Wind turbine blade bearings are often operated in harsh circumstances which may easily be damaged causing the turbine to lose control and to further result in the reduction of energy production. However, for condition monitoring and fault diagnosis (CMFD) of wind turbine blade bearings, one of the main difficulties is that the rotation speeds of blade bearings are very slow (less than 5 rpm). Over the past few years, Acoustic Emission (AE) analysis has been used to carry out bearing CMFD. This article presents the results that reflect the potential of the AE analysis for diagnosing a slow-speed wind turbine blade bearing. To undertake this experiment, a 15-year-old naturally damaged industrial and slow-speed blade bearing is used for our study. However, due to very slow rotation speed conditions, the fault signals are very weak and masked by heavy noise disturbances. To denoise the raw AE signals, we propose a novel cepstrum editing method, discrete/random separation based cepstrum editing liftering (DRS-CEL), to extract weak fault features from raw AE signals, where DRS is used to edit the cepstrum. Thereafter, the morphological envelope analysis is employed to further filter the residual noise leaked from DRS-CEL and demodulate the denoised signal, so the specific bearing fault type can be inferred in the frequency domain. The diagnostic framework combining DRS-CEL and morphological analysis is validated by comparing several methods and related studies which offers a promising solution for wind farm applications.

We demonstrate that if the dark matter (DM) in the Universe contains multiple components, the possible interactions between the DM components may convert the heavier DM components into lighter ones. It is then possible that the lightest DM component with an annihilation cross section significantly larger than that of the typical weakly interacting massive particle (WIMP) may lead to a relic density in agreement with cosmological observations, due to an enhancement of number density from the DM conversion process at late time after the thermal decoupling. This may provide an alternative source of boost factor relevant to the positron and electron excesses reported by the recent DM indirect search experiments.

This article uses acoustic emission (AE) analysis to diagnose an industrial-scale and slow-speed wind turbine blade bearing. The main challenge for AE analysis is that the fault signals are mingled with heavy noise. As a result, the objective of this article is to filter the raw AE signals and extract weak fault signals. To achieve this goal, a general linear and nonlinear auto-regressive (GLNAR) model is first developed to exploit the nonlinear characteristics of the AE signals. Then, the sparse augmented Lagrangian (SAL) algorithm is applied to learn the built GLNAR model and filter the raw AE signals. The characteristics of SAL is that it is a novel sparse representation technique which can convert the original filtering problem into a number of suboptimization problems, and these subproblems can be solved separately. Finally, as the blade bearing rotates at fluctuating speeds, the filtered signals are resampled so that the bearing fault type can be diagnosed in the order domain. The proposed diagnostic framework was validated in several experiments under time-varying low-speed and heavy-blade-load conditions. The results indicate that our proposed methods are effective and accurate.