To analyze the vibration characteristic differences of a gear transmission system with gear and shaft cracks, an improved computational method for deriving the shaft stiffness matrix with breathing cracks is developed. Also, the three-dimensional contact model of spur gear with a crack is established via the finite element method (FEM), and its meshing stiffness is calculated. Simultaneously, considering bearing stiffness and shaft flexibility, the finite element dynamic models of two-stage gear transmission system with gear and shaft cracks are established. Based on this, different source fault vibration responses are compared and the influencing factors are explored. In addition, a novel signal processing method based on the particle swarm optimization, maximum correlated kurtosis deconvolution, variational, mode decomposition and fast spectral kurtosis (PSO-MCKD-VMD-FSK) is utilized to extract fault characteristics for the signal-to-noise ratio and uneven energy distribution problems. Results show that a system with gear cracks mainly presents periodic impact in the time domain, while in the frequency domain it impacts rotation frequency modulation near the meshing frequency and its multiple frequencies. However, the shaft crack breathing effect meant that the time domain mainly presents “simple harmonic” modulation, and the rotation frequency and its faulty shaft multiplication occurs in the low-frequency region of the frequency domain. The PSO-MCKD-VMD-FSK method extracts fault features in a strong noise environment and has good robustness. Results identify different source faults and provide a theoretical basis.

Rice husk powder was oxidized in aqueous sodium hypochlorite solution under mild conditions with different reaction times. Fourier transform infrared spectroscopy, gas chromatography-mass spectrometry (GC-MS), and direct analysis in real time-mass spectrometry (DART-MS) were used to analyze the oxidation products. Results showed that oxidation was a feasible way to depolymerize the macromolecules in the biomass and convert hydroxyl groups to carboxyl groups. In total, 113 organic compounds in oxidation products with molecular mass less than 500 Da were identified using GC-MS. As an ambient ionization technique, DART-MS was applied to the determination of biomass derivatives and revealed mass distribution and molecular structure information for the rice husk oxidation products.

The reliability and safety of rotating equipment depend on the performance of bearings. For complex systems with high reliability and safety needs, effectively predicting the fault data in the use stage has important guiding significance for reasonably formulating reliability plans and carrying out reliability maintenance activities. Many methods have been used to solve the problem of reliability prediction. Due to its convenience and efficiency, the data-driven method is increasingly widely used in practical reliability prediction. In order to ensure the reliability of bearing operation, the main objective of the present study is to establish a novel model based on the optimized maximum correlation kurtosis deconvolution (MCKD) and long short-term memory (LSTM) recurrent neural network to realize early bearing fault warnings by predicting bearing fault time series. The proposed model is based on the lifecycle vibration signal of the bearing. In the first step, the cuckoo search (CS) is utilized to optimize the parameter filter length and deconvolution period of MCKD, considering the influence of periodic bearing time series, and to improve the fault impact component of the optimized MCKD deconvolution time series. Then the LSTM learning rate is selected according to the deconvolution time series. Finally, the dataset obtained through various preprocessing approaches is used to train and predict the LSTM model. The analyses performed using the XJTU-SY bearing dataset demonstrate that the prediction results are in good consistency with real fault data, and the average prediction accuracy of the optimized MCKD–LSTM model is 26% higher than that of the original time series.

Vibration signal is the most used monitoring signal in fault diagnosis. However, the location of the sensor will be restricted in some special cases. Therefore, non-contact microphone sensors are often used in these cases, which may cause some problems. For example, acoustic signals are always the low signal-to-noise ratio signals, which are difficult to accurately detect faults. Hilbert-Huang Transform (HHT) is a powerful method for bearing fault diagnosis, which includes Empirical Mode Decomposition (EMD) and Hilbert Transform (HT). However, mode aliasing and redundant modes are common in empirical mode decomposition, which are bad for detecting bearing faults. Aiming at the disadvantages of HHT, the Variational-Hilbert-Huang Transform (VHHT) is proposed for weak bearing fault diagnosis. Firstly, the signal is decomposed into several intrinsic mode functions (IMFs) through EMD. Then sensitive IMFs, which are chosen through the sensitivity evaluation algorithm, are decomposed by variational mode decomposition (VMD) to obtain several limited bandwidth intrinsic mode function (LBIMFs). Next, LBIMFs are reorganized according to the signal characteristics to obtain the fault component. Finally, the bearing fault is detected by HT. The proposed method is verified by simulation and experimental signals, and compared with HHT and VMD. The results show that VHHT is more prominent in detecting faults in low SNR rolling bearing signals.

Planetary gear train fault diagnosis is critical to Prognostics and Health Management of the helicopter. Generally, the localized gear fault will cause periodic impulses in the vibration signal. However, the fault characteristic generated by incipient fault is often weak and submerged in strong background noise, resulting in difficulty in fault feature extraction. To address this issue, a method combining Maximum Correlated Kurtosis Deconvolution(MCKD) and Overlapping Group Shrinkage(OGS) algorithms is proposed. Firstly, Fruit fly Optimization Algorithm(FOA) was employed to search for the optimal influencing parameters of MCKD, and the impulses of the raw fault signal could be enhanced after processed by MCKD adaptively. Then, the deconvolution signal was further processed by OGS to eliminate the residual noise. Finally, the fault characteristic frequency components could be identified by analyzing the envelope spectrum of the filtered signal, and achieved the incipient fault diagnosis of helicopter planetary gear. The proposed method is validated using the vibration signal of the planetary gear train in a helicopter transmission test rig, and a slight cracked planetary gear fault is successfully identified.

According to the characteristics of the large transmission ratio of the helicopter main reducer in the future, the research on the characteristic distribution of the planetary gear train of the helicopter main reducer is carried out. Because of the characteristics that the heavy load condition in helicopter main reducer’s running and the changeable running state and complex and harsh climatic conditions and the increasing heat of the flow field in the reducer, by the way, the large transmission ratio increases the uncertainty of the planetary gear train operation analysis at the same time, the probability of causing a failure is greater; Especially the complex and variable structure of the helicopter which has large transmission ratio main reducer makes the analysis difficulty further. For ensuring the safety and enhancing the reliability of the helicopter, this paper make a comparative analysis of the time domain characteristics of the state signals under the multi-operating condition between the normal planetary gear and planetary gears with different degrees of failure, to explore the distribution regularity of the fault characteristics of the helicopter main reducer, so as to realize the research on fault diagnosis of helicopter large transmission ratio planetary gear.