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The fault features of gearbox are often influenced and interwoven with each other under the non-stationary condition. The traditional shallow intelligent diagnosis models are difficult to detect and identify gearbox faults with selected features according to prior knowledge. To solve this problem, a novel deep convolutional auto-encoding neural network is designed based on the fusion of the convolutional neural network with the automatic encoder in this research. The vibration signals of gearbox are transformed into Hilbert envelope spectrum by using Hilbert transform and Fourier transform, and the different characteristics of spectral spatial data are automatically learned by convolutional auto-encoding neural network with multiple convolution kernels. The parameters of the convolutional neural network are fine-tuned through a fully connected neural network with a small number of labeled samples. Through the analysis for gearbox fault experiments, the effectiveness and practicability of the proposed method in equipment fault diagnosis are verified. The deep convolutional neural network embedded in the auto-encoder has stronger learning ability, and the diagnosis performance is more stable and reliable in practical engineering application.

The traditional reliability evaluation method based on large sample statistics is inefficient for a single or a small batch computer numerical control turning cutting tool due to the inadequate description of time, dynamic process, inaccurate model and individualization. To solve the problem, a new operation reliability evaluation method based on singular value decomposition transform and support vector space is proposed. In this new method, the singular value decomposition is used for the dimensionality reduction of high-dimensional feature data so as to reduce the computational complexity and the redundant components. The hypersphere space of the similar data is established based on the dimension reduction data. The relative distance between the sample points and the hypersphere is then calculated and used to describe the performance of the tool. The semi-normal function is introduced to define the mapping relationship of the relative distance and the operation reliability of the tool. Finally, two cutting tools in the experiment are taken as the research example to verify the effectiveness of the method. The result shows that this method can evaluate the operation reliability of the tool effectively and the singular value decomposition dimensionality reduction improves the accuracy of the evaluation. It provides a new theoretical and practical support for the reliability evaluation of small sample data.