Variational mode decomposition (VMD) is widely used in the condition monitoring and fault diagnosis of rotary machinery for its unique advantages. An adaptive parameter optimized VMD (APOVMD) is proposed in order to adaptively determine the suitable decomposed parameters and further enhance its performance. The traditional singular value decomposition (SVD) method cannot effectively select the reconstructed order, which often leads to unsatisfactory results for signal reconstruction. Thus, a singular kurtosis difference spectrum method is proposed to accurately determine the effective reconstructed order for signal noise reduction. In addition, because the fault signal of the planetary gearbox at the early fault stage is weak and susceptible to ambient noise and other signal interference, the fault feature information is difficult to extract. To address this issue, a novel method for early fault feature extraction of planetary gearbox based on APOVMD and singular kurtosis difference spectrum is proposed in this paper. First, the APOVMD is applied to decompose the planetary gearbox vibration signal into a series of band-limited intrinsic mode functions adaptively and non-recursively. Second, the sensitive component is selected from the IMFS according to the cosine similarity index. Third, the Hankel matrix is constructed for the sensitive component in the phase space and decomposed by SVD. Here, the effective reconstructed order is automatically selected by the singular kurtosis difference spectrum method for noise reduction. Finally, the Hilbert envelope spectrum analysis is carried out on the reconstructed signal, and the fault characteristic frequency information of planetary gearbox can be accurately extracted from the envelope spectrum to realize the fault identification and location. The results of simulation studies and actual experimental data analysis demonstrate that the proposed method has superior ability to extract the early weak fault characteristics of the planetary gearbox compared with the VMD-SVD and EEMD-SVD methods, and the validity and feasibility of the presented method are proved. INDEX TERMS Planetary gearbox, adaptive parameter optimized VMD, singular kurtosis difference spectrum, cosine similarity, early fault diagnosis.
PurposeThis study aimed to explore the effect of exercise and cold exposure on insulin sensitivity and the level of serum free fatty acids (FFA) in diet-induced obese rats.MethodsSixty-four diet-induced obese rats were randomly assigned to eight groups: room temperature–sedentary, room temperature–exercise, acute cold exposure–sedentary, acute cold exposure–exercise, intermittent cold exposure–sedentary, intermittent cold exposure–exercise, sustained cold exposure–sedentary, and sustained cold exposure–exercise. After the interventions, the homeostatic model assessment for insulin resistance (HOMA-IR) values, the level of serum FFA, subcutaneous fat ratio (SFR) and visceral fat ratio, enzyme activities of adipose triglyceride lipase, and lipoprotein lipase (LPL) in inguinal adipose tissue, and protein expression of PGC1-α and p38 MAPK in skeletal muscle were investigated.ResultsWe found that exercise (P = 0.0136) and cold exposure (P < 0.0001) reduced HOMA-IR values independently. Exercise reduced serum FFA (P = 0.0041), whereas cold exposure did not affect them. Moreover, the HOMA-IR values were positively correlated with the serum FFA levels (r = 0.32, P = 0.01). SFR or visceral fat ratio was coordinately reduced by the interaction (for SFR, P = 0.0015) or opposing main effects between or of cold exposure and exercise, supporting the reduction of serum FFA. However, cold exposure or exercise increased the activity of adipose triglyceride lipase and LPL independently or interactively (for LPL, P = 0.0143), suggesting an increase in serum FFA. Finally, cold exposure and exercise enhanced protein expression of PGC1-α and p38 MAPK independently or interactively (for p38 MAPK, P = 0.0226), suggesting increased uptake and oxidation of serum FFA in muscle.ConclusionsThese results suggest that the combination of exercise and cold exposure may result in more serum FFA utilization than production and thus lead to reduced serum FFA and increased insulin sensitivity.
Aiming at the problem that the vibration signals of rolling bearings working in a harsh environment are mixed with many harmonic components and noise signals, while the traditional sparse representation algorithm takes a long time to calculate and has a limited accuracy, a bearing fault feature extraction method based on the ensemble empirical mode decomposition (EEMD) algorithm and improved sparse representation is proposed. Firstly, an improved orthogonal matching pursuit (adapOMP) algorithm is used to separate the harmonic components in the signal to obtain the filtered signal. The processed signal is decomposed by EEMD, and the signal with a kurtosis greater than three is reconstructed. Then, Hankel matrix transformation is carried out to construct the learning dictionary. The K-singular value decomposition (K-SVD) algorithm using the improved termination criterion makes the algorithm have a certain adaptability, and the reconstructed signal is constructed by processing the EEMD results. Through the comparative analysis of the three methods under strong noise, although the K-SVD algorithm can produce good results after being processed by the adapOMP algorithm, the effect of the algorithm is not obvious in the low-frequency range. The method proposed in this paper can effectively extract the impact component from the signal. This will have a positive effect on the extraction of rotating machinery impact features in complex noise environments.
Abstract:Rolling element bearings are of great importance in planetary gearboxes. Monitoring their operation state is the key to keep the whole machine running normally. It is not enough to just apply traditional fault diagnosis methods to detect the running condition of rotating machinery when weak faults occur. It is because of the complexity of the planetary gearbox structure. In addition, its running state is unstable due to the effects of the wind speed and external disturbances. In this paper, a signal model is established to simulate the vibration data collected by sensors in the event of a failure occurred in the planetary bearings, which is very useful for fault mechanism research. Furthermore, an improved wavelet scalogram method is proposed to identify weak impact features of planetary bearings. The proposed method is based on time-frequency distribution reassignment and synchronous averaging. The synchronous averaging is performed for reassignment of the wavelet scale spectrum to improve its time-frequency resolution. After that, wavelet ridge extraction is carried out to reveal the relationship between this time-frequency distribution and characteristic information, which is helpful to extract characteristic frequencies after the improved wavelet scalogram highlights the impact features of rolling element bearing weak fault detection. The effectiveness of the proposed method for weak fault recognition is validated by using simulation signals and test signals.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.