Subjects: Information Science and Systems Science >> Control science and technology submitted time 2024-01-13
Abstract： As a new field with rapid development in the past ten years, deep learning has attracted more and more researchers' attention. It has obvious advantages compared with shallow model in feature extraction and modeling. Deep learning is good at mining increasingly abstract feature representations from raw input data, and these representations have good generalization ability. It overcomes some of the problems in AI that were considered difficult to solve in the past. With the significant increase in the number of training data sets and the surge in chip processing power, it has achieved remarkable results in the fields of target detection and computer vision, natural language processing, speech recognition and semantic analysis, so it also promotes the development of artificial intelligence. Deep learning is a hierarchical machine learning method that includes multilevel nonlinear transformations. Firstly, this paper discusses the basic knowledge of deep learning, analyzes the superiority of the algorithm, and introduces the mainstream learning algorithm and its application status. Finally, the existing problems and development direction are summarized.
Peer Review Status:
Awaiting Review
Subjects: Information Science and Systems Science >> Control science and technology submitted time 2024-01-07
Abstract： Traditional motor fault diagnosis technology is usually based on a single type of state parameters, such as vibration parameters or electrical parameters. However, the monitoring range of a single type of motor state parameters is very limited in many cases, which is difficult to meet the needs of comprehensive fault diagnosis of motors. The purpose of this paper is to propose a comprehensive motor fault diagnosis method by fusing vibration data and current data, so as to improve the reliability and accuracy of diagnosis. On the basis of data fusion, it is considered that in the actual industrial and production environment, the cost of obtaining large-scale labeled samples is often high or even not feasible. Therefore, the neural network is further studied and improved, and a small sample fault diagnosis network based on RNN and attention mechanism is proposed.
In this paper, the motor fault feature extraction method is used to study the vibration and current signal characteristics of the motor under different faults. The fault feature extraction methods adopted include Fast Fourier Transform (FFT) and Hilbert-Huang Transform.
According to the actual data fusion requirements in this paper, the overall implementation scheme of data fusion is designed. The fault features are extracted by using FFT, Hilbert-Huang Transform (HHT) and Convolutional Neural Network (CNN) and Multilayer Perceptron (MLP) in turn, and the vibration and current parameters of the motor are fused to carry out comprehensive fault identification and fault diagnosis. The results show that the motor fault diagnosis technology using data fusion method can improve the accuracy of diagnosis results and reduce the uncertainty caused by a single parameter, thus improving the accuracy of motor fault diagnosis. The designed small sample fault diagnosis network is used to identify the health status of equipment under small samples, in which the attention mechanism captures the spatial and channel relationship of signals, and a single experimental sample is used to verify that the network used in this paper has the advantages of diagnostic efficiency and accuracy under different small sample working conditions.
Peer Review Status:Awaiting Review
Subjects: Information Science and Systems Science >> Control science and technology submitted time 2024-01-06
Abstract： One of the core topics of artificial intelligence is neural networks and deep learning, which imitate the working principle of the human brain and use multi-level neural connections to mine valuable knowledge and rules from data. The research of neural networks started in the 1940s and went through several ups and downs and innovations. It now covers many types and fields, such as convolutional neural networks, recurrent neural networks, speech recognition, computer vision and natural language processing. Deep learning refers to using multi-layer neural networks to solve complex nonlinear problems. It relies on massive data and computing resources, as well as efficient training and optimization techniques. Deep learning has achieved amazing progress in recent years, but also faces some difficulties and challenges, such as model interpretability, generalization ability, security and reliability. Deep learning is still a vibrant and promising research field, which is expected to open up more opportunities and possibilities for human intelligence and life. This article will briefly introduce some types of neural network structures and some deep learning model structures.
Peer Review Status:Awaiting Review
Subjects: Information Science and Systems Science >> Control science and technology submitted time 2024-01-05
Abstract： In the realm of soft sensing, missing data frequently occurs during the journey from data collection to application, significantly diminishing model accuracy. This paper introduces a filling model based on the Variational Autoencoder (VAE) and GRU neural network. Validation through industrial processes confirms the accuracy of the imputed data. Experimental results demonstrate that the VAE imputation model yields an RMSE and MAE of 3.396% and 2.458% for missing rates of 10%, and 3.549% and 3.078% for missing rates of 30%, respectively. Compared to alternative imputation algorithms like PCA and SVD, the VAE model exhibits significantly enhanced performance, affirming the feasibility of this model.
Peer Review Status:Awaiting Review
Subjects: Information Science and Systems Science >> Control science and technology Subjects: Computer Science >> Computer Application Technology Subjects: Engineering and technical science >> Engineering Cybernetics submitted time 2022-12-07
Abstract： Abstract: Objective In practice, more than 95 % of industrial process control problems can be solved by PID control algorithm. In this paper, a new method of general controller optimization setting is established on the basis of inheriting and innovatively using 2DOF PID internal model control technology. Methods Model System was introduced into the method, and optimization template was obtained after offline simulation of the Model System. In the process of the signal excitation of the actual control system, the PID internal model control parameters are set by a specific algorithm guided by the template. Results Without accurate object parameters, the identification of object parameters and optimization of control performance can be completed simultaneously after several cycles of iteration.
Conclusions This method has high setting efficiency, is convenient for later online maintenance of the system, and reduces the requirements for the technology and experience of the implementation personnel. The controller inherits the characteristics of good control with large time delay and strong robustness of internal model control, and both "target tracking" and "interference suppression" performance optimization. The control algorithm is simple, intuitive, and easy to be upgraded in the original PID control or realized by embedded system software and hardware, which is easy to be popularized and applied in production.
Limits and future work Restricted by the design requirements of traditional internal model control, for the controlled object with negative response characteristics, this method should adopt the pre compensation processing mechanism or find a new model system and study the improved algorithm.
Peer Review Status:Awaiting Review
Subjects: Information Science and Systems Science >> Control science and technology Subjects: Computer Science >> Computer Application Technology submitted time 2022-04-20
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Peer Review Status:Awaiting Review
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