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.

The underground coal mine production of the fully mechanized mining face exists many problems, such as poor operating environment, high accident rate and so on. Recently, the intelligent autonomous coal mining is gradually replacing the traditional mining process. The artificial intelligence technology is an active research area and is expect to identify and warn the underground abnormal conditions for intelligent longwall mining. It is inseparable from the construction of datasets, but the downhole dataset is still blank at present. This work develops an image dataset of underground longwall mining face (DsLMF+), which consists of 138004 images with annotation 6 categories of mine personnel, hydraulic support guard plate, large coal, towline, miners’ behaviour and mine safety helmet. All the labels of dataset are publicly available in YOLO format and COCO format. The availability and accuracy of the datasets were reviewed by experts in coal mine field. The dataset is open access and aims to support further research and advancement of the intelligent identification and classification of abnormal conditions for underground mining.

In order to improve vibration energy harvesting, this paper designs an arc-shaped piezoelectric bistable vibration energy harvester (ABEH). The bistable configuration is achieved by using magnetic coupling, and the nonlinear magnetic force is calculated. Based on Lagrangian equation, piezoelectric theory, Kirchhoff’s law, etc., a complete theoretical model of the presented ABEH is built. The influence of the nonlinear stiffness terms, the electromechanical coupling coefficient, the damping, the distance between magnets, and the load resistance on the dynamic response and the energy harvesting performance of the ABEH is numerically explored. More importantly, experiments are designed to verify the energy harvesting enhancement of the ABEH. Compared with the non-magnet energy harvester, the ABEH has much better energy harvesting performance.

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.

In engineering, the fault data unevenly distribute and difficultly share, which causes that the existing fault diagnosis methods cannot recognize the newly added fault types. An intelligent diagnosis method for machine fault is proposed based on federated learning. Firstly, the local fault diagnosis models diagnosing the existing fault data and the newly added fault data are established by deep convolutional neural network. Then, the weight parameters of local models are fused into global model parameters by federated learning. Finally, the global model parameters are transmitted to each local model. Therefore, each local model update into a global shared model which can recognize the newly added fault types. The proposed method is verified by bearing data. Compared with the traditional model, which can only diagnose the existing fault data but cannot recognize newly added fault types, the federated fault diagnosis model fusing weight parameters can diagnose newly added faults without exchanging the data, and the accuracy is 100%. The proposed method provides an effective method to solve the poor sharing of fault data and poor generalization of fault diagnosis model for mechanical equipment.

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.