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Improving detection and diagnosis of bearing failures in wind turbine drivetrains
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Machine Elements.ORCID iD: 0000-0002-7970-8655
2020 (English)Doctoral thesis, comprehensive summary (Other academic)Alternative title
Förbättrad detektion och diagnostik av lagerskador i vindkraftverk (Swedish)
Abstract [en]

Wind power has in the last 20 years grown into one of the main sources of renewable energy in the world, with both the amount and size of the turbines increasing substantially. One of the major challenges for the wind power industry is the premature failures of especially the drivetrain components. These failures cause a lot of turbine downtime, which increases the operation and maintenance costs of the turbines. Failures in the gearbox have been shown to lead to the highest downtime and the multitude of bearings within that subsystem is overrepresented in the total amount of component failures. Vibrationbased condition monitoring is considered the best method to find these types of defects early and avoid prolonged turbine downtime. Previous research has therefore been focused on the different aspects of condition monitoring; i.e. measurement technologies, signal analysis of vibration measurements to improve detection and diagnosis as well as the implementation of machine learning solutions. However, the majority of research work has yet to evaluate the performance of new developments using wind turbine field data, and still no fundamentally new developments have seen a large-scale implementation in the industry. Further, it is known that the positioning of the accelerometer, used to measure the vibrations, affects the ability to detect and diagnose defects. However, it is not known how to optimally position the accelerometers to monitor the individual drivetrain sub-systems. Also, previous research does not show how the influence of the measurement properties of the field data affect the ability to detect and diagnose component failures.

Therefore, this thesis provides a comprehensive evaluation of how to improve the detection and diagnosis of bearing failures specifically in wind turbine drivetrains. In this thesis, a simulation model was developed to study how the accelerometer positioning affects the measurement quality. Vibration simulations of a similar sized bearing to ones found in the wind turbine drivetrain show an optimal accelerometer position as close to the primary loaded zone of the bearings as possible. The current placement of the accelerometers in the wind turbine drivetrain are often diametrically opposed to the loaded zone, and the performance of the vibration monitoring with respect to the commonly used signal analysis tools could thereby be increased. Further, wavelet-based signal analysis has been evaluated using historical wind turbine drivetrain field data. A new implementation of the wavelet packet transform to analyse enveloped vibration measurements in the frequency domain was developed, where the measurements were decomposed into packets matching the frequency resolution of the fast Fourier transform, and analysing the packet energy spectra. Finally, an anomaly detection solution utilizing an artificial neural network has been implemented to separate the condition indicator values, used for detection and diagnosis, from their inherent variance due to the dynamic turbine operation seen in the drivetrain rotational speed.

The results in this thesis show the inadequacy of the commonly stored vibration measurements to the condition monitoring databases when used in post failure investigations and application of research developments on available field data. Measurements both taken over a long period of time and covering wide frequency range should be stored, instead of the either/or of today. Otherwise, the real-time monitoring of wind turbine drivetrain bearing failures cannot be replicated and monitoring improvements not fully evaluated. By implementing the wavelet packet transform, the detection and diagnosis performance was increased 250% compared to the fast Fourier transform, in an example of gearbox output shaft bearing failure. By implementing the anomaly detection by the artificial neural network, the performance increased further and was able to provide indications in a planet bearing failure case, which was not possible before. For turbine owners, these results provide both practical actions to take and provide an example of an easily implementable signal analysis tool to improve the detection and diagnosis of drivetrain bearing failures. The anomaly detection, which utilizes available historic data from healthy turbines and does not require any amount of labelled data for all considered types of bearing failures, also shows promise to detect failures in the drivetrain components which has been historically problematic. For the research community, the results mainly provides guidance into using historic field data when evaluating new developments. Also, they highlight potential pitfalls one can face using field data and what data properties to look for to successfully show the potential of your work.

Place, publisher, year, edition, pages
Luleå: Luleå University of Technology, 2020.
Series
Doctoral thesis / Luleå University of Technology 1 jan 1997 → …, ISSN 1402-1544
National Category
Tribology (Interacting Surfaces including Friction, Lubrication and Wear)
Research subject
Machine Elements
Identifiers
URN: urn:nbn:se:ltu:diva-79491ISBN: 978-91-7790-621-6 (print)ISBN: 978-91-7790-622-3 (electronic)OAI: oai:DiVA.org:ltu-79491DiVA, id: diva2:1439962
Public defence
2020-10-16, A109, Luleå, 09:00 (English)
Opponent
Supervisors
Available from: 2020-06-15 Created: 2020-06-12 Last updated: 2020-09-28Bibliographically approved
List of papers
1. Multi-body simulation and validation of fault vibrations from rolling-element bearings
Open this publication in new window or tab >>Multi-body simulation and validation of fault vibrations from rolling-element bearings
2021 (English)In: Proceedings of the Institution of mechanical engineers. Part J, journal of engineering tribology, ISSN 1350-6501, E-ISSN 2041-305X, Vol. 235, no 9, p. 1834-1841Article in journal (Refereed) Published
Abstract [en]

Dynamic simulations are often used to evaluate the vibrational response of rolling-element bearings experiencing defects. Previously, the optimal accelerometer position has been found to be as close as possible to the bearing. However, further details about the influence of rotational symmetry have not been closely investigated. This paper presents a dynamic simulation model of a radially loaded complete spherical roller bearing with a defect in the inner ring and placed in a housing with 72 equally spaced accelerometer positions around the circumference. Surface accelerations have been extracted and transformed into the frequency domain. Thereafter, the vibrational components indicating the defect have been evaluated around the circumference. The results show an optimal position as close as possible to the primary loaded zone and validation test rig experiments show a reasonable qualitative agreement.

