Abstract
The following chapter presents six signal analysis methods, which were found useful for wind turbine fault detection and require more advanced signal processing than the standard methods described in the Chap. 2. The selection of the methods was based on the author’s research history which was closely linked to a problem solving in the renewable power generation business. The first method uses only the data collected by the CMS with no access to the raw data. This is standard data collected on many machines and is typically stored as 10 min averages. Due to highly nonstationary operation, simple thresholding is not sufficient for early fault detection and thus, the method based on statistical parameters is proposed. The second method is the Spectral Kurtosis which is helpful for early detection of bearing and gear faults. The third one is Protrugram whose development was inspired by the observation that spectral kurtosis is vulnerable to random impacts in the signal. If this happens, series of impulses caused by a REB fault may go unnoticed. Next, a set of cyclostationary tools is described. The Modulation Intensity Distribution method belongs to this family and is useful when bearing related modulations are weak or masked by the other faults. The last method is capable of presenting details of dynamic operation of complex gearboxes. A 2-D map is produced and it can help to detect and distinguish faults of a ring, planets and a sun gear. Every method is accompanied by a case study in which its performance is presented.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zimroz R, Bartelmus W, Barszcz T, Urbanek J (2014) Diagnostics of bearings in presence of strong operating conditions non-stationarity—A procedure of load-dependent features processing with application to wind turbine bearings. Mech Syst Sig Process 46:16–27
Bartelmus W, Zimroz R (2009) A new feature for monitoring the condition of gearboxes in non-stationary operation conditions. Mech Syst Sig Process 23(5):1528–1534
Antoni J (2006) The spectral kurtosis: A useful tool for characterising non-stationary signals. Mech Syst Sig Process 20:282–307
Antoni J, Randall RB (2006) The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines. Mech Syst Sig Process 20:308–331
Antoni J (2009) Cyclostationarity by examples. Mech Syst Sig Process 23(4):987–1036
Antoni J (2007) Fast computation of the kurtogram for the detection of transient faults. Mech Syst Sig Process 21:108–124
Barszcz T, Randall RB (2009) Application of spectral kurtosis for detection of a tooth crack in the planetary gear of a wind turbine. Mech Syst Sig Process 23:1352–1365
Courrech J, Gaudel M (1998) Envelope analysis—the key to rolling-element bearing diagnosis. Bruel & Kjaer Application Notes
Ho D, Randall RB (2000) Optimization of bearing diagnostics techniques using simulated and actual bearing fault signals. Mech Syst Sig Process 14(5):763–788
Barszcz T, Jablonski A (2009) Analysis of Kurtogram performance in case of high level non-Gaussian noise. In: The Proceedings of the 16th International Congress on Sound and Vibration, Krakow, Poland, 5–9 July 2009
Barszcz T, Jablonski A (2011) A novel method for the optimal band selection for vibration signal demodulation and comparison with the Kurtogram. Mech Syst Sig Process 25(1):431–451
Gardner W (1994) Cyclostationarity in Communications and Signal Processing. IEEE Press, New York
Gardner W (1990) Introduction to random processes. McGraw-Hill, New York
Serpedin E, Panduru F, Sari I, Giannakis GB (2005) Bibliography on cyclostationarity. Signal Process 85(12):2233–2303
Urbanek J, Barszcz T, Uhl T (2012) Comparison of advanced signal-processing methods for roller bearing faults detection. Metrol Meas Syst 19(4):715–726
Urbanek J, Antoni J, Barszcz T (2012) Detection of signal component modulations using modulation intensity distribution. Mech Syst Sig Process 28:399–413
Villa LF, Renones A, Peran JR, de Miguel LJ (2011) Angular resampling for vibration analysis in wind turbines under non-linear speed fluctuation. Mech Syst Sig Process 25(6):2157–2168
Jablonski A, Barszcz T (2012) Instantaneous circular pitch cyclic power (ICPCP)—a tool for diagnosis of planetary gearboxes. KEM 518:168–173
Belsak A, Flasker J (2006) Method for detecting fatigue crack in gears. Theor Appl Fract Mec 46(2):105–113
Forrester BD (2001) Method for the separation of epicyclic planet gear vibration signatures, U.S. Patent 6,298,725
Maczak J (2009) Local meshing plane as a source of diagnostic information for monitoring the evolution of gear faults. In: Proceedings of the 4th world congress on engineering asset management, Athens, Greece, 28–30 Sept ’
William A. Gardner, Antonio Napolitano, Luigi Paura, (2006) Cyclostationarity: Half a century of research. Signal Processing 86 (4):639–697
Nandi A (ed) (1999) Blind estimation using higher-order statistics, Springer
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Barszcz, T. (2019). Advanced Analysis Methods. In: Vibration-Based Condition Monitoring of Wind Turbines. Applied Condition Monitoring, vol 14. Springer, Cham. https://doi.org/10.1007/978-3-030-05971-2_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-05971-2_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-05969-9
Online ISBN: 978-3-030-05971-2
eBook Packages: EngineeringEngineering (R0)