Aiming at the problems of high manual cost, low efficiency, and low precision of the mechanical axis health management in industrial robot applications, this paper proposes a health assessment and state prediction algorithm based on hidden Markov model (HMM) and temporal convolutional networks (TCN). First, the MPdist similarity comparison algorithm is used to construct the mechanical axis health index. Then the hidden Markov model is trained with observable sensor data. After that, the temporal convolution neural network is used to predict state transition time iteratively, and the predicted results are decoded by HMM. The experimental results show that the HMM‐TCN model can accurately assess the health state of the mechanical axis and predict the state transition in real‐time. The prediction accuracy of this method reaches 87.5%, and the error interval locates in [−3,9] time steps. The accuracy, early/late prediction indicators are better than HMM‐RNN, HMM‐LSTM, and HMM‐GRU.
Wind power generation is considered as one of the very promising new energy power generation methods. Offshore wind farms are usually located in open spaces far from the coast, where the wind is strong enough to generate electricity efficiently and reliably. The operation and maintenance of offshore wind power generation is more important, it will greatly affect the life cycle cost, although it is more difficult. Different from the methods used in other papers, this paper uses the Internet of Things (IOT) technology to collect and analyze wind power generation data to accurately and efficiently realize the operation and maintenance of offshore wind power generation. This paper also establishes an economic model for further analysis. Estimated electricity production under real weather is integrated into the model. According to the estimated model, with IOT technology can reduce maintenance costs by about 75% compared to without IOT technology, and according to our operation and maintenance data, we found that the downtime caused by blades, gearboxes, and generators accounted for more than 87% of the total unplanned downtime, and maintenance costs accounted for more than 3/4 of the total maintenance costs. These data have reference significance for the operation and maintenance of offshore wind power generation.
Recent researches on image super-resolution (SR) have achieved great progressing with the great development of convolutional neural networks (CNNs). However, existing CNNs usually adopt fixed filter structures and the convolutions just rely on the local information contained in the fixed receptive field. Above phenomena prevent high-level convolution layers from encoding semantics over spatial locations and largely limits the learning capacity of CNNs. What's more, many methods simply used a single-size feature map and failed to consider the spatial information, thereby these results also are unsatisfactory. To address these problems, in this paper, a network with multi-scale space features and deformable convolutional (MulSSD) is presented to further improve the reconstruction accuracy. Specifically, a multi-scale space features compressed block containing the deformable convolutional layer is proposed, which can augment the spatial sampling locations and incorporate the multi-scale space compression features and adaptively adjust the sampling grid and receptive fields. In addition, the design of symmetrical combinations make the information can be smoothly propagated through multiple channels during the training, which effectively improves the training efficiency. Extensive experiments on benchmark datasets validate that the proposed method achieves outperforming quantitative and qualitative performance. And the experimental results also proved that our proposed MulSSD can reconstruct high-quality high-resolution (HR) images at a relatively fast speed and outperform other methods by a large margin.
Human recognition models based on spatial-temporal graph convolutional neural networks have been gradually developed, and we present an improved spatial-temporal graph convolutional neural network to solve the problems of the high number of parameters and low accuracy of this type of model. The method mainly draws on the inception structure. First, the tensor rotation is added to the graph convolution layer to realize the conversion between graph node dimension and channel dimension and enhance the model’s ability to capture global information for small-scale tasks. Then the inception temporal convolution layer is added to build a multiscale temporal convolution filter to perceive temporal information under different time domains hierarchically from 4-time dimensions. It overcomes the shortcomings of temporal graph convolutional networks in the field of joint relevance of hidden layers and compensates for the information omission of small-scale graph tasks. It also limits the volume of parameters, decreases the arithmetic power, and speeds up the computation. In our experiments, we verify our model on the public dataset NTU RGB + D. Our method reduces the number of the model parameters by 50% and achieves an accuracy of 90% in the CS evaluation system and 94% in the CV evaluation system. The results show that our method not only has high recognition accuracy and good robustness in human behavior recognition applications but also has a small number of model parameters, which can effectively reduce the computational cost.
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