on Neural Networks (IJCNN);8-13 July 2018;Rio de Janeiro, Brazil. Piscataway, NJ: IEEE, Article ID 8489388.
Open this publication in new window or tab >>Predicting bucket-filling control actions of a wheel-loader operator using aneural network ensemble
2018 (English)In: 2018 International Joint Conference on Neural Networks (IJCNN), Piscataway, NJ: IEEE, 2018, article id 8489388Conference paper, Published paper (Refereed)
Abstract [en]

Automatic bucket filling is an open problem since three decades. In this paper, we address this problem with supervised machine learning using data collected from manual operation. The range-normalized actuations of lift joystick, tilt joystick and throttle pedal are predicted using information from sensors on the machine and the prediction errors are quantified. We apply linear regression, k-nearest neighbors, neural networks, regression trees and ensemble methods and find that an ensemble of neural networks results in the most accurate predictions. The prediction root-mean-square-error (RMSE) of the lift action exceeds that of the tilt and throttle actions, and we obtain an RMSE below 0.2 for complete bucket fillings after training with as little as 135 bucket filling examples

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE, 2018
Series
Proceedings of the International Joint Conference on Neural Networks, E-ISSN 2161-4407
National Category
Media and Communication Technology Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Industrial Electronics; Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-71382 (URN)10.1109/IJCNN.2018.8489388 (DOI)2-s2.0-85055724313 (Scopus ID)978-1-5090-6014-6 (ISBN)
Conference
2018 International Joint Conference on Neural Networks (IJCNN);8-13 July 2018;Rio de Janeiro, Brazil
Available from: 2018-10-30 Created: 2018-10-30 Last updated: 2019-02-11Bibliographically approved
Nilsson, J. & Sandin, F. (2018). Semantic Interoperability in Industry 4.0: Survey of Recent Developments and Outlook. In: 2018 IEEE 16th International Conference on Industrial Informatics (INDIN): . Paper presented at 16th IEEE International Conference on Industrial Informatics, INDIN 2018; Porto; Portugal; 18-20 July 2018. (pp. 127-132). IEEE, Article ID 8471971.
Open this publication in new window or tab >>Semantic Interoperability in Industry 4.0: Survey of Recent Developments and Outlook
2018 (English)In: 2018 IEEE 16th International Conference on Industrial Informatics (INDIN), IEEE, 2018, p. 127-132, article id 8471971Conference paper, Published paper (Refereed)
Abstract [en]

Semantic interoperability is the ability of systems to exchange information with unambiguous meaning. This is an outstanding challenge in the development of Industry 4.0 due to the trend towards dynamic re-configurable production processes with increasingly complex automation systems and a diversity of standards, components, tools and services. The cost of making systems interoperable is a major limiting factor in the adoption of new technology and the envisioned development of production industry. Therefore, methods and concepts enabling efficient interoperation of heterogeneous systems are investigated to understand how the interoperability problem should be addressed. To support this development, we survey the literature on interoperability to identify automation approaches that address semantic interoperability, in particular in dynamic cyber-physical systems at large scale. We find that different aspects of the interoperability problem are investigated, some based on a conventional bottom-up standardization approach, while others consider a goal-driven computational approach; and that the different directions explored are related to open questions that motivates further research. We argue that a goaldriven machine learning approach to semantic interoperability can result in solutions that are applicable across standardization domains and thus is a promising direction of research in this era of the industrial internet of things.

Place, publisher, year, edition, pages
IEEE, 2018
Keywords
Interoperability, Semantics, Ontologies, Industries, Standards, Automation, Tools
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Industrial Electronics
Identifiers
urn:nbn:se:ltu:diva-71466 (URN)10.1109/INDIN.2018.8471971 (DOI)000450180200018 ()2-s2.0-85055539075 (Scopus ID)9781538648292 (ISBN)
Conference
16th IEEE International Conference on Industrial Informatics, INDIN 2018; Porto; Portugal; 18-20 July 2018.
Available from: 2018-11-06 Created: 2018-11-06 Last updated: 2018-12-03Bibliographically approved
Martin del Campo Barraza, S., Sandin, F. & Strömbergsson, D. (2017). A dictionary learning approach to monitoring of wind turbine drivetrain bearings. Mechanical systems and signal processing
Open this publication in new window or tab >>A dictionary learning approach to monitoring of wind turbine drivetrain bearings
2017 (English)In: Mechanical systems and signal processing, ISSN 0888-3270, E-ISSN 1096-1216Article in journal (Other academic) Submitted
Abstract [en]

Condition monitoring and predictive maintenance are central for efficient operation of wind farms due to the challenging operating conditions, rapid technology development and high number of aging wind turbines. In particular, preventive maintenance planning requires early detection of faults with few false positives. This is a challenging problem due to the complex and weak signatures of some faults, in particular of faults occurring in some of the drivetrain bearings. Here, we investigate recently proposed condition monitoring methods based on unsupervised dictionary learning using vibration data recorded from three wind turbines over about four years of operation, thereby contributing novel test results based on real world data. Results of former studies addressing condition--monitoring tasks using dictionary learning indicate that unsupervised feature learning is useful for diagnosis and anomaly detection purposes. However, these studies are based on data from test rigs operating under controlled conditions. Furthermore, most former studies focus on classification tasks using relatively small sets of labeled data, which are useful for quantitative method comparisons but gives little information about how useful these approaches are in practice. In this study dictionaries are learned from gearbox vibrations in three different turbines known to be in healthy conditions, and the dictionaries are subsequently propagated over a few years of monitoring data when faults are known to occur. We calculate the dictionary distance between the initial and propagated dictionaries and find time periods of abnormal dictionary adaptation starting six months before a drivetrain bearing replacement and one year before the resulting gearbox replacement. When repeating that experiment with a dictionary that initially is learned from the vibration of another type of rotating machine, the corresponding difference of dictionary distances is three times lower and do not appear abnormal. We also investigate the distance between dictionaries learned from geographically nearby turbines of the same type in healthy conditions and find that the features learned are similar, and that a dictionary learned from one turbine can be useful for monitoring of another similar turbine.

