Aiming at the strong vibration condition of the inner curve radial plunger hydraulic motor, the bolt connection of the motor base is easy to loosen, and it is difficult to distinguish the early loosening faults online in time by using vibration signals. In this paper, we propose a bolt loosening fault diagnosis method based on LightGBM to recognize sound signal features, and this method can achieve online monitoring of bolt loosening faults. Through the vibration energy recovery test platform, the sound signals of four different bolt preload forces during the normal operation of the equipment were collected, and the bolt preload force was increased from completely loosened (0 N.m) to 60 N.m, with an increment of 20 N.m each time, and the sound signals were denoised using wavelet threshold denoising method. The LightGBM bolt loosening fault diagnosis model is constructed based on the gradient of one-sided sampling (GOSS) and mutually exclusive feature bundle (EFB) algorithms. By extracting the time and frequency domain features of the denoised sound signals, a dataset containing labels of normal and three faulty signals is generated for training and diagnosis. Finally, the diagnostic accuracy of this method is compared and verified. The results show that the LightGBM algorithm after wavelet threshold denoising improves the diagnostic accuracy by 2.17% over the no-denoising LightGBM, and by 5.47% and 2.21% over the XGboost algorithm before and after denoising, respectively.

The six-degree-of-freedom (6 DOF) motion of the mother ship, especially the heave motion, brings difficulties to offshore operations and even causes safety accidents. To ensure the reliability of the launch and recovery system when the active heave compensation (AHC) function works, its dynamic characteristics are analyzed. Firstly, the dynamic model of the launch and recovery system with a rigid rope is constructed, and the mathematical simulation of the launch and recovery system without the AHC function under the excitation of irregular waves is completed. Secondly, a flexible rope based on the finite element method is established, and the dynamic responses of the launch and recovery system with AHC function under different conditions are compared with those without AHC function. The simulation results demonstrate that the dynamic responses of the system are related to the load, the underwater penetration, and the sea condition. In detail, the increase of the load leads to an increase in the dynamic response of the system. In essence, the underwater penetration affects the dynamic response of the system by affecting the activity of load acceleration. Therefore, a short rope is preferred in the maritime operations. Moreover, the harsh sea condition usually causes an undesirable effect of AHC function. In summary, the overall work is conductive to the modeling of the launch and recovery system, as well as the development of AHC technology so as to improve the quality of offshore operations in the future.

Web page prefetching techniques are used to address the access latency problem of the Internet. To perform successful prefetching, we must be able to predict the next set of pages that will be accessed by users. The PageRank algorithm used by Google is able to compute the popularity of a set of Web pages based on their link structure. In this paper, a novel PageRank-like algorithm is proposed for conducting Web page prediction. Two biasing factors are adopted to personalize PageRank, so that it favors the pages that are more important to users. One factor is the length of time spent on visiting a page and the other is the frequency that a page was visited. The experiments conducted show that using these two factors simultaneously to bias PageRank results in more accurate Web page prediction than other methods that use only one of these two factors.