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Rolling bearing is a key element of rotating machine in safe and reliable operation, and its fault diagnosis is a research focus. When a single bearing fault fails to be addressed in time, it will cause the progressive composite faults between 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(IF-EMD-AADPCI) method is proposed, which has an adaptive image expression ability of tested vibration signal, and then can provide the high-quality vibration image samples for the model training. Second, an intelligent diagnosis model combining Convolutional Neural Network(CNN) and Support Vector Machine(SVM) is proposed, which has an excellent generalization ability to recognize the different composite faults. Third, the different compound 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 transform, which proves that this work is effective for the composite fault diagnosis of 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 EMD-PWVD. 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.

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.

This paper presents Kinect-based vision system of mine rescue robot working in illuminous underground environment. The somatosensory system of Kinect is used to realize the hand gesture recognition involving static hand gesture and action. A Kcurvature based convex detection method is proposed to fit the hand contour with polygon. In addition, the hand action is completed by using the NiTE library with the framework of hand gesture recognition. In addition, the proposed method is compared with BP neural network and template matching. Furthermore, taking advantage of the information of the depth map, the interface of hand gesture recognition is established for human machine interaction of rescue robot. Experimental results verify the effectiveness of Kinect-based vision system as a feasible and alternative technology for HMI of mine rescue robot.

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.

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