The sky region in an image provides horizontal and background information for autonomous ground robots and is important for vision-based autonomous ground robot navigation. This paper proposes a sky region detection algorithm within a single image based on gradient information and energy function optimization. Unlike most existing methods, the proposed algorithm is applicable to both colour and greyscale images. Firstly, the gradient information of the image is obtained. Then, the optimal segmentation threshold in the gradient domain is calculated according to the energy function optimization and the preliminary sky region is estimated. Finally, a post-processing method is applied in order to refine the preliminary sky region detection result when no sky region appears in the image or when objects extrude from the ground. Experimental results have proven that the detection accuracy is greater than 95% in our test set with 1,000 images, while the processing time is about 150ms for an image with a resolution of 640×480 on a modern laptop using only a single core.

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Feature recognition and fault diagnosis plays an important role in equipment safety and stable operation of rotating machinery. In order to cope with the complexity problem of the vibration signal of rotating machinery, a feature fusion model based on information entropy and probabilistic neural network is proposed in this paper. The new method first uses information entropy theory to extract three kinds of characteristics entropy in vibration signals, namely, singular spectrum entropy, power spectrum entropy, and approximate entropy. Then the feature fusion model is constructed to classify and diagnose the fault signals. The proposed approach can combine comprehensive information from different aspects and is more sensitive to the fault features. The experimental results on simulated fault signals verified better performances of our proposed approach. In real two-span rotor data, the fault detection accuracy of the new method is more than 10% higher compared with the methods using three kinds of information entropy separately. The new approach is proved to be an effective fault recognition method for rotating machinery.

Mechanical intelligent fault diagnosis is an important method to accurately identify the health status of mechanical equipment. Traditional fault diagnosis methods perform poorly in the diagnosis of rolling bearings under complex conditions. In this paper, a feature transfer learning model based on improved DenseNet and joint distribution adaptation (FT-IDJ) is proposed. With this model, we apply it to implement rolling bearing fault diagnosis. A lightweight DenseNet model is firstly proposed to extract the transferable features of the raw vibration signal. Furthermore, the parameters in the DenseNet are constrained by the domain adaptive regularization term and pseudo label learning. The marginal distribution discrepancy and the conditional distribution discrepancy of the learned transferable features are reduced by this way. The proposed method is validated by the diagnosis experiments with CWRU and Jiangnan University rolling bearing datasets. The experimental results showed that the proposed FT-IDJ has higher classification accuracy than DAN and other eight methods, which demonstrated its effectively learning transferable features from auxiliary data.