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Intelligent diagnosis is an important manner for mechanical fault diagnosis in the era of industrial big data, and deep network has received extensive attention in this field because of automatically learning features and classifying entered samples. As a classic deep learning model, Convolutional Neural Network has been applied in mechanical intelligent fault diagnosis. However, the limitation is that entered samples must be balanced to achieve satisfactory recognition rate. During the operation of machinery, the normal samples are abundant and the fault samples are rare. Therefore, the recognition rate of the minority category is minor when processing the imbalanced data with Convolutional Neural Network. To solve the above problem, an intelligent classification method for imbalanced mechanical data based on Deep Cost Adaptive Convolutional Network is proposed. According to this model, first, it learns intrinsic state characteristics in mechanical raw signals through multiple convolution and pooling operations. Second, it maps these characteristics to mechanical health condition by fully connected layers. Finally, the cost adaptive loss function adaptively assigns different misclassification costs for all categories and keeps updating them in training process to effectively classify the imbalanced mechanical data. The proposed method is verified by bearing data and milling cutter data with different imbalanced ratio, and compared with other methods. The experimental results show that the proposed method is robust and is able to effectively classify the imbalanced mechanical data.

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In this paper, an intelligent evaluation method is proposed to quantitatively characterize surface-breaking cracks based on laser ultrasonic technique and the quantized particle swarm optimized support vector regression algorithm. Based on the physical model analysis, interactions between laser-generated surface acoustic waves (SAWs) and different cracks is numerically investigated. By selecting crucial features of the transmissions and reflections after interacting with cracks, the crack depth is evaluated with the optimized algorithm. To verify the proposed method, experimental datasets containing twelve different depths were used to size the surface-breaking cracks with incomplete prior knowledge. Evaluation results showed the high accuracy of the proposed evaluations, demonstrating the feasibility of this intelligent method for various applications in industry.

The UN (United Nations) identifies sustainable development of the marine ecosystem as a major goal. To achieve the goal, the garbage in the ocean has to be removed, and as people recognize that different types of classification should be disposed differently, classification is critical after garbage is collected. This led to the engineering goal of constructing a robot that can pick up garbage in the water and classify the garbage into its right category. The robot was developed by attaining four critical functions: driving under water condition, a set of gripper, color recognition and garbage classification. Results of data obtained from developing the garbage classification was plotted on multiple graphs including training and validation loss, training time, training and testing accuracy and so on. After the critical functions are fulfilled, it is shown that the accumulative testing accuracy for garbage classification algorithm was around 90.6%, while the programs for the other three critical functions all compiled successfully. It was a regret that datasets weren’t shaped to the same sizes and the critical functions should be synthesized for further research.

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