Zhigang Chen’s research while affiliated with Beijing University of Civil Engineering and Architecture and other places

The non-stationary operating conditions of wind turbines, along with adverse environmental factors, frequently cause the rotation shaft to move abnormally, resulting in periodic impulse interference and noise. There are significant challenges for the accurate diagnosis of wind turbine bearing faults. Box-cox sparse measures (BCSM) deconvolution is a validity method for machinery fault diagnosis because it is significantly for reducing noise and eliminating the interference of the system transmission path. The improper selection of the initial filter can result in the failure of BCSM deconvolution (BCSMD) to effectively extract fault characteristics of wind turbine bearings subjected to strong periodic interference. To overcome this limitation, a simulation-guided BCSMD method is proposed. In the initial step, a finite element method simulation is conducted to identify the potential carrier frequency derived from the first flexural frequency in modal analysis. Subsequently, this carrier frequency is employed as the central frequency, along with a predetermined filter length, to ascertain the appropriate filter. Finally, the simulation designed filter is used as the initial filter of BCSMD to filter the raw signal. Compared with the original BCSMD method, its main contribution lies in the organic integration of a finite element simulation with filter design, thereby effectively preventing the iteratively derived filter from converging to non-fault frequency bands. Experimental results indicate that simulation-guided BCSMD can precisely identify fault characteristics even in the presence of periodic impulse interference.

Resonance demodulation is one of the most effective methods for rolling bearing fault diagnosis. However, the selection of the proper demodulation frequency band (DFB) has always been considered as a substantial challenge. Although many popular DFB selection methods have been developed, such as fast Kurtogram (FK), Protrugram, and Autogram, they would suffer unsatisfactory performance degradation when encountering random impulsive noise or cyclostationary noise. Therefore, this paper proposes a novel DFB selection method called Losengram to address this problem. In the proposed method, a robust sub-band indicator, localized square envelope spectrum kurtosis, is designed to evaluate the fault information in a sub-band. With this indicator, the interferences of random impulsive noise and cyclostationary noise could be suppressed well. Besides, in order to circumvent the various adverse effects incurred by the utilization of a multi-rate finite impulse response filter bank, a frequency-domain sub-band filtering strategy is presented to filter the divided sub-bands in a 1/3-binary tree structure. The effectiveness of the proposed method is tested on both simulated and experimental signals, and the results show that it has a superior performance than the FK, Protrugram, as well as Autogram.

The Jiles-Atherton model is key to researching the hysteresis loop. The focus of scholars across various countries has always been the parameter identification of the Jiles-Atherton model. This paper on the Levy whale optimization algorithm (LWOA), based on the whale optimization algorithm (WOA), proposes to overcome the disadvantage that WOA tends to involve the local optimum. The recommended algorithm uses the Levy flight strategy instead of the encircling prey policy since the former improves the global search. Therefore, the new algorithm is better at stability and calculation accuracy. To substantiate the efficacy of the proposed algorithm, it is tested against six benchmark functions and compared with the WOA, particle swarm optimization (PSO), grey wolf algorithm (GWO), and shuffled frog leaping algorithm (SFLA). In addition, the proposed algorithm is applied to realize two classical engineering problems, such as the tension/compression spring and welded beam design issues. The experimental findings reveal that the proposed algorithm is highly competitive with metaheuristic optimizers and improves the algorithm’s performance. To address the poor stability of the J-A model parameter identification, an improved calculation method for parameter ${k}$ and the reduced parameter ranges of the model parameters ${a}$ and $\alpha$ were combined with LWOA. The proposed algorithm is called C-LWOA, which is compared with LWOA, PSO, GWO, SFLA, and the cuckoo search (CS) based on the data reported in the literature. Moreover, the simulation results demonstrate that the stability and calculation accuracy of the parameter identification by the C-LWOA was significantly strengthened. Equally important, the calculation error was within 0.2%. Finally, the proposed algorithm was subsequently used to fit the actual measurements of the hysteresis loop of permalloy.

The power end of a fracturing truck is a key component that provides kinetic energy during pressure operations. Its vibration signal is collected during operation because of the complex working conditions and heavy loads, resulting in the collected signal being filled with a large amount of noise, for which it is difficult to perform effective fault feature extraction. To address this problem, a new fault diagnosis method is proposed. This method combines local mean decomposition (LMD) and synchroextracting transform (SET) for signal processing. First, LMD processing is done on the acquired signal to obtain several product function (PF) components. The cross-correlation coefficient and kurtosis value are used as references to select the true PF components. After that, the SET method is used to process the real PF components, extract the energy that is most correlated with the time-varying features of the signal, remove the fuzzy energy, improve the time-frequency resolution, and enhance the fault features contained in the signal to facilitate accurate fault diagnosis. Finally, the vibration signals collected from the power end of the fracturing vehicle are experimentally verified. The results show that the method can accurately extract the fault characteristics of bearing failure in the power end, and provide some useful reference for the diagnosis method of fracturing vehicle power system.

To solve the problem of inconspicuous feature extraction when LMD method is used to extract rolling bearing fault characteristic signals, A fault feature extraction method based on Local Mean Decomposition (LMD) and Transient- Extracting Transform (TET) was proposed. Firstly, the rolling bearing fault signals were processed by LMD and the feature components with rich fault information were screened out by using the kurtosis values. Then, the secondary feature extraction and envelope analysis of the acquired components were carried out by using TET method. The experimental results showing that this method can extract the pulse characteristics of the impact signal of rolling bearings efficiently, and is suitable for the fault diagnosis of rolling bearing.