National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering Tribology (Interacting Surfaces including Friction, Lubrication and Wear)
Research subject
Industrial Electronics; Machine Elements
Identifiers
urn:nbn:se:ltu:diva-63111 (URN)
Available from: 2017-04-21 Created: 2017-04-21 Last updated: 2019-05-14
Sandin, F. & Martin-del-Campo, S. (2017). Dictionary Learning with Equiprobable Matching Pursuit. In: : . Paper presented at 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, 14-19 May 2017 (pp. 557-564). Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), Article ID 7965902.
Open this publication in new window or tab >>Dictionary Learning with Equiprobable Matching Pursuit
2017 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Sparse signal representations based on linear combinations of learned atomshave been used to obtain state-of-the-art results in several practical signalprocessing applications. Approximation methods are needed to processhigh-dimensional signals in this way because the problem to calculate optimalatoms for sparse coding is NP-hard. Here we study greedy algorithms forunsupervised learning of dictionaries of shift-invariant atoms and propose anew method where each atom is selected with the same probability on average,which corresponds to the homeostatic regulation of a recurrent convolutionalneural network. Equiprobable selection can be used with several greedyalgorithms for dictionary learning to ensure that all atoms adapt duringtraining and that no particular atom is more likely to take part in the linearcombination on average. We demonstrate via simulation experiments thatdictionary learning with equiprobable selection results in higher entropy ofthe sparse representation and lower reconstruction and denoising errors, bothin the case of ordinary matching pursuit and orthogonal matching pursuit withshift-invariant dictionaries. Furthermore, we show that the computational costsof the matching pursuits are lower with equiprobable selection, leading tofaster and more accurate dictionary learning algorithms.

Place, publisher, year, edition, pages
Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2017
Series
IEEE International Joint Conference on Neural Networks (IJCNN), ISSN 2161-4393
Keywords
dictionary learning, sparse approximation, matching pursuit, unsupervised learning, homeostatic regulation, neuromorphic engineering
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Industrial Electronics
Identifiers
urn:nbn:se:ltu:diva-60768 (URN)10.1109/IJCNN.2017.7965902 (DOI)0004269687000752161-4393 ()2-s2.0-85030995364 (Scopus ID)978-1-5090-6182-2 (ISBN)
Conference
2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, 14-19 May 2017
Funder
The Kempe Foundations
Available from: 2016-11-29 Created: 2016-11-29 Last updated: 2019-05-14Bibliographically approved
Martin-del-Campo, S. & Sandin, F. (2017). Online feature learning for condition monitoring of rotating machinery. Engineering applications of artificial intelligence, 64, 187-196
Open this publication in new window or tab >>Online feature learning for condition monitoring of rotating machinery
2017 (English)In: Engineering applications of artificial intelligence, ISSN 0952-1976, E-ISSN 1873-6769, Vol. 64, p. 187-196Article in journal (Refereed) Published
Abstract [en]

Condition-based maintenance of rotating machinery requires efficient condition monitoring methods that enable early detection of abnormal operational conditions and faults. This is a challenging problem because machines are different and change characteristics over time due to wear and maintenance. The efficiency and scalability of conventional condition monitoring methods are limited by the need for manual analysis and re-configuration. The problem to extract relevant features from condition monitoring signals and thereby detect and analyze changes in such signals is a central issue, which in principle can be addressed using machine learning methods. Former work demonstrates that dictionary learning can be used to automatically derive signal features that characterize different operational conditions and faults of a rotating machine, but the use of such methods for online condition monitoring purposes is an open problem. Here we investigate online learning of features using dictionary learning. We describe dictionary distance and signal fidelity based heuristics for anomaly detection, and we study the time--propagated features and sparse approximation of vibration and acoustic emission signals in three different case studies. We present results of numerical experiments with different hyperparameters affecting the approximation accuracy, computational cost, and the adaptation rate of the learned features. We find that the learned features change rapidly when a fault appears in the machine or changes characteristics, and that the dictionary is different in normal and faulty conditions. We find that the learned features change slowly under normal variations of the operational conditions in comparison to the rapid adaptation observed when a fault appears (bearing defects, magnetite particles in the lubricant, or plastic deformation of steel). Furthermore, a sparse signal approximation with 2.5\% preserved coefficients based on a propagated dictionary is sufficient for anomaly detection in the cases considered here. Furthermore, we find that a sparse signal approximation with 2.5\% preserved coefficients based on a propagated dictionary is sufficient for bearing defect detection.

Place, publisher, year, edition, pages
Elsevier, 2017
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Industrial Electronics
Identifiers
urn:nbn:se:ltu:diva-63110 (URN)10.1016/j.engappai.2017.06.012 (DOI)000412378800016 ()2-s2.0-85023608740 (Scopus ID)
Note

Validerad; 2017; Nivå 2; 2017-08-14 (andbra)

Available from: 2017-04-21 Created: 2017-04-21 Last updated: 2019-05-14Bibliographically approved
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Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-5662-825x

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