At present, most of the intelligent fault diagnosis methods of rolling element bearings require sufficient labeled data for training. However, collecting labeled data is usually expensive and time-consuming, and when the distribution of the test data is different from the distribution of the training data, the diagnostic performance will decrease. In order to solve the problem of unlabeled cross-domain diagnosis of bearings, this paper proposes an adversarial domain adaption method based on deep transfer learning. The short-time Fourier transform is used to transform the original data into a time-frequency image. The feature extractor is used to extract its deep features. The maximum mean discrepancy and domain confusion function are used for domain adaptation to extract domain-invariant features between two domains for cross-domain fault diagnosis. Experiments on two bearing datasets are carried out for validations. The results prove that the method in this paper is superior to other deep transfer learning methods. It shows the advantages of the improved method and can be used as an effective tool for cross-domain fault diagnosis.

Tool wear monitoring is essential in precision manufacturing to improve surface quality, increase machining efficiency, and reduce manufacturing cost. Although tool wear can be reflected by measurable signals in automatic machining operations, with the increase of collected data, features are manually extracted and optimized, which lowers monitoring efficiency and increases prediction error. For addressing the aforementioned problems, this paper proposes a tool wear monitoring method using vibration signal based on short-time Fourier transform (STFT) and deep convolutional neural network (DCNN) in milling operations. First, the image representation of acquired vibration signals is obtained based on STFT, and then the DCNN model is designed to establish the relationship between obtained time-frequency maps and tool wear, which performs adaptive feature extraction and automatic tool wear prediction. Moreover, this method is demonstrated by employing three tool wear experimental datasets collected from three-flute ball nose tungsten carbide cutter of a high-speed CNC machine under dry milling. Finally, the experimental results prove that the proposed method is more accurate and relatively reliable than other compared methods.

Tool wear monitoring is a typical multi-sensor information fusion task. The handcrafted features may be a suboptimal choice that will lower the monitoring accuracy and require significant computational costs that hinder the real-time applications. In order to solve these problems, this paper proposed a new multisensory data-driven tool wear predicting method based on reshaped time series convolutional neural network (RTSCNN). In this method, the reshaped time series layer is introduced to represent the multisensory raw signals, the alternately convolutional and pooling layers is employed to adaptively learn distinctive characteristics of tool wear directly from multisensory raw signals while the multi-layer perceptron with regression layer performs automatic tool wear prediction. In addition, three tool run-to-failure datasets measured from three-flute ball nose tungsten carbide cutter of high-speed CNC machine under milling operations are used to experimentally demonstrate the performance of the proposed RTSCNN-based multisensory data-driven tool wear predicting method. The experimental results show that the prediction error of the RTSCNN-based data-driven method is observably lower than other state-of-art methods.