Modeling forest fire spread is a very complex problem, and the existing models usually need some input parameters which are hard to get. How to predict the time series of forest fire spread rate based on passed series may be a key problem to break through the current technical bottleneck. In the process of forest fire spreading, spread rate and wind speed would affect each other. In this paper, three kinds of network models based on Long Short-Term Memory (LSTM) are designed to predict fire spread rate, exploring the interaction between fire and wind. In order to train these LSTM-based models and validate their effectiveness of prediction, several outdoor combustion experiments are designed and carried out. Process data sets of forest fire spreading are collected with an infrared camera mounted on a UAV, and wind data sets are recorded using a anemometer simultaneously. According to the close relationship between wind and fire, three progressive LSTM based models are constructed, which are called CSG-LSTM, MDG-LSTM and FNU-LSTM, respectively. A Cross-Entropy Loss equation is employed to measure the model training quality, and then prediction accuracy is computed and analyzed by comparing with the true fire spread rate and wind speed. According to the performance of training and prediction stage, FNU-LSTM is determined as the best model for the general case. The advantage of FNU-LSTM is further demonstrated by doing comparison experiments with the normal LSTM and other LSTM based models which predict both fire spread rate and wind speed separately. The experiment has also demonstrated the ability of the model to the real fire prediction on the basis of two historical wildland fires.

In this paper, a fault diagnosis method based on data augmentation technology is proposed to investigate the unequal length series by combining Dynamic Time Warping (DTW) with Deep Residual Network (Resnet) for excavator, where the hydraulic and power units are operated in a hybrid manner. A data augmentation method is designed to segment time series with unequal length by enhancing appropriate statistical features based on the working characteristics of excavators. Based on instantaneous calculation, an optimized DTW algorithm is proposed to reduce the calculation cost and ensure the effectiveness of the searched optimal warping path. In order to achieve fault diagnosis in industrial applications, a DTW-Resnet model combining optimized DTW algorithm with Resnet model is proposed, which overcomes the disadvantage of neural networks being unable to learn time series with unequal length easily. Results with respect to SANY excavator datasets and four publicly available datasets have well indicated that the proposed method is provided with preferable diagnostic performance compared with state-of-the-art neural networks and traditional models. Furthermore, the ablative experiment shows that the data augmentation and optimized DTW are of great significance to improve the classification accuracy of the model.

Equipment operating in intermittent industrial processes generates complex system coupling and unequal-length sensor data, which are considered significant challenges for fault diagnosis in industrial production. To address these challenges, a generic fault diagnosis approach, the dynamic time warping Gaussian spatiotemporal self-attention network (DGSSANet), is proposed. dynamic time warping combined with Gaussian blur is employed to handle sensor signal complexity, transforming unequal-length data into warping matrices of equal dimensions. The inclusion of spatiotemporal self-attention, with the multiscale temporal attention module and multielement spatial self-attention module, enhances the ability of DGSSANet to capture spatial and temporal relationships. DGSSANet is validated through benchmark experiments on bearing datasets and applied to excavator cases, with its interpretability analyzed via visualization. The results demonstrate that DGSSANet addresses the challenges of fault diagnosis in intermittent production processes effectively.

This paper is focused on designing a cost function of selecting a foothold for a physical quadruped robot walking on rough terrain. The quadruped robot is modeled with Denavit–Hartenberg (DH) parameters, and then a default foothold is defined based on the model. Time of Flight (TOF) camera is used to perceive terrain information and construct a 2.5D elevation map, on which the terrain features are detected. The cost function is defined as the weighted sum of several elements including terrain features and some features on the relative pose between the default foothold and other candidates. It is nearly impossible to hand-code the weight vector of the function, so the weights are learned using Supporting Vector Machine (SVM) techniques, and the training data set is generated from the 2.5D elevation map of a real terrain under the guidance of experts. Four candidate footholds around the default foothold are randomly sampled, and the expert gives the order of such four candidates by rotating and scaling the view for seeing clearly. Lastly, the learned cost function is used to select a suitable foothold and drive the quadruped robot to walk autonomously across the rough terrain with wooden steps. Comparing to the approach with the original standard static gait, the proposed cost function shows better performance.