This paper investigates the warp let-off and take-up mechanism of rapier looms in order to solve the problem that the warp tension of rapier looms fluctuates greatly and the warp let-off is difficult to maintain constant. The design and hardware implementation of a let-off and take-up control system based on fuzzy neural network (FNN) and vector control (VC) are presented to improve the control level of warp tension and drive performance of the let-off and take-up system. Firstly, the spring-damper dynamic model of the warp is established according to the mechanical properties of the warp. The parametric expression of warp tension and the control strategy of fixed angle interval based on let-off and take-up motions are constructed according to the generation mechanism and fluctuation law of warp tension. Then, on the basis of fuzzy reasoning mechanism and neural network model, the fusion theory of fuzzy neural network is introduced, and a tension controller based on T-S fuzzy neural network (FNN) is designed. FNN is trained by introducing genetic optimization and the backpropagation fusion algorithm (GA-BP). In addition, a specialized let-off and take-up hardware circuit is constructed through embedded technology, and the SVPWM algorithm is used as the driving scheme of the hardware circuit. Finally, simulation and actual weaving experiments test the proposed let-off and take-up control system and hardware circuit. The results show that, when compared to PID and fuzzy PID, the proposed fuzzy neural network algorithm has higher tension control accuracy and can effectively restrain the rapier loom's warp tension undulation. The designed hardware circuit and SVPWM algorithm have the fast and stable driving ability, which ensures the constant let-off amount.INDEX TERMS Rapier loom, tension control, vector control, fuzzy neural network, genetic algorithm.

In order to improve the effect of intelligent monitoring and condition analysis of textile machinery, some solutions have been proposed to mitigate incomplete monitoring positions, insufficient decision accuracy, uncertainty reasoning and generalization of the current loom monitoring system. Firstly, a model of the weaving machine spindle dynamics was constructed, and the types and sources of monitoring data were specified. Secondly, an improved rough set method is proposed for processing the collected loom attribute data. A genetic multi-objective optimization method combined with a genetic algorithm is proposed to improve the problem of too many reduction results of the rough set method and improve the monitoring system's reliability. In order to solve the problem that new objects do not have unique matching rules in the constructed rule base, a fusion of Dezert-Smarandache Theory (DSmT) for uncertainty inference is proposed, which increases the distinguishability of decision support probabilities. Experiments show that the improved rough set method based on DSmT and genetic multi-objective optimization has higher classification accuracy and better recognition than the traditional rough set method for weaving machine condition monitoring.

At present, the fault diagnosis methods of lithium battery pole rolling mill mostly rely on manual experience and the self-test function of mature control devices such as frequency converters and lack the ability of intelligent fault diagnosis for the whole equipment and the ability to evaluate the health state of the equipment during operation. To improve the intellectual health diagnosis ability of lithium battery pole double rolling mill equipment, starting from the structure and technology of lithium battery pole double rolling equipment, this paper analyzes its common fault types. It summarizes the shortcomings and common fault types of existing equipment. Then, we introduce fuzzy reasoning into the fault diagnosis method based on Expert Systems and establish the FEFDM of lithium battery pole double rolling equipment. Finally, we introduce the concept of health degree, effectively connect BP neural network and health degree through the fuzzy set, and establish an equipment operation health state evaluation method based on an improved BP Neural Network, which realizes the evaluation ability of the health state of double roller equipment. In addition, we use Extended Kalman Filtering (EKF) to clean the "dirty data" and filter out the Gaussian white noise from the signal. The health diagnosis method proposed in this paper can meet the ability to accurately locate and diagnose the fault of lithium battery pole double roller equipment and evaluate the health state of equipment operation and maintain the equipment in advance.