Expert Experience and Data-Driven Based Hybrid Fault Diagnosis for High-SpeedWire Rod Finishing Mills

The reliable operation of high-speed wire rod finishing mills is crucial in the steel production enterprise.As complex system-level equipment,it is difficult for high-speed wire rod finishing mills to realize fault location and real-time monitoring.To solve the above problems,an expert experience and data-driven-based hybrid fault diagnosis method for high-speed wire rod finishing mills is proposed in this paper.First,based on its mechanical structure,time and frequency domain analysis are improved in fault feature extraction.The approach of combining virtual value,peak value with kurtosis value index,is adopted in time domain analysis.Speed adjustment and side frequency analysis are proposed in frequency domain analysis to obtain accurate component characteristic frequency and its corresponding sideband.Then,according to time and frequency domain characteristics,fault location based on expert experience is proposed to get an accurate fault result.Finally,the proposed method is implemented in the equipment intelligent diagnosis system.By taking an equipment fault on site,for example,the effectiveness of the proposed method is illustrated in the system.

Single Neuron PID Control Based on Expert Experiences for Temperature Difference Control System of a Digester

Considering the temperature difference of displacement cooking characterized by severe non-linearity, large time delay, and real-time control, a cascade PID adaptive control strategy composed of a single neuron is proposed to ensure cooking temperature uniformity. The control strategy introduces expert experiences to adjust the single neuron gain K, while a single neuron PID self-learning and adaptive ability, as well as cascade advantage can be combined to realize the real-time and fast temperature difference control. In the Simulink, the s-function of this control strategy is used to carry out a dynamic simulation experiment with temperature difference characteristics and verify the robustness and response to model mismatch. Compared to conventional temperature difference-flow PID cascade control and single neuron PID cascade control, this control strategy has better robustness and stronger adaptability. The results of real-time control on the THJSK-1 experiment platform indicate this control strategy is feasible.

UAV maneuvering decision-making algorithm based on deep reinforcement learning under the guidance of expert experience

Autonomous umanned aerial vehicle(UAV) manipulation is necessary for the defense department to execute tactical missions given by commanders in the future unmanned battlefield. A large amount of research has been devoted to improving the autonomous decision-making ability of UAV in an interactive environment, where finding the optimal maneuvering decisionmaking policy became one of the key issues for enabling the intelligence of UAV. In this paper, we propose a maneuvering decision-making algorithm for autonomous air-delivery based on deep reinforcement learning under the guidance of expert experience. Specifically, we refine the guidance towards area and guidance towards specific point tasks for the air-delivery process based on the traditional air-to-surface fire control methods.Moreover, we construct the UAV maneuvering decision-making model based on Markov decision processes(MDPs). Specifically, we present a reward shaping method for the guidance towards area and guidance towards specific point tasks using potential-based function and expert-guided advice. The proposed algorithm could accelerate the convergence of the maneuvering decision-making policy and increase the stability of the policy in terms of the output during the later stage of training process. The effectiveness of the proposed maneuvering decision-making policy is illustrated by the curves of training parameters and extensive experimental results for testing the trained policy.

Feasibility Analysis of the Value of Q Method in the Classification and Understanding of Expert Experience

The Q method, combining qualitative and quantitative methods, refers to the qualitative analysis of Q-sorts based on quantitative techniques. It is used to research individual subjective experience, analyzing consensus and divergence to identify and categorize subjects＇ viewpoints. The sorting process is completely performed by the subjects, independent of study researchers. The Q method in medical research has been applied in many fields, including nursing care, clinical studies, doctor and patient＇s perceptions, health evaluation and decision making. The authors used the Q method to research Chinese medicine （CM） group decision making, exploring its practical feasibility in this important field. Four primary domains are addressed： （1） integration of expert opinion; （2） expert classification; （3） ascertaining the entire viewpoint orientation of a certain type of expert; and （4） comparison of expert opinion using an additional perspective. The essence of the Q method caters to the CM thinking model and should be introduced into CM and explored more deeply.

Classification of Cancer Cells Using Neural Networks in Combination with Expert Experience

This paper briefly reviews some neural networks and discusses their drawbacks, the main defaults is that these neural networks do not use expert experience (or knowledge) and have the human flexibility, therefore, a new better method for the combination of neural networks with expert experience (or knowledge) was proposed. Probabilistic neural networks (PNNs) classification of cancer cell image is described. This networks is simpler and faster than back propagating neural networks (BPNNs) during training and learning. Neural networks combined with expert experience is presented in order to improve the classification accuracy of the networks and the simulation experiments were performed and the results have shown that the method presented is very efficient and feasible.