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.
Milling is a main processing mode of the modern manufacturing industry, which seriously affects the quality and precision of the machined workpiece. However, it is difficult to monitor the tool wear condition in the continuous cutting process, especially under a variable speed condition. The existing tool wear condition monitoring methods only carry out analysis with a constant engine speed. Different from the general monitoring methods, this paper put forward a milling cutter wear condition monitoring method based on order analysis (OA) and stacked sparse autoencoder (SSAE). The methodology in the research include signals feature extraction and tool wear state monitoring and were designed to analyze the three-phase spindle current signals instead of the traditional force signals and vibration signals. The variable speed signals were transformed into angle domain stationary signals by order analysis, and the SSAE neural network was used to monitor the tool wear state. The proposed method was verified on the laboratory signals and the results showed a better performance than the other methods and a better applicability in actual industrial manufacturing.
Synthesis of uniform and dense metal nanostructures on the whole exposed fiber, instead of simply on the membrane surface, is necessary for controllable separation and sensitive SERS detection.
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