Fault Diagnosis Technology for Complex Mechanical Systems Based on Deep Learning - Guo, Zijian (Transactions on Computer Science and Intelligent Systems Research, 2024)
Traditional fault diagnosis methods are usually based on expert knowledge and experience, but this method often relies on manual analysis and judgment, which is time-consuming and inaccurate. Deep learning has become a hot spot because of the development of optimization algorithms and computer hardware, and has achieved great success in many applications. In the field, neural network, as a popular tool, can establish a feature recognition system between perceptron and classifier through training. This system includes the representation process of input layer, the promotion process of hidden layer and output layer. While ensuring the safe, stable and reliable operation of equipment and personal safety, improving the production efficiency of enterprises and enhancing the international competitiveness and influence of the industry are the new development trends of the design, production and maintenance guarantee system of manufacturing industry worldwide. At the same time, comprehensive testing and evaluation records of the operating status of the transmission equipment of the rolling mill can provide reliable reference for the diagnosis of faults and major and medium repairs of this type of rolling mill in the future. In practical engineering, due to different working conditions and complex environments, the types of faults that occur are unpredictable, which may result in a lack of samples available for training fault diagnosis models. This article mainly discusses and analyzes in depth the fault diagnosis technology of complex mechanical systems based on deep learning algorithms.