Application of the multi‐scale enveloping spectrogram to detect weak faults in a wind turbine gearbox

Abstract

The gearbox of a wind turbine involves multiple rotating components, each having a potential to be affected by a fault. Detecting weak faults of these components with traditional demodulation analysis is challenging. Multi-scale enveloping spectrogram (MuSEnS) decomposes a vibration signal into different frequency bands while simultaneously generating the corresponding envelope spectra. In this study, a MuSEnS-based diagnosis approach is applied to detect faults affecting the intermediate stage of a gearbox installed in an operating wind turbine. The MuSEnSs of 12 vibration channels have allowed to identify multiple fault features, including the weak fault of the big gear on the sun shaft. The effectiveness of the proposed fault diagnosis approach has been tested with industrial data and the faults themself have been confirmed with the disassembled gears.

Improved criticality analysis method of equipment failures in wind turbines

“…Suppose a permutation consisting of n elements, {a [1], a [2], …, a[n]} and assign {1, 2, …, n} as a standard permutation. If i < j and…”

“…The amount of inversed orders is called as the inverse number of the permutation [25]. For example, if assign {1, 2, 3} as the standard permutation, the inverse number of the permutation {1, 3, 2} is one, because it contains one inversed order (3,2). Similarly, the inverse number of the permutation {3, 2, 1} is three, because it contains three inversed order (3,2), (3,1) and (2,1).…”

“…For the above example, the disparity between the permutations {1, 2, 3} and {1, 3, 2} is one, the disparity between the permutations {1, 2, 3} and {3, 2, 1} is three. To determine the disparity between the permutations {1, 3, 2} and {3, 2, 1}, set {1, 3, 2} as the standard permutation, then the permutation {3, 2, 1} has the inversed order (3,1) and (2,1), which represents their disparity is two.…”

“…Wind turbines are the major equipment capturing wind energy, which operate in harsh environment and are installed in high altitude and remote area. Compared with the power equipment on the ground, wind turbines have higher failure rate and maintenance cost [2,3]. Literature [4] stated that maintenance cost occupied 10% of power generation cost for onshore wind turbines and 35% for offshore units.…”

DFIG stator flux‐oriented control scheme execution for test facilities utilising commercial converters

“…To avoid complicated and potentially damaging fullscale tests on sensitive and costly commercial WT units, the practical aspects of WT energy conversion research are typically underpinned by utilisation of laboratory facilities capable of emulating WT electric drive behaviour. The development of realscale laboratory facilities for this purpose [4] can, however, pose prohibitive limitations in the academic research community where there is a need for lower cost yet representative test facilities to enable proof of concept research of various aspects of WT operations: these can range from model and control method verification to examination of power quality, fault effects and grid support capability [2][3][4][5][6][7][8][9][10][11].…”