Wind turbine bearing condition monitoring based on high frequency resonance method

Zhang, Xueyan; Yang, Shenggang; Li, Xiaoli

doi:10.1109/icecc.2011.6066289

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…In the conventional AEA technique, bearing damage detection is achieved by means of filtering, and traditionally, the determination of the filter parameters is based on experience or by trial‐and‐error. For example, according to Zhang et al, 42 a band‐filter with a passband of 9–11 kHz was used, and the analysis results are shown in Figure 6.…”

Section: Validation With Wind Turbine Operation Datamentioning

confidence: 99%

Fault diagnosis of low‐speed rolling bearing based on weighted reconstruction of vibration envelopes

et al. 2023

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The main bearing supports the rotation of the main shaft of a wind turbine. It bears heavy dead weights as well as variable speed dynamic loading during operations; thus, it is a vulnerable part in a wind turbine drive train. Because of the low speed and time-varying operations of the main bearing, vibrations generated by bearing faults are often weak in response amplitudes, low in frequency range, and smeared in damage feature energy. As a result, the applicability of the conventional acceleration envelope analysis (AEA) technique, a traditionally effective technology for bearing fault diagnosis, is limited in such cases. In order to resolve this, a modified AEA method specially designed for bearings with low and variable speed operation is proposed in this paper. First, the structural response is decomposed by means of variational mode decomposition (VMD) for the low frequency components to form a series of band-limited intrinsic mode functions (BLIMFs). Next, weighting factors are determined for the BLIMFs by defined energy ratios. Finally, a new envelope is reconstructed by weighting the envelopes of each BLIMF for bearing fault diagnosis.The effectiveness and practicality of the proposed method for the diagnosis of main bearing faults in wind turbines is verified through the analysis of measured data from a wind turbine in the field. The proposed method provides an effective way for bearing fault diagnosis at low and variable rotational speeds.

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Section: Validation With Wind Turbine Operation Datamentioning

confidence: 99%

Fault diagnosis of low‐speed rolling bearing based on weighted reconstruction of vibration envelopes

et al. 2023

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“…It has been always a challenge to extract early bearing fault signature due to presence of multiple components like outer race, inner race, rollers and cage of roller and also the vibration signals coming from shaft, gearbox also increases the background noise. Reference [11] has used High Frequency Resonance (HFR) technique for fault diagnosis in wind turbine bearing. Reference [12] utilized Shock Pulse Method (SPM) to detect the faults in wind turbine bearing .Shock pulse method uses the real time frequency spectrum and vibration data to locate the defective bearing.…”

Section: Introductionmentioning

confidence: 99%

Design and development of a wind turbine test rig for condition monitoring studies

Biswal

Sabareesh

2015

2015 International Conference on Industrial Instrumentation and Control (ICIC)

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Wind energy is an emerging, clean and renewable source of energy. It is estimated that by year 2035, wind energy will be generating more than 25% of the world's electricity according to International Energy Agency (IEA). With the increase in demand for wind energy, its maintenance issues are becoming more prominent .The scheduled maintenance is more economical than unscheduled repair resulting from failure. So a continuous condition monitoring of various critical components like bearings, gearbox, and shafts of wind turbine is essential in order to enable predictive maintenance. 10% of the total failure is contributed by the bearings, shaft and gear box failures, but the downtime is more than 50% of the total downtime. This paper discusses the development of a bench-top test rig which is designed to mimic the operating condition of an actual wind turbine and use it for monitoring its condition so as to diagnose the incipient faults in its critical components using latest machine learning algorithms such as Artificial Neural Network (ANN).

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Wind turbine bearing condition monitoring based on high frequency resonance method

Cited by 2 publications

References 6 publications

Fault diagnosis of low‐speed rolling bearing based on weighted reconstruction of vibration envelopes

Fault diagnosis of low‐speed rolling bearing based on weighted reconstruction of vibration envelopes

Design and development of a wind turbine test rig for condition monitoring studies

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