The rolling bearing is a key element of rotating machine and its fault diagnosis is a research focus. When a single fault of a rolling bearing fails to be addressed in time, it will cause progressive composite faults between the bearing and other elements. In this paper, the different composite fault cases of bearing and rotor are considered. First, an information fusion-empirical mode decomposition-angle adaptive distribution of polar coordinates image method is proposed, which has an adaptive image expression ability for the tested vibration signal, and can provide high-quality vibration image samples for diagnosis model training. Second, an intelligent diagnosis model combining a convolutional neural network and a support vector machine is proposed, which has an excellent generalization ability to recognize the different composite faults. Third, the different composite faults between rolling bearing and rotor are fabricated, tested and then diagnosed. The results show the test accuracy of the proposed method is higher than the conventional method and simple in the image mapping, which proves that this work is effective for the composite fault diagnosis of a rolling bearing and rotor.

Rolling bearing is key component of rotating machinery and its fault diagnosis is of great significance for reliable operation of machine. In this paper, an intelligent fault diagnosis method of rolling bearing based on FCM clustering of vibration images obtained by EMD-PWVD is presented. Firstly, vibration signals with different fault degrees are transformed into contour time-frequency images by EMDPWVD. Secondly, vibration images are divided into sections and their energy distribution values are used as image feature. Then, feature vectors are constructed for known signals, which are standardized as inputs of FCM clustering to obtain classification matrix and clustering center. Finally, proximity between tested samples and clustering centers of known samples are calculated to realize identification of bearing faults. Experimental results show that identification accuracy of this proposed method is high. When adding noise, the proposed method is more stable than other vibration images such as grayscale and symmetrical polar coordinate image, and when the added noise with SNR of 5, the reduction rate of identification accuracy is obviously smaller than those of other two methods.

Square steel tubes have been widely used in buildings and machines in civil engineering. The inspection of square tubes is becoming increasingly urgent and important to ensure the safety of these buildings and machines. However, the current most frequently used traditional ultrasonic inspection method is time-consuming and inefficient when dealing with long square tubes. There is an urgent need to develop an efficient approach to inspect square tubes. In this article, the use of ultrasonic guided waves is proposed. Phase and group velocity dispersion curves of square tube structures are first derived using the semi-analytical finite element method. An appropriate guided wave mode used for inspecting square tubes is selected. Ultrasonic guided waves propagating in normal, in-plane surface-damaged, and edge-damaged square tubes are numerically studied. It is illustrated that the monitoring points are able to receive reflected wave signals from both the in-plane surface and the edge damages. Experimental studies are also conducted to study ultrasonic guided waves interacting with circular through-hole damages located in surfaces and slot damages at edges. It is shown that both the circular through-hole damages located in different surfaces and slot damages at different edges can be clearly detected by reflected guided wave packets. It is found that the signal-to-noise ratios have been significantly improved after applying impedance matching to piezoelectric wafer transducers. The results have shown that ultrasonic guided waves are a promising and effective method for the inspection of square tubes.

Deep learning‐based fault diagnosis of rolling bearings is a hot research topic, and a rapid and accurate diagnosis is important. In this paper, aiming at the vibration image samples of rolling bearing affected by strong noise, the convolutional neural network‐ (CNN‐) and transfer learning‐ (TL‐) based fault diagnosis method is proposed. Firstly, four kinds of vibration image generation method with different characteristics are put forward, and the corresponding pure vibration image samples are obtained according to the original data. Secondly, using CNN as the adaptive feature extraction and recognition model, the influences of main sensitive parameters of CNN on the network recognition effect are studied, such as learning rate, optimizer, and L1 regularization, and the best model is determined. In order to obtain the pretraining parameters, the training and fault classification test for different image samples are carried out, respectively. Thirdly, the Gaussian white noise with different levels is added to the original signals, and four kinds of noised vibration image samples are obtained. The previous pretrained model parameters are shared for the TL. Each kind of sample research compares the impact of thirteen data sharing schemes on the TL accuracy and efficiency, and finally, the test accuracy and time index are introduced to evaluate the model. The results show that, among the four kinds of image generation method, the classification performance of data obtained by empirical mode decomposition‐pseudo‐Wigner–Ville distribution (EP) is the best; when the signal to noise ratio (SNR) is 10 dB, the model test accuracy obtained by TL is 96.67% and the training time is 170.46 s.

Ferrography analysis(FA) is an important approach to detect the wear state of mechanical equipment. Ferrographic image recognition based on deep learning needs a large number of image samples. However, the ferrographic images of mechanical equipment are difficult to obtain enough high-quality samples in a short time due to the complexity and low efficiency of the ferrogram making. Therefore, the recognition method for small sample ferrographic images based on the convolutional neural network(CNN) and transfer learning(TL) is proposed. Based on the similarity of samples, the virtual ferrographic image set is designed as the source data of the pretraining model, the tested CNN model is constructed by using the TL. Based on the AlexNet frame, this paper studies the influence of the CNN internal factors including network structure, convolution parameters, activation function, optimization mode, learning rate and the external factors on the classification effect of test samples, and the L2 regularizer is added to solve the overfitting. According to the classification result of test samples, an optimal parameter combination is obtained to establish an intelligent recognition model of ferrographic images based on CNN and TL with the recognition accuracy of 93.75%. Moreover, the t-SNE is used to realize the wear particle recognition process visualization, which proves the effectiveness of the proposed algorithm. This work provides an effective way for the ferrographic image recognition of wear particles under small samples.