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Strömbergsson, DanielORCID iD iconorcid.org/0000-0002-7970-8655
Publications (5 of 5) Show all publications
Saari, J., Strömbergsson, D., Lundberg, J. & Thomson, A. (2019). Detection and identification of windmill bearing faults using a one-class support vector machine (SVM). Measurement, 137, 287-301
Open this publication in new window or tab >>Detection and identification of windmill bearing faults using a one-class support vector machine (SVM)
2019 (English)In: Measurement, ISSN 0263-2241, E-ISSN 1873-412X, Vol. 137, p. 287-301Article in journal (Refereed) Published
Abstract [en]

The maintenance cost of wind turbines needs to be minimized in order to keep their competitiveness and, therefore, effective maintenance strategies are important. The remote location of wind farms has led to an opportunistic maintenance strategy where maintenance actions are postponed until they can be handled simultaneously, once the optimal opportunity has arrived. For this reason, early fault detection and identification are important, but should not lead to a situation where false alarms occur on a regular basis. The goal of the study presented in this paper was to detect and identify wind turbine bearing faults by using fault-specific features extracted from vibration signals. Automatic identification was achieved by training models by using these features as an input for a one-class support vector machine. Detection models with different sensitivity were trained in parallel by changing the model tuning parameters. Efforts were also made to find a procedure for selecting the model tuning parameters by first defining the criticality of the system and using it when estimating how accurate the detection model should be. Method was able to detect the fault earlier than using traditional methods without any false alarms. Optimal combination of features and model tuning parameters was not achieved, which could identify the fault location without using any additional techniques.

Place, publisher, year, edition, pages
Elsevier, 2019
Keywords
Novelty detection, Wind turbine, Bearing fault diagnostics
National Category
Other Civil Engineering Tribology (Interacting Surfaces including Friction, Lubrication and Wear)
Research subject
Operation and Maintenance; Machine Elements
Identifiers
urn:nbn:se:ltu:diva-72839 (URN)10.1016/j.measurement.2019.01.020 (DOI)000464553200027 ()2-s2.0-85060852953 (Scopus ID)
Note

Validerad;2019;Nivå 2;2019-02-11 (svasva)

Available from: 2019-02-11 Created: 2019-02-11 Last updated: 2019-04-29Bibliographically approved
Strömbergsson, D., Marklund, P., Berglund, K., Saari, J. & Thomson, A. (2019). Mother wavelet selection in the discrete wavelet transform for condition monitoring of wind turbine drivetrain bearings. Wind Energy, 22(11), 1581-1592
Open this publication in new window or tab >>Mother wavelet selection in the discrete wavelet transform for condition monitoring of wind turbine drivetrain bearings
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2019 (English)In: Wind Energy, ISSN 1095-4244, E-ISSN 1099-1824, Vol. 22, no 11, p. 1581-1592Article in journal (Refereed) Published
Abstract [en]

Although the discrete wavelet transform has been used for diagnosing bearing faults for two decades, most work in this field has been done with test rig data. Since field data starts to be made more available, there is a need to shift into application studies.

The choice of mother wavelet, ie, the predefined shape used to analyse the signal, has previously been investigated with simulated and test rig data without consensus of optimal choice in literature. Common between these investigations is the use of the wavelet coefficients' Shannon entropy to find which mother wavelet can yield the most useful features for condition monitoring.

This study attempts to find the optimal mother wavelet selection using the discrete wavelet transform. Datasets from wind turbine gearbox accelerometers, consisting of enveloped vibration measurements monitoring both healthy and faulty bearings, have been analysed. The bearing fault frequencies' excitation level has been analysed with 130 different mother wavelets, yielding a definitive measure on their performance. Also, the applicability of Shannon entropy as a ranking method of mother wavelets has been investigated.

The results show the discrete wavelet transforms ability to identify faults regardless of mother wavelet used, with the excitation level varying no more than 4%. By analysing the Shannon entropy, broad predictions to the excitation level could be drawn within the mother wavelet families but no direct correlation to the main results. Also, the high computational effort of high order Symlet wavelets, without increased performance, makes them unsuitable.

Place, publisher, year, edition, pages
John Wiley & Sons, 2019
Keywords
bearing failure, condition monitoring, discrete wavelet transform, mother wavelet selection, wind turbine field measurements
National Category
Other Civil Engineering Tribology (Interacting Surfaces including Friction, Lubrication and Wear)
Research subject
Machine Elements; Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-75777 (URN)10.1002/we.2390 (DOI)000480192400001 ()
Note

Validerad;2019;Nivå 2;2019-12-06 (johcin)

Available from: 2019-08-30 Created: 2019-08-30 Last updated: 2019-12-06Bibliographically approved
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
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
Strömbergsson, D., Marklund, P., Edin, E. & Zeman, F. (2017). Acoustic emission monitoring of a mechanochemical surface finishing process. Tribology International, 112, 129-136
Open this publication in new window or tab >>Acoustic emission monitoring of a mechanochemical surface finishing process
2017 (English)In: Tribology International, ISSN 0301-679X, E-ISSN 1879-2464, Vol. 112, p. 129-136Article in journal (Refereed) Published
Abstract [en]

Acoustic emission monitoring of cutting machining operations is an established researched area, though monitoring non-removal finishing processes is less studied.

This work presents an initial investigation on Acoustic emissions potential of an mechanochemical superfinishing process. Conclusions are drawn from the monitoring signal regarding the resulting surface friction characteristics, composition and possible runnability issues.

Monitoring data was collected from tests performed at Applied Nano Surfaces' testing laboratory. Test series with varying parameters enabled a correlation analysis between the monitoring data, surface friction characteristics and tribofilm formation. Increasing tool wear tests were monitored to find early runnability warning.

Results shows Acoustic emissions indication potential when the finishing process has achieved the intended friction reduction, tribofilm deposition as well as runnability issues identification.

Place, publisher, year, edition, pages
Elsevier, 2017
National Category
Tribology (Interacting Surfaces including Friction, Lubrication and Wear)
Research subject
Machine Elements
Identifiers
urn:nbn:se:ltu:diva-62937 (URN)10.1016/j.triboint.2017.03.031 (DOI)000401217100015 ()2-s2.0-85017199001 (Scopus ID)
Note

Validerad; 2017; Nivå 2; 2017-04-18 (rokbeg)

Available from: 2017-04-07 Created: 2017-04-07 Last updated: 2018-09-13Bibliographically approved
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ORCID iD: ORCID iD iconorcid.org/0000-0002-7970-8655

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