Most online tool condition monitoring (TCM) methods easily cause machining interference. To solve this problem, we propose a method based on the analysis of the spindle motor current signal of a machine tool. Firstly, cutting experiments under multi-conditions were carried out at a Fanuc vertical machining center, using the Fanuc Servo Guide software to obtain the spindle motor current data of the built-in current sensor of the machine tool, which can not only apply to the actual processing conditions but, also, save costs. Secondly, we propose the variational mode decomposition (VMD) algorithm for feature extraction, which can describe the tool conditions under different cutting conditions due to its excellent performance in processing the nonstationary current signal. In contrast with the popular wavelet packet decomposition (WPD) method, the VMD method was verified as a more effective signal-processing technique according to the experimental results. Thirdly, the most indicative features that relate to the tool condition were fed into the ensemble learning (EL) classifier to establish a nonlinear mapping relationship between the features and the tool wear level. Compared with existing TCM methods based on current sensor signals, the operation process and experimental results show that using the proposed method for the monitoring signal acquisition is suitable for the actual processing conditions, and the established tool wear prediction model has better performance in both accuracy and robustness due to its good generalization capability.

Blood vessels serve an important role in tumor growth and metastasis, and recent studies have shown that certain tumor cancer stem cells may differentiate into endothelial cells and contribute to angiogenesis. In the present study, vascular endothelial growth factor (VEGF) was used to induce endothelial differentiation of breast cancer stem-like cells (BcSLcs), and methods including flow cytometry, western blotting and immunofluorescence were used to study the relationship between autophagy and the endothelial differentiation of BcSLcs. The results showed that BcSLcs could differentiate into endothelial cells under the induction of VEGF in vitro. Subsequently, the role of autophagy in the endothelial differentiation of BcSLcs was examined. Autophagic activity was measured during endothelial differentiation of BcSLcs, and the association between autophagy and endothelial differentiation was investigated using autophagy activators, autophagy inhibitors and autophagy related 5 (Atg5)-knockdown BcSLcs. Autophagy was increased during endothelial differentiation of BcSLcs, and there was a positive association between autophagy and endothelial differentiation. The ability of cells to undergo endothelial differentiation was reduced in BcSLcs with Atg5 knockdown. Therefore, autophagy was essential for endothelial differentiation of BcSLcs, and the findings of the present study may highlight novel potential avenues for reducing angiogenesis and improving treatment of breast cancer.

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Because the linear motor feeding system always runs in complex working conditions for a long time, its performance and state transition have great randomness. Therefore, abnormal detection is particularly significant for predictive maintenance to promptly discover the running state degradation trend. Aiming at the problem that the abnormal samples of linear motor feed system are few and the samples have time-series features, a method of abnormal operation state detection of a linear motor feed system based on normal sample training was proposed, named GANomaly-LSTM. The method constructs an encoding-decoding-reconstructed encoding network model. Firstly, the time-series features of vibration, current and composite data samples are extracted by the long short-term memory (LSTM) network; Secondly, the three-layer fully connected layer is employed to extract potential feature vectors; Finally, anomaly detection of the system is completed by comparing the potential feature vectors of the two encodings. An experimental platform of the X-Y two-axis linkage linear motor feeding system is built to verify the rationality of the proposed method. Compared with other classical methods such as GANomaly and GAN-AE, the average AUROC index of this method is improved by 17.5% and 9.3%, the average accuracy is enhanced by 11.6% and 15.5%, and the detection time is shortened by 223 ms and 284 ms, respectively. GANomaly-LSTM has successfully proved its superiority for abnormal detection for running state of linear motor feeding systems.

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