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To realize real-time detection of the operating environment of bridge cranes, this paper presents a three-dimensional mapping method using a binocular camera and laser ranging sensor. First, the left and right images are obtained by using the binocular camera, and the block matching is used for stereo correspondence to obtain the disparity image. Then, according to the reprojection matrix and the disparity image, the depth value of each pixel is obtained, and the depth image can be transformed into a three-dimensional point cloud of the current frame. Relying on the camera position data obtained by the laser ranging sensor, the three-dimensional point cloud generated from different positions is registered to obtain the three-dimensional point cloud of the craneʼs global operating environment. Finally, an octree map is used to represent the operating environment. To simulate the construction process of the operating environment of the bridge crane, an experimental platform was built, and experiments were carried out using the method proposed in this paper. The experimental results verified the feasibility of the method proposed in this paper.

Accurate degradation state recognition of rolling bearing is critical to effective condition based on maintenance for improving reliability and safety. In this work, a new architecture is proposed to recognize the degradation state of the rolling bearing. Firstly, the time-domain features including RMS, kurtosis, skewness and RMSEE, and Mel-frequency cepstral coefficients features are extracted from bearing vibration signals, which are then used as the input of k-means algorithm. These unlabeled features are clustered by k-means in order to define the different categories of the bearing degradation state. In this way, the original vibration signals can be labeled. Then, the convolutional neural network recognition model is built, which takes the bearing vibration signals as input, and outputs the degradation state category. So, interference brought by human factors can be eliminated, and further, the bearing degradation can be grasped so as to make maintenance plan in time. The proposed method was tested by bearing run-to-failure dataset provided by the Center for Intelligent Maintenance System, and the result proved the feasibility and reliability of the methodology.