Fault feature extraction of planet gear in wind turbine gearbox based on spectral kurtosis and time wavelet energy spectrum

Planetary transmission plays a vital role in wind turbine drivetrains, and its fault diagnosis has been an important and challenging issue. Owing to the complicated and coupled vibration source, time-variant vibration transfer path, and heavy background noise masking effect, the vibration signal of planet gear in wind turbine gearboxes exhibits several unique characteristics： Complex frequency components, low signal-to-noise ratio, and weak fault feature. In this sense, the periodic impulsive components induced by a localized defect are hard to extract, and the fault detection of planet gear in wind turbines remains to be a challenging research work. Aiming to extract the fault feature of planet gear effectively, we propose a novel feature extraction method based on spectral kurtosis and time wavelet energy spectrum （SK-TWES） in the paper. Firstly, the spectral kurtosis （SK） and kurtogram of raw vibration signals are computed and exploited to select the optimal filtering parameter for the subsequent band-pass filtering. Then, the band-pass filtering is applied to extrude periodic transient impulses using the optimal frequency band in which the corresponding SK value is maximal. Finally, the time wavelet energy spectrum analysis is performed on the filtered signal, selecting Morlet wavelet as the mother wavelet which possesses a high similarity to the impulsive components. The experimental signals collected from the wind turbine gearbox test rig demonstrate that the proposed method is effective at the feature extraction and fault diagnosis for the planet gear with a localized defect.

Structural health monitoring of long-span suspension bridges using wavelet packet analysis

During the service life of civil engineering structures such as long-span bridges, local damage at key positions may continually accumulate, and may finally result in their sudden failure. One core issue of global vibration-based health monitoring methods is to seek some damage indices that are sensitive to structural damage, This paper proposes an online structural health monitoring method for long-span suspension bridges using wavelet packet transform （WPT）. The WPT- based method is based on the energy variations of structural ambient vibration responses decomposed using wavelet packet analysis. The main feature of this method is that the proposed wavelet packet energy spectrum （WPES） has the ability to detect structural damage from ambient vibration tests of a long-span suspension bridge. As an example application, the WPES-based health monitoring system is used on the Runyang Suspension Bridge under daily environmental conditions. The analysis reveals that changes in environmental temperature have a long-term influence on the WPES, while the effect of traffic loadings on the measured WPES of the bridge presents instantaneous changes because of the nonstationary properties of the loadings. The condition indication indices VD reflect the influences of environmental temperature on the dynamic properties of the Runyang Suspension Bridge. The field tests demonstrate that the proposed WPES-based condition indication index VD is a good candidate index for health monitoring of long-span suspension bridges under ambient excitations.

Analysis of Dynamic Tensile Process of Fiber Reinforced Concrete by Acoustic Emission Technique

The fiber reinforced concrete has good dynamic mechanical properties. But corresponding research lacks the dynamic damage characteristics of the polypropylene fiber（fiber of low elastic modulus） and steel fiber（fiber of high elastic modulus） reinforced concrete under medium strain rate（10-6 s-1-10-4 s-1）. In order to study the effect of strain rate on the damage characteristics of fiber reinforced concrete during the full curve damage process, the real time dynamic acoustic emission（AE） technique was applied to monitor the damage process of fiber reinforced concrete at three strain rates. The AE wavelet energy spectrum in ca8 frequency band and average AE peak frequency at three strain rates were analyzed. With the accumulation of damage, the AE wavelet energy spectrum in ca8 frequency band increased first and then decreased, and the average AE peak frequency increased gradually. With the increase of strain rate, the AE wavelet energy spectrum in ca8 frequency band and average AE peak frequency decreased gradually. The polypropylene fiber content has more obvious effect on the Dynamic increase factor（DIF） of the peak stress than the steel fiber content. The theoretical basis was provided for the monitoring of dynamic damage of fiber reinforced concrete based on the AE technique.