Deep learning (DL) has become a popular option for data-driven fault diagnosis, because it can avert the influence of subjective factors in an artificial feature extraction process. However, it also suffers from the adverse effects accompanied with small fault sample and unbalanced data, resulting in limited accuracy improvement. For the aforementioned problem, this paper introduces a variational auto-encoder (VAE) into a fault diagnosis framework to realize data amplification by vibration signal generation, then an enhanced fault diagnosis approach is proposed combining with a convolution neural network. The well-trained VAE can realize the infinite generation of vibration signal by using the hidden variables sampled from Gaussian distribution, and then the generated artificial signals are mixed with the real original signals to form an enhanced training set, which can be utilized for classifier training to realize fault identification. Experimental results show that the generated artificial signals have similar time-frequency characteristics compared with the original real ones, and the enhanced fault diagnosis method holds a higher and more stable recognition accuracy than the unenhanced version and other typical methods.

Domain shift is the main reason that the recognition accuracy of traditional intelligent fault diagnosis methods greatly decreases. However, previous methods mainly focus on global domain matching, without considering the finer internal structure and making full use of source domain data, resulting in unsatisfactory adaptive effects. In this paper, we present an unsupervised joint subdomain adaptation network (JSAN), which reduces the discrepancy between the two domains by joint local maximum mean discrepancy (JLMMD). JLMMD divides the features of the specific activation layer into different subdomains according to the labels and matches the subdomains which have the same labels. At the same time, the joint distribution of the feature space and the prediction space of the target domain is matched utilizing the joint distribution of the source domain feature and the label. The experimental results show that the proposed method has achieved remarkable results in the domain adaptation task.

Vibration signals are widely applied in mechanical fault diagnosis methods. Strong and non-stationary noise can easily affect vibration signals, and the vibration signals will differ depending on the load conditions. Additionally, there aren’t many vibration signals with faults that can be employed because mechanical equipment typically operates as intended. A brand-new data augmentation and composite multi-scale network (DACMSN) for mechanical fault identification is proposed as a solution to these issues. The method of data augmentation is to generate samples simply and efficiently through a mathematical permutation and combination method. Each generated sample consists of two original samples, and each generated sample contains all fault information of the original sample. The dense multi-scale convolution neural network (DMSCNN) and the multi-scale residual network (MSRN) make up the composite multi-scale network (CMSN). The CMSN has rich multi-scale feature extraction capabilities and coarse-fine feature extraction capabilities. Convolutional kernels of various sizes are used in DMSCNN, which also takes into account the results of previous layers to initially discover fault features. MSRN further performs multi-scale feature extraction on each sub network of DMSCNN, achieving the effect of fully exploring fault features and effectively accelerating loss backpropagation. The proposed model can demonstrate unique advantages through the use of these mechanisms, such as a wider feature extraction scale and a more complete ability to extract coarse-fine features. This article compares the proposed method with different methods using noise and variable load dataset for tests. The experimental results demonstrate that the proposed method can perform well for fault diagnosis even in noisy environments.

The number of factors considered in the establishing of chatter model for regenerative chatter would directly affect the accuracy of numerical analysis. In this paper, considering the helix angle of cutter, the establishment method of three degrees of freedom complex model is investigated synthetically. An improved semi-discretization method is adopted for the prediction of milling stability of variable speed milling with helix angle. The influences of milling cutter helix angle, radial immersion rate and variable speed modulation coefficient on chatter suppression in milling process are studied. The results show that the selection of milling cutters with different helix angles has a certain influence on the overall chatter stability, especially the effect cannot be ignored under local special machining conditions. Excessive radial immersion rate will increase the probability of chatter. Compared with constant speed milling, the chatter can be suppressed obviously by variable speed milling, and the selection of variable speed modulation coefficient will directly affect the suppression effect of chatter.

Chatter is an unstable self-excited vibration generated during processing. It not only reduces the machining efficiency, machining accuracy, the service life of machine tools and cutting tools, but also results in sound pollution and material waste. To improve the machining stability and product quality of thin-walled workpieces, effective chatter detection of machine tools is essential. This paper presented a signal feature evaluation model and multi-feature recognition system for chatter detection. First, the original signals obtained from the acceleration sensor were processed through local mean decomposition to reduce the noise in the signal. Thereafter, the correlation between the system state and the different features of amplitude domain, frequency domain and nonlinear domain was analyzed. Further, through the feature evaluation model based on recursive feature elimination, the main feature parameters related to machine tool state are obtained, and different recognition algorithms were used to verify the rationality of the fusion features. Finally, an end-to-end chatter detection method and the corresponding software system have been established. Experimental results show that the proposed method can effectively improve the accuracy of vibration detection.