Place, publisher, year, edition, pages
Sage Publications, 2021
Keywords
Vibration modelling, bearing failure, dynamic simulations, accelerometer position, rolling element bearings
National Category
Tribology (Interacting Surfaces including Friction, Lubrication and Wear)
Research subject
Machine Elements
Identifiers
urn:nbn:se:ltu:diva-79490 (URN)10.1177/1350650120977974 (DOI)000683903600006 ()2-s2.0-85112128082 (Scopus ID)
Note

Validerad;2021;Nivå 2;2021-08-11 (alebob);

Artikeln har tidigare förekommit som manuskript i avhandling

Available from: 2020-06-12 Created: 2020-06-12 Last updated: 2021-08-26Bibliographically approved
2. Mother wavelet selection in the discrete wavelet transform for condition monitoring of wind turbine drivetrain bearings
Open this publication in new window or tab >>Mother wavelet selection in the discrete wavelet transform for condition monitoring of wind turbine drivetrain bearings
Show others...
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 ()2-s2.0-85070690626 (Scopus ID)
Note

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

Available from: 2019-08-30 Created: 2019-08-30 Last updated: 2020-08-26Bibliographically approved
3. Bearing monitoring in the wind turbine drivetrain: A comparative study of the FFT and wavelet transforms
Open this publication in new window or tab >>Bearing monitoring in the wind turbine drivetrain: A comparative study of the FFT and wavelet transforms
2020 (English)In: Wind Energy, ISSN 1095-4244, E-ISSN 1099-1824, Vol. 23, no 6, p. 1381-1393Article in journal (Refereed) Published
Abstract [en]

Wind turbines are often plagued by premature component failures, with drivetrain bearings being particularly subjected to these failures. To identify failing components, vibration condition monitoring has emerged and grown substantially. The fast Fourier transform (FFT) is the major signal processing method of vibrations. Recently, the wavelet transforms have been used more frequently in bearing vibration research, with one alternative being the discrete wavelet transform (DWT). Here, the low‐frequency component of the signal is repeatedly decomposed into approximative and detailed coefficients using a predefined mother wavelet. An extension to this is the wavelet packet transform (WPT), which decomposes the entire frequency domain and stores the wavelet coefficients in packets. How wavelet transforms and FFT compare regarding fault detection in wind turbine drivetrain bearings has been largely overlooked in literature when applied on field data, with non‐ideal placement of sensors and uncertain parameters influencing the measurements. This study consists of a comprehensive comparison of the FFT, a three‐level DWT, and the WPT when applied on enveloped vibration measurements from two 2.5‐MW wind turbine gearbox bearing failures. The frequency content is compared by calculating a robust condition indicator by summation of the harmonics and shaft speed sidebands of the bearing fault frequencies. Results show a higher performance of the WPT when used as a field vibration measurement analysis tool compared with the FFT as it detects one bearing failure earlier and more clearly, leading to a more stable alarm setting and avoidable, costly false alarms.

Place, publisher, year, edition, pages
John Wiley & Sons, 2020
Keywords
bearing failure, condition monitoring, discrete wavelet transform, wavelet packet transform, wind turbine gearbox bearings
National Category
Tribology (Interacting Surfaces including Friction, Lubrication and Wear)
Research subject
Machine Elements
Identifiers
urn:nbn:se:ltu:diva-77861 (URN)10.1002/we.2491 (DOI)000513910600001 ()2-s2.0-85079731480 (Scopus ID)
Note

Validerad;2020;Nivå 2;2020-06-03 (alebob)

Available from: 2020-02-27 Created: 2020-02-27 Last updated: 2020-06-12Bibliographically approved
4. Wind turbine drivetrain condition monitoring by vibration analysis Part A:: Property requirements of vibration measurements
Open this publication in new window or tab >>Wind turbine drivetrain condition monitoring by vibration analysis Part A:: Property requirements of vibration measurements
(English)Manuscript (preprint) (Other academic)
Keywords
Vibration measurement properties, Wind turbine drivetrain bearings, Bearing failure, Wavelet packet transform
National Category
Tribology (Interacting Surfaces including Friction, Lubrication and Wear)
Research subject
Machine Elements
Identifiers
urn:nbn:se:ltu:diva-79484 (URN)
Available from: 2020-06-12 Created: 2020-06-12 Last updated: 2020-06-12
5. Wind turbine drivetrain condition monitoring by vibration analysis Part B:: Anomaly detection using artificial neural networks
Open this publication in new window or tab >>Wind turbine drivetrain condition monitoring by vibration analysis Part B:: Anomaly detection using artificial neural networks
(English)Manuscript (preprint) (Other academic)
Keywords
Vibration measurements, Bearing failure, Wind turbine drivetrain bearings, Artificial neural network
National Category
Tribology (Interacting Surfaces including Friction, Lubrication and Wear)
Research subject
Machine Elements
Identifiers
urn:nbn:se:ltu:diva-79489 (URN)
Available from: 2020-06-12 Created: 2020-06-12 Last updated: 2020-06-12

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