Feeding Material Identification for a Crusher Based on Deep Learning for Status Monitoring and Fault Diagnosis

Avionics Module Fault Diagnosis Algorithm Based on Hybrid Attention Adaptive Multi-Scale Temporal Convolution Network

Since the reliability of the avionics module is crucial for aircraft safety, the fault diagnosis and health management of this module are particularly significant. While deep learning-based prognostics and health management (PHM) methods exhibit highly accurate fault diagnosis, they have disadvantages such as inefficient data feature extraction and insufficient generalization capability, as well as a lack of avionics module fault data. Consequently, this study first employs fault injection to simulate various fault types of the avionics module and performs data enhancement to construct the P2020 communications processor fault dataset. Subsequently, a multichannel fault diagnosis method, the Hybrid Attention Adaptive Multi-scale Temporal Convolution Network (HAAMTCN) for the integrated functional circuit module of the avionics module, is proposed, which adaptively constructs the optimal size of the convolutional kernel to efficiently extract features of avionics module fault signals with large information entropy. Further, the combined use of the Interaction Channel Attention (ICA) module and the Hierarchical Block Temporal Attention (HBTA) module results in the HAAMTCN to pay more attention to the critical information in the channel dimension and time step dimension. The experimental results show that the HAAMTCN achieves an accuracy of 99.64% in the avionics module fault classification task which proves our method achieves better performance in comparison with existing methods.

Fault monitoring method of domestic waste incineration slag sorting device based on back propagation neural network

The main monitoring points of traditional sorting equipment fault monitoring methods are usually limited to the inlet and outlet, making it difficult to monitor the internal equipment, which may affect the accuracy of fault monitoring. Therefore, a new fault monitoring method based on back propagation neural network has been studied and designed, which is mainly applied to the sorting device of domestic waste incineration slag. The fault monitoring modeling variables of the domestic waste incineration slag sorting device are selected to determine the operation status of the sorting device. Based on back propagation neural network, a fault monitoring model for the sorting device of municipal solid waste incinerator slag is constructed, and the fault data of the sorting device is trained in the model, so that the fault data of the sorting device can be optimized faster, thus improving the accuracy of fault monitoring. Through comparative experiments with traditional methods, it has been confirmed that this fault monitoring method based on back propagation neural network has significant advantages in detection performance, demonstrating its potential in practical applications.

Seismic Response Models and the AVO Simulation of Coal-Bearing Reservoirs

The presence of coal in complex structures featuring sandstone reservoirs seriously hinders reservoir characterization and the identification of fluids in subsurface formations. Coal can strongly obscure the reflections from sandstone, easily leading to false bright spots during exploration; thus, reservoirs and their boundaries cannot be accurately described. Furthermore, sandstone layers intercalated with thin coal seams form complex composite reflections. Therefore, considering the complexity of coal-bearing reservoirs together with the geological evolution of coal and actual logging data, this study systematically analyses the seismic reflections of coal-bearing reservoirs. First, the seismic responses of various coal-bearing reservoir models are established by evaluating multiple well logs of the target layer. Then, by forward-simulating theoretical seismic data, seismic response models comprising different lithological combinations are established. Finally, seismic attributes (such as the amplitude, frequency and phase) of coal-bearing and non-coal-bearing strata are compared, and the seismic responses of typical lithological combinations of coal-bearing reservoirs are summarized. A single-well model test and a comparison between synthetic and seismic data confirm that numerical simulations can be used to forward model the seismic response characteristics of different sand–coal models, thereby eliminating the influences of coal and accurately characterizing sandstone reservoirs.