Fast and accurate fault diagnosis of strongly coupled, time-varying, multivariable complex industrial processes remain a challenging problem. We propose an industrial fault diagnosis model. This model is established on the base of the temporal convolutional network (TCN) and the one-dimensional convolutional neural network (1DCNN). We add a batch normalization layer before the TCN layer, and the activation function of TCN is replaced from the initial ReLU function to the LeakyReLU function. To extract local correlations of features, a 1D convolution layer is added after the TCN layer, followed by the multi-head self-attention mechanism before the fully connected layer to enhance the model's diagnostic ability. The extended Tennessee Eastman Process (TEP) dataset is used as the index to evaluate the performance of our model. The experiment results show the high fault recognition accuracy and better generalization performance of our model, which proves its effectiveness. Additionally, the model's application on the diesel engine failure dataset of our partner's project validates the effectiveness of it in industrial scenarios.

No abstract

No abstract

No abstract

Local information coding helps capture the fine-grained features of the point cloud. The point cloud coding mechanism should be applicable to the point cloud data in different formats. However, the local features of the point cloud are directly affected by the attributes, size and scale of the object. This paper proposes an Adaptive Locally-Coded point cloud classification and segmentation Network coupled with Genetic Algorithm(ALCN-GA), which can automatically adjust the size of search cube to complete network training. ALCN-GA can adapt to the features of 3D data at different points, whose adjustment mechanism is realized by designing a robust crossover and mutation strategy. The proposed method is tested on the ModelNet40 dataset and S3DIS dataset. Respectively, the overall accuracy and average accuracy is 89.5% and 86.5% in classification, and overall accuracy and mIoU of segmentation is 80.34% and 51.05%. Compared with PointNet, average accuracy in classification and mIoU of segmentation is improved about 10% and 11% severally.