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.
The safety and stability of the rapier loom during operation directly impact the quality of the fabric. Therefore, it is of great significance to carry out fault diagnosis research on rapier looms. In order to solve the problems of low diagnosis efficiency, untimely diagnosis, and high maintenance cost of existing rapier looms in manual troubleshooting of loom failures. This paper proposes a new intelligent fault diagnosis method for rapier looms based on the fusion of expert system and fault tree. A new expert system knowledge base is formed by combining the dynamic fault tree model with the expert system knowledge base. It solves the problem that the traditional expert system cannot achieve precise positioning in the face of complex fault types. Construct the rapier loom’s fault diagnosis model, build the intelligent diagnosis platform, and finally realize the intelligent fault diagnosis of the rapier loom. Experimental results show that the algorithm can quickly diagnose and locate rapier loom faults. Compared with the current intelligent diagnosis algorithm, the algorithm structure is simplified, which provides a theoretical basis for the broad application of intelligent fault diagnosis on rapier looms.
Rapier looms are currently important equipment in the weaving process. The control system of the loom determines the performance of the loom, to a large extent. To effectively reduce the production cost and energy consumption and to improve the start-up performance and production efficiency of rapier looms in industrial production, this paper develops an integrated rapier loom control system based on the direct drive of a switched reluctance motor (SRM) spindle and conducts field tests and applications. The contribution and innovation of this paper is to develop a complete set of low-cost control systems, propose an SRM single neuron fuzzy PID speed control strategy based on voltage chopping control and use it for the control of the main shaft drive technology of rapier looms. The integrated rapier loom control system based on the SRM spindle direct drive proposed in this paper reduces the production costs and energy consumption and improves the start-up performance and production efficiency of the rapier loom. This text carries on the systematic plan design to the control system from the hardware system and the software system. First, according to actual needs, starting from the aspect of reducing control costs and combined with the characteristics of embedded systems, such as tailorability, low cost, and strong scalability, this paper proposes a control system hardware structure based on CAN bus communication and a fully embedded STM32. The control system is divided into multiple control modules, such as the main control module, the spindle drive module, and the power transmission coil module. The system conducts a distributed control to the loom through the CAN bus and is equipped with various communication interfaces, such as Ethernet and RS485. Second, combining the characteristics of the SRM with a simple structure, a large starting torque and the operation mode of the loom, the basic control mode of SRM voltage chopping control is determined. To improve the efficiency and start-up performance of the speed control system, the SRM single neuron PID control algorithm is proposed, and a single neuron is used to improve the PID parameters. On this basis, fuzzy control is introduced to adjust the output gain of a single neuron PID control online to improve the system performance and reduce system energy consumption. Finally, the entire set of rapier loom control systems was verified, tested and debugged on site. The results show that each functional circuit works normally, and that the designed control system can meet the speed response demand of the loom at 850 rpm and reduce the production cost and energy consumption. The comparison experiment between the single neuron fuzzy PID algorithm of the motor and the traditional PID control algorithm in the actual loom production process proves that the proposed control algorithm has a better dynamic response performance. The proposed control algorithm effectively improves the starting performance and production efficiency of rapier looms and meets the actual needs of...
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.
This work was partly supported by the fifth phase of the "333 Project" training fund project in Jiangsu Province, China: the research on the decentralized selforganizing network of looms and the remote fault diagnosis expert system, under Grants BRA2020244.
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