Poor working environment leads to frequent failures of planetary gear trains. However, complex structure and variable transmission make the vibration signal strongly non-linear and non-stationary, which brings big problems to fault diagnosis. A method of planetary gear fault diagnosis via feature image extraction based on multi central frequencies and vibration signal frequency spectrum is proposed. The original vibration signal is decomposed by variational mode decomposition (VMD), and four components with narrow bands and independent central frequencies are decomposed. In order to retain the feature spectrum of the original vibration signal as far as possible, the corresponding feature bands are intercepted from the frequency spectrum of original vibration signal based on the central frequency of each component. Then, the feature images of fault signals are constructed as the inputs of the convolution neural network (CNN), and the parameters of the neural network are optimized by sample training. Finally, the optimized CNN is used to identify fault signals. The overall fault recognition rate is up to 98.75%. Compared with the feature bands extracted directly from the component spectrums, the extraction method of the feature bands proposed in this paper needs fewer iterations under the same network structure. The method of planetary gear fault diagnosis proposed in this paper is effective.

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We report on various approaches to control the antiferro-ferromagnetic transition temperature of Mn1.2Co0.8Si0.2P0.8 and related materials for magnetocaloric applications. The effects of the Si substitution, Mn/Co ratio and B addition/substitution on MnCoP have been characterized by X-ray diffraction and magnetic measurements. In each case, these parameters have a great influence on the structural and magnetic properties. Silicon and boron turn out to be the elements suitable to substitute the phosphorous and bring TN closer to room temperature. The study thus paves the way for future tuning of the working temperature and adjustment of the magnetic properties. With entropy change of about 1.4 J kg-1 K-1 for 5 T, the inverse magnetocaloric effect of Mn1.2Co0.8Si0.2P0.8 is modest, but might present some interest for applications other than magnetic cooling.